Workshop 6: Information Processing in the Visual System

(April 23,2007 - April 27,2007 )

Organizers


Alessandra Angelucci
Ophthalmology and Visual Science, University of Utah
Paul Bressloff
Department of Mathematics, University of Utah

The traditional feedforward model of the visual system invokes a sequence of processing stages, beginning with the relay of retinal input to neurons in the primary visual cortex (V1), via the lateral geniculate nucleus (LGN), and subsequent higher-order processing through a hierarchy of cortical areas. According to this model, neurons at each successive stage process inputs from increasingly larger regions of space, and code for increasingly more complex aspects of visual stimuli. The selectivity of a neuron to a given stimulus parameter (e.g., orientation, color, depth) is assumed to result from the ordered convergence of afferents from the lower stages.

In this workshop, three different aspects of visual information processing will be considered.

  • Thalamus: There is growing evidence that thalamocortical and corticothalamic interactions play an important role in controlling the flow of visual information, both at the initial entry stage where it can be modulated by attentional states, and at higher-order stages involving sensory and motor processing.
  • Early visual processing: There is considerable physiological and psychophysical evidence that long-distance integration of visual signals can occur at very early stages of processing including V1. In particular, the response of a V1 cell to stimulation of its classical receptive field (RF) can be selectively modulated by contextual stimuli lying far outside its RF.
  • Top-down processing: An important source of top-down influences on bottom-up sensory processing arises from selective attention, in which the saliency of an object can be altered in light of behavioral relevance.

Accepted Speakers

Alessandra Angelucci
Ophthalmology and Visual Science, University of Utah
Dana Ballard
Computer Science, University of Texas
Andreas Burkhalter
Anatomy and Neurobiology, Washington University School of Medicine
David Fitzpatrick
Neurobiology, Duke University Medical Center
Yves Fregnac
Institute of Neurobiology, U.N.I.C.
Charles Gray
Center for Computational Biology, Montana State University
Tai Sing Lee
Computer Science, Carnegie Mellon University
Risto Miikkulainen
Dept. of Computer Sciences & Inst. for Neuroscience, University of Texas
David B. Omer
The Neurobiology Dept., The Weizmann Institute of Science
Ning Qian
Ctr. Neurobiology & Behavior, Columbia University
Robert Shapley
Center for Neural Science, New York University
Murray Sherman
Neurobiology, University of Chicago
Gregory Smith
Applied Science, College of William and Mary
Mriganka Sur
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
Martin Usrey
Center for Neuroscience, University of California, Davis
Steven Zucker
Computer Science, Yale University
Monday, April 23, 2007
Time Session
09:15 AM
07:00 PM
Murray Sherman - The Role of Thalamus: Relay Functions and More

The LGN and pulvinar (a massive but generally mysterious and ignored thalamic relay) are


examples of two different types of relay: the LGN is a first order relay, transmitting information


from a subcortical source (retina), while the pulvinar is mostly a higher order relay, transmitting


information from layer 5 of one cortical area to another area. First and higher order thalamic


relays can also be recognized for the somatosensory and auditory thalamic systems, and this


division of thalamic relays can also be extended beyond sensory systems. Thus the first and


higher order thalamic equivalents of the somatosensory and auditory systems are, respectively,


the ventral posterior nucleus and the posterior medial nucleus (somatosensory), and the ventral


versus dorsal portion of  the medial geniculate nucleus (auditory). Other thalamic nuclei have


also been placed into this framework, and so the medial dorsal nucleus is mostly higher order,


while the ventral anterior and ventral lateral nuclei seem to be a mosaic of first and higher order


relays. It now seems clear that most of thalamus is comprised of higher order relays. Higher


order relays seem especially important to general corticocortical communication, and this


challenges and extends the conventional view that such communication is based on direct


corticocortical connections. Thus the thalamus is not just a simple relay responsible for getting


peripheral information to cortex: instead it both provides a behaviorally relevant, dynamic


control over the nature of information relayed, and it also plays a key role in basic corticocortical


communication

10:30 AM
11:15 AM
Martin Usrey - Feedforward and feedback contributions to visual processing in the lateral geniculate nucleus

At the heart of the retinogeniculocortical pathway are neurons in the lateral geniculate nucleus (LGN) of the thalamus. LGN neurons receive feedforward input from retinal ganglion cells and feedback input from the primary visual cortex. Because LGN neurons are strongly driven by the retina and have receptive fields much like those of their retinal afferents, the LGN is generally regarded as a structure that simply relays retinal activity without doing much in terms of visual processing. Closer examination, however, reveals a more complex picture of LGN function, as LGN neurons can dynamically filter their retinal input. Results will be presented from experiments in anesthetized and alert animals that examined the role of feedforward and feedback pathways in the spike transfer, surround suppression and attentional modulation of LGN neurons.

11:45 AM
12:30 PM
Gregory Smith - Feedback inhibition and throughput properties of network models of retinogeniculate transmission

Computational modeling has played an important role in the dissection of the biophysical basis of rhythmic oscillations in thalamus that are associated with sleep and certain forms of epilepsy. In contrast, the dynamic filter properties of thalamic relay nuclei during states of arousal are not well understood. Here we present two modeling studies of the throughput properties of the visually driven dorsal lateral geniculate nucleus (dLGN) in the presence of feedback inhibition from the perigeniculate nucleus (PGN). In the first study we employ thalamocortical (TC) and thalamic reticular (RE) versions of a minimal integrate-and-fire-or-burst type model and a one-dimensional, two-layered network architecture. Potassium leakage conductances control the neuromodulatory state of the network and eliminate rhythmic bursting in the presence of spontaneous input (i.e., wake up the network). The aroused dLGN/PGN network model is subsequently stimulated by spatially homogeneous spontaneous retinal input or spatio-temporally patterned input consistent with the activity of X-type retinal ganglion cells during full-field or drifting grating visual stimulation. The throughput properties of this visually-driven dLGN/PGN network model are characterized and quantified as a function of stimulus parameters such as contrast, temporal frequency, and spatial frequency. In the second study we show how all-to-all coupled TC-RE networks can be described by a multivariate probability density function that satisfies a conservation equation with appropriately defined probability fluxes and boundary conditions. Consistent with the IFB model, the independent variables of these densities are the membrane potential of both cell types and the inactivation gating variable of the low-threshold Ca current IT. The synaptic coupling of the populations and external excitatory drive are modeled by instantaneous jumps in the membrane potential of postsynaptic neurons. When the aroused population density model is stimulated with constant retinal input (10-100 spikes/sec), a wide range of responses are observed depending on cellular parameters and network connectivity. These include asynchronous burst and tonic spikes, sleep spindle-like rhythmic bursting, and oscillations in population firing rate that are distinguishable from sleep spindles due to their amplitude, frequency, or the presence of tonic spikes. Supported by NSF grants IBN 0228273, MCB 0133132, and DMS/NIGMS 0443843.

03:15 PM
04:00 PM
David Fitzpatrick - Learning to see: The experience-dependent emergence of direction selectivity in visual cortex

Development of the selective response properties that define columns in sensory cortex is thought to begin early in cortical maturation, without the need for visual experience. My talk will focus on recent experiments that explore the development of direction selectivity in visual cortex of the ferret using intrinsic signal and in vivo 2-photon calcium signal imaging. Our results indicate that direction columns, unlike other columnar systems, emerge a few days after eye opening and this emergence depends on visual experience. The impact of visual experience on the emergence of direction columns can be visualized in individual visually naive animals that are exposed to a motion training stimulus. After 10-12 hours of stimulation, direction columns become evident and gradually strengthen. These effects of visual experience are strikingly limited to the cortical neurons that are activated by the training stimulus, and reflect a rapid increase in the direction selectivity of individual layer 2/3 neurons. Taken together, these results suggest that visual experience plays a critical role in the emergence of direction selective cortical responses.



 
Tuesday, April 24, 2007
Time Session
09:00 AM
09:45 AM
Robert Shapley - Visual feature selectivities and spatial interactions in V1 cortex

I will describe a local circuit computational model of a patch of the input layer 4Ca of the primary visual cortex (V1) of the macaque monkey. The model neurons are integrate-and-fire neurons with biologically plausible synaptic conductances. This model can account for the distributions of orientation and spatial frequency selectivity across the population of V1, and also the relative prevalence of linear and nonlinear spatial summation in V1 neurons. The crucial controlling variable appears to be the relative strength of cortico-cortical inhibition relative to excitation, the local circuit. Experiments on responses to spatially extended stimuli indicate that there is also a strong influence of long-distance, possibly feedback, interactions on V1 neurons.



 
10:00 AM
10:45 AM
Ralph Freeman - Dynamic spatial processing originates in early visual pathways

Several investigations in the visual system have established that coarse spatial features are processed before those of fine detail. Although it is generally assumed that this is a cortical function, there are features of early visual pathways that provide the basis for a coarse-to-fine sequence. We have investigated this possibility by neurophysiological studies in the lateral geniculate nucleus. Our findings show clear coarse-to-fine properties for nearly all LGN neurons in our sample. By use of a model, we estimate that the known temporal dynamic features in the visual cortex may be accounted for by a feed-forward process.

01:30 PM
02:15 PM
David B. Omer - The dynamics of evoked and ongoing activity in the behaving monkey

Previous findings from Voltage Sensitive Dye Imaging (VSDI) experiments done on anesthetized cats (Grinvald et al., 1989; Arieli et al., 1995; Arieli et al., 1996; Tsodyks et al., 1999; Kenet et al., 2003) indicated that the amplitude of ongoing activity (primarily synaptic potentials) is large, suggesting that it may play an important role in cortical processing by affecting the evoked activity and therefore the final behavior itself. VSDI was recently implemented also on the awake monkey (Slovin et al., 2002; Seidemann et al., 2002;) allowing monitoring of  activity from the same patch of cortex, repetitively,  for more than a year. We investigated the cortical activity in the primary visual cortex of a behaving monkey during both evoked and ongoing conditions. Several questions have been addressed: what are the spatial-temporal characterizations of the ongoing activity in early visual areas of the behaving monkey? How is it related to the functional architecture? We combined simultaneous VSDI with electrophysiological recordings of the local field potential (LFP) single and multi unit activities. In the evoked condition, the monkey was trained to fixate for 10s while presented with a full field moving grating. We found that our fast switching  stimuli abolished the high frequency oscillations at about 30Hz, oscillations that were present in the absence of a stimulus. During the ongoing condition, the monkey was required to sit quietly in a totally dark room. We found that the VSD signals in both conditions are often highly similar to the LFP, just like in the anesthetized cat. The similarity between the VSD signals and LFP was highest within the α (9-14 Hz) frequency band. For the awake monkey, the ratio between amplitude of ongoing and evoked activity was much smaller than what was found in the anesthetized cats. However, extensive spike triggered averaging (STA) of the VSD signals revealed coherent spontaneous activity also in the awake primate. Some cells exhibited coherent activity with large assemblies in both area V1 and V2. Cortical states related to orientation representations, if any had a short life time and short coherence length,  much smaller than those found in the anesthetized cats. These results suggest that ongoing activity is richer in fast spatio-temporal patterns in awake animal. Therefore, it may play multiple functional role in the awake primate, rather than being an epi-phenomenon of anesthetized preparations. However the exact functional role remained to be evaluated.

02:30 PM
03:15 PM
Mriganka Sur - Plasticity and Dynamics of V1 Networks

N/A

Wednesday, April 25, 2007
Time Session
09:00 AM
10:00 AM
David Ferster , Ken Miller - Contrast-invariant orientation tuning, surround suppression, and inhibition-stabilized networks

In area V1 and other visual cortical areas, the response of neurons to stimuli within the classical receptive field (RF) are modulated by stimuli outside the classical RF (in the "surround"). Surround modulation is likely to be the neural correlate of context-dependent perception. We have investigated the neural circuits underlying surround modulation in macaque V1.


In a first set of studies, we quantitatively compared the spatial dimensions of V1 neurons RF center and surround with the visuotopic extent of geniculocortical feedforward, intra-V1 horizontal and extrastriate feedback connections to V1. We found that the surround of V1 neurons can extend well beyond the monosynaptic range of feedforward (Angelucci & Sainsbury, J Comp. Neurol. 2006) and horizontal connections (Angelucci et al., J. Neurosci. 2002), but well within the range of feedback connections (Angelucci et al., J. Neurosci. 2002). These results and evidence from other laboratories on the slow propagation of signals along horizontal axons, suggested that feedforward connections and polysynaptic chains of horizontal connections cannot underlie the fast onset of modulation arising from the "far" surround of V1 neurons. Feedback connections are thus the most likely substrate for far surround modulation. Feedforward and horizontal connections, however, are likely to underlie "near" surround modulation.


We have used an anatomically and physiologically constrained recurrent network model of macaque V1, to investigate the relative contribution of horizontal and feedback connections to the size tuning properties of V1 neurons, and to surround modulation in V1 (Schwabe et al., J. Neurosci. 2006). In the model, both horizontal and feedback connections contribute to contrast-dependent increases in RF size and to near surround modulation, whereas feedback connections underlie far surround modulation. To account for the anatomical observation that excitatory feedback axons target almost exclusively excitatory neurons in V1, in our model feedback neurons in the far surround exert their suppressive influence via contacts with excitatory neurons in the near surround sending monosynaptic horizontal connections to excitatory and inhibitory neurons in the RF center. Our model can account for a wide range of physiological data and generates several testable predictions. We are currently working to incorporate surround suppression of LGN afferents into our network model, so as to investigate the specific contribution of feedforward connections to surround modulation in V1.


A central prediction of our feedback model is that the "suppressive" far surround of V1 neurons can be facilitatory under conditions that weakly activate neurons in the RF center. We have tested this model prediction (Ichida et al., 2007) by recording the response of single neurons in macaque V1 to a center-surround stimulus in which the surround grating does not directly activate feedforward and monosynaptic horizontal connections to the RF center. This stimulus allowed us to isolate the modulatory signals arising from the far surround. As predicted by the model, we found iso-orientation far surround facilitation when the RF center was driven by a low-contrast stimulus, and the far surround by a small annular stimulus. Suppression occurred when the center stimulus contrast or the size of the surround stimulus were increased. This suggests that center-surround interactions result from excitatory and inhibitory mechanisms of similar spatial extent, and changes in their balance determine whether facilitation or suppression occurs.


In layer 4C, the main target of geniculocortical afferents which lacks long-range intra-cortical connections, far surround facilitation was rare and large surround fields (suppressive and facilitatory) were absent. In contrast, near surround facilitation was strongest in layer 4C and other layers of V1 that receive direct LGN input. This suggests that while feedforward connections may contribute to near surround facilitation, they do not contribute to far surround facilitation or suppression; the latter is thus generated by intra-cortical mechanisms, likely involving top-down feedback.


Work done in collaboration with J.S. Lund, J.B.Levitt, L. Schwabe, J.M. Ichida, and S. Shushruth.

11:15 AM
12:00 PM
Charles Gray - Neuronal Processing of Natural Scenes in Visual Cortex

The most common paradigm for studying visual cortical processing has been to examine the activity of single neurons in response to artificial stimuli such as bars and gratings. While this approach has been highly successful, very little has been learned about how groups of neurons jointly respond to natural scenes. To address this issue, we have recorded spike activity from small groups of 3-10 well isolated, single units in the striate cortex of anesthetized cats and alert monkeys and analyzed their responses to the repeated presentation of short episodes of time-varying natural scenes (movies). We find that the responses of striate neurons to movies are brief, decorrelated, and exhibit high population sparseness. Adjacent neurons differ significantly in their peak firing rates, even when they responding to the same frames of a movie. Pairs of adjacent neurons recorded by the same probe exhibit as much heterogeneity in their responses as pairs recorded by different probes. During periods of joint activity, some cell pairs exhibit transient episodes of synchronous firing indicating a time- and stimulus-dependence on the occurrence and strength of synchronous activity. These findings demonstrate that complex natural scenes evoke highly heterogeneous, but sparse, responses within local populations that can be transiently synchronized, and thereby reveal features of visual cortical dynamics not readily apparent in response to simple stimuli.



 
02:30 PM
03:15 PM
Ning Qian - Physiologically Based Models for Conventional and da Vinci Stereopsis

I will present some of our work on modeling binocular depth perception based on physiological properties of binocular cells in the visual cortex. I will show that binocular disparity maps can be effectively computed from stereograms with a population of complex cells, without explicit feature matching. I will describe a unified theory for understanding depth effects of both horizontal and vertical disparities, and will show that our stereo model can be naturally combined with the motion-energy model to explain a family of Pulfrich depth illusions. I will also address some recent criticisms of our models. Finally, I will discuss da Vinci stereopsis and present a simple model for determining the location and ocularity of monocularly occluded regions using disparity-boundary-selective cells.



 
Thursday, April 26, 2007
Time Session
12:00 AM
07:00 PM
Rudiger Von Der Heydt - Neurophysiological Experiments on Figure-Ground Organization and Selective Visual Attention

A number of phenomena in visual perception can be related to the assignment of 'border ownership', a hypothetical process of detecting contours of objects and assigning them to the corresponding image regions. Border ownership assignment relates to the interpretation of the image in terms of a 3D layout of objects and the perception of shadows and transparent overlay; it affects recognition of form and deployment of attention; figures draw attention, while shapes of the ground tend to be ignored. In this lecture I will review evidence for border ownership coding in the visual cortex of macaques and discuss recent neurophysiological experiments on the relationship between figure-ground organization and attention. These new results show that border ownership coding and voluntary (top-down) attention influences are combined in single neurons of area V2. Border ownership coding was found for attended as well as for ignored figures, indicating that figure-ground organization occurs preattentively, and in parallel across the image. Tests with two overlapping figures revealed spatial asymmetry of the attention effect relative to the receptive field of the individual neuron. This asymmetry was correlated with border ownership preference, indicating that border ownership and attention mechanisms share critical neural circuitry. A possible interpretation is that the neural network that creates figure-ground organization also provides the interface for the top-down selection process. Thus, the phenomena of figure-ground organization may reflect the general process of recoding a visual image representation into a more efficient data structure that enables further, object-based processing.

09:00 AM
09:45 AM
Risto Miikkulainen - The computational role of lateral and feedback connections in the primary visual cortex

The primary visual cortex can be modeled computationally as a self-organizing map in a dynamic equilibrium with afferent, lateral, and feedback input. Simulated experiments with such a unified model, LISSOM, demonstrate how a wide variety of phenomena follow from this principle. The map organizes into orientation, direction, ocular dominance, and color selective patches, and develops selective patchy lateral connections between them. The model demonstrates how the map can recover from retinal and cortical injury, as well as how psychophysical phenomena such as tilt aftereffects and contour integration may arise in it. For instance, the model suggests how Kanizsa-type illusory contours can arise based on feedback from a higher area, a prediction that was subsequently verified through VSD optical imaging on macaque V1. In this manner, the model can be used to gain a precise computational understanding of existing data, and to make specific predictions for future biological experiments.



 
10:00 AM
11:00 AM
Zhaoping Li - Surface border ownership in V2 by Intra-cortical mechanisms --- a model

A border between two image regions normally belongs to only one of the regions; determining which one it belongs to is essential for surface perception and figure-ground segmentation. Von der Heydt and colleagues have observed that border ownership is signaled by a class of V2 neurons, even though the ownership value depends on information coming from well outside the classical receptive fields of the cells. I use a model of V2 to show that this visual area is able to generate the ownership signal by itself, without requiring any top-down mechanism or external explicit labels for figures, T junctions, or corners. In the model, neurons have spatially local classical receptive fields, are tuned to orientation, and receive information (from V1) about the location and orientation of borders. Border ownership signals that model physiological observations arise through finite range, intraareal interactions. Additional effects from surface features and attention are discussed. The model licenses testable predictions.

11:15 AM
12:00 PM
Andreas Burkhalter - Inhibitory control of excitation in feedforward and feedback circuits between lower and higher areas of mouse visual cortex

Primate visual cortex contains multiple functionally specialized areas which are linked by feedforward (FF) and feedback (FB) connections within a hierarchical network. We have found that mouse visual cortex has a similar organization in that it contains at least ten visuotopically organized areas which are interconnected by reciprocal FF and FB circuits. Our anatomical studies have shown that interareal FF and FB connections are made almost exclusively by pyramidal neurons which in the target area form excitatory synapses onto pyramidal cells and GABAergic interneurons. Both pathways provide 12-15% of inputs to interneurons, whereas the rest goes to pyramidal cells. Whole cell patch clamp recordings in acute slices have further shown that FF and FB circuits contain similar distributions of fast spiking (45-48%), regular spiking nonpyramidal cells (31-33%), irregular spiking (4-16%) and low threshold spiking (6-7%) neurons and that these cells provide disynaptic 'feedforward' inhibition to pyramidal cells. Each of these cell types receives approximately 10 to 16 FF and FB inputs. Despite these similarities, we have found that FB inputs generate smaller fast GABA-A receptor-mediated postsynaptic inhibitory currents than FF inputs. One reason for this is that FB synapses are more strongly depressing than FF synapses and that they deplete more quickly during repetitive stimulation. Another reason is that fast spiking neurons in the FB pathway have a more positive spike threshold (-36 mV) than in the FF pathway (-44 mV), which may decrease the number of simultaneously active interneurons and lower the inhibitory output of the FB circuit. In addition to the pathway-differences in fast synaptic inhibition, we found that slow GABA-B receptor-mediated inhibition is much more powerful in the FF than in the FB circuit. Strong GABA-B receptor-mediated inhibition was also found after stimulation of thalamocortical (TC) afferents and horizontal connections (HC) within V1. As a result of the strong slow inhibition, FF, TC and HC circuits generate much less recurrent polysynaptic excitation than FB circuits.


Contextual information is represented by influences of the receptive field surround on neuronal firing evoked by visual stimulation of the receptive field center. It is thought that surround inputs represent modulatory HC and/or FB influences on TC responses. We have simulated these interactions by co-activating HC+TC and FB+TC inputs in cortical slices. The results show that in both conditions the combined inputs produce stronger monosynaptic excitation, but the combined responses were always smaller that the arithmetic sum of the separate responses. In contrast, polysynaptic excitation evoked by co-activation of FB+TC was larger than the arithmetic sum, whereas recurrent excitation due to stimulation of HC+TC was suppressed by strong activation of GABA-B receptor-mediated slow inhibition. These results suggest that the reported suppressive and facilitating effects of the receptive field surround on its center are due to diverse 'feedforward' inhibitory mechanisms in TC, HC, FF and FB circuits.

01:30 PM
02:30 PM
Paul Bressloff, Alessandra Angelucci - The contribution of top-own feedback to the far surround of V1 neurons

n area V1 and other visual cortical areas, the response of neurons to stimuli within the classical receptive field (RF) are modulated by stimuli outside the classical RF (in the "surround"). Surround modulation is likely to be the neural correlate of context-dependent perception. We have investigated the neural circuits underlying surround modulation in macaque V1.


In a first set of studies, we quantitatively compared the spatial dimensions of V1 neurons RF center and surround with the visuotopic extent of geniculocortical feedforward, intra-V1 horizontal and extrastriate feedback connections to V1. We found that the surround of V1 neurons can extend well beyond the monosynaptic range of feedforward (Angelucci & Sainsbury, J Comp. Neurol. 2006) and horizontal connections (Angelucci et al., J. Neurosci. 2002), but well within the range of feedback connections (Angelucci et al., J. Neurosci. 2002). These results and evidence from other laboratories on the slow propagation of signals along horizontal axons, suggested that feedforward connections and polysynaptic chains of horizontal connections cannot underlie the fast onset of modulation arising from the "far" surround of V1 neurons. Feedback connections are thus the most likely substrate for far surround modulation. Feedforward and horizontal connections, however, are likely to underlie "near" surround modulation.


We have used an anatomically and physiologically constrained recurrent network model of macaque V1, to investigate the relative contribution of horizontal and feedback connections to the size tuning properties of V1 neurons, and to surround modulation in V1 (Schwabe et al., J. Neurosci. 2006). In the model, both horizontal and feedback connections contribute to contrast-dependent increases in RF size and to near surround modulation, whereas feedback connections underlie far surround modulation. To account for the anatomical observation that excitatory feedback axons target almost exclusively excitatory neurons in V1, in our model feedback neurons in the far surround exert their suppressive influence via contacts with excitatory neurons in the near surround sending monosynaptic horizontal connections to excitatory and inhibitory neurons in the RF center. Our model can account for a wide range of physiological data and generates several testable predictions. We are currently working to incorporate surround suppression of LGN afferents into our network model, so as to investigate the specific contribution of feedforward connections to surround modulation in V1.


A central prediction of our feedback model is that the "suppressive" far surround of V1 neurons can be facilitatory under conditions that weakly activate neurons in the RF center. We have tested this model prediction (Ichida et al., 2007) by recording the response of single neurons in macaque V1 to a center-surround stimulus in which the surround grating does not directly activate feedforward and monosynaptic horizontal connections to the RF center. This stimulus allowed us to isolate the modulatory signals arising from the far surround. As predicted by the model, we found iso-orientation far surround facilitation when the RF center was driven by a low-contrast stimulus, and the far surround by a small annular stimulus. Suppression occurred when the center stimulus contrast or the size of the surround stimulus were increased. This suggests that center-surround interactions result from excitatory and inhibitory mechanisms of similar spatial extent, and changes in their balance determine whether facilitation or suppression occurs.


In layer 4C, the main target of geniculocortical afferents which lacks long-range intra-cortical connections, far surround facilitation was rare and large surround fields (suppressive and facilitatory) were absent. In contrast, near surround facilitation was strongest in layer 4C and other layers of V1 that receive direct LGN input. This suggests that while feedforward connections may contribute to near surround facilitation, they do not contribute to far surround facilitation or suppression; the latter is thus generated by intra-cortical mechanisms, likely involving top-down feedback.

02:45 PM
03:30 PM
Tai Sing Lee - Natural scene statistics and visual inference

Recurrent feedback in the visual cortex can potentially be conceptualized as a mechanism for mediating the influence of prior beliefs in a hierarchical Bayesian inference framework. We consider the computational problem of 3D shape inference based on monocular and binocular cues. We present evidence suggesting that neuronal tuning and neuronal interaction in the primary visual cortex encode ecological statistical priors between 3D scene structures and 2D images relevant for 3D inference. These sensitivities, together with evidence on the response dynamics of neurons in the early visual cortex, are consistent with the hierarchical Bayesian perspective on visual processing.



 
Friday, April 27, 2007
Time Session
09:00 AM
09:45 AM
Dana Ballard - Discrete predictive feedback can account for biphasic responses of LGN cells

Many receptive fields in lateral geniculate nucleus (LGN) are biphasic in time, i.e. a bright (dark) excitatory phase is followed by a dark (bright) excitatory phase. We describe a hierarchical model of predictive coding and simulations that capture these changing neuronal response properties. The model is composed of two areas, resembling the LGN and primary visual cortex (V1). Model V1 attempts to predict its LGN inputs, while neurons in LGN signal the difference between actual input and the V1 predictions. After training on natural images, model V1 receptive fields resemble simple cell receptive fields. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN.


Furthermore we show that special care must be taken in using signed line codings in such circuits. The biophysics of nerve cells does not allow for a straightforward implementation of simple signed algebraic relationships, but these can still be implemented if sufficient care is taken.


The model predicts a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses and neurons of opposite polarity increase their responses due to feedback. This phase-reversed pattern of influence was recently confirmed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.

10:00 AM
10:45 AM
Jack Gallant - Feature-based attention dynamically changes shape representation in area V4

Top-down processes such as attention and memory enable selective filtering of sensory information in order to meet behavioral demands, but the mechanisms underlying this process remain unknown. Most evidence supports the idea that attention highlights attended locations or features by enhancing neuronal responses but does not change stimulus selectivity. However, some theoretical work suggests that attention might alter the way neurons encode visual information. In two experiments we have demonstrated that feature-based attention alters the multidimensional tuning curves of many V4 neurons, thereby dynamically changing the way that shape is represented in this area. This attentional modulation enhances responses to attended features, consistent with a matched filter model of visual attention. Our results demonstrate that some V4 neurons do not act as "labeled lines," and they have important implications for the way the visual network must be designed in order to permit correct decoding and interpretation of the information in V4 by higher visual areas. Taken together, our results suggest that memory and decision-making processes required for natural vision are integrated into the basic processes of visual representation and distributed widely across the neocortex.



 
Name Email Affiliation
Aguda, Baltazar bdaguda@gmail.com MBI - Long Term Visitor, Bioinformatics Institute, Singapore
Albright , Thomas tom@salk.edu Systems Neurobiology Laboratories, University of California, San Diego
Angelucci , Alessandra alessandra.angelucci@hsc.utah.edu Ophthalmology and Visual Science, University of Utah
Bair, Wyeth wyeth@physiol.ox.ac.uk Biological Structure, University of Washington
Baker , Jonathan jbaker@nervana.montana.edu Center for Computational Biology, Montana State University
Ballard , Dana dana@cs.rochester.edu Computer Science, University of Texas
Bednar, James jbednar@inf.ed.ac.uk Institute for Adaptive and Neural Computation, University of Edinburgh
Best, Janet jbest@mbi.osu.edu
Bressloff, Paul bressloff@math.utah.edu Department of Mathematics, University of Utah
Burkhalter , Andreas burkhala@pcg.wustl.edu Anatomy and Neurobiology, Washington University School of Medicine
Casagrande, Vivien vivien.casagrande@vanderbilt.edu Cell & Developmental Biology, Vanderbilt University
Covic, Elise eliseg@uchicago.edu Biological Sciences, University of Chicago
Dembele, Bassidy bdembele@mbi.osu.edu MBI - Long Term Visitor, Howard University
Djordjevic, Marko mdjordjevic@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Enciso, German German_Enciso@hms.harvard.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Federer, Fred Moran Eye Center, University of Utah
Ferster , David ferster@northwestern.edu Neurobiology and Physiology, J. L. Kellogg Graduate School of Management
Fitzpatrick, David wendy.lesesne@duke.edu Neurobiology, Duke University Medical Center
Freeman, Ralph freeman@neurovision.berkeley.edu School of Optometry, University of California, Berkeley
Fregnac, Yves Yves.Fregnac@iaf.cnrs-gif.fr Institute of Neurobiology, U.N.I.C.
Gallant, Jack gallant@berkeley.edu Deptment of Psychology, University of California, Berkeley
Grajdeanu, Paula pgrajdeanu@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Gray , Charles cmgray@nervana.montana.edu Center for Computational Biology, Montana State University
Green, Edward egreen@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Guillery , Ray rguiller@facstaff.wisc.edu Anatomy, Marmara University
Hartvigsen, Gregg hartvig@geneseo.edu MBI-Long Term Visitor, University at Albany (SUNY)
Huertas, Marco mahuer@wm.edu Department of Applied Science, College of William and Mary
Kao, Chiu-Yen kao.71@osu.edu MBI - Long Term Visitor, The Ohio State University
Kilpatrick, Zachary kilpatri@math.utah.edu Mathematics, University of Utah
Kim, Yangjin ykim@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Koelling, Melinda melinda.koelling@wmich.edu Department of Mathematics, Western Michigan University
LaMar, Michael drew.lamar@gmail.com Department of Biology, College of William and Mary
Lee , Tai Sing or Computer Science, Carnegie Mellon University
Levitt, Jonathan jlevitt@ccny.cuny.edu Deptment of Biology, City University of New York (CUNY)
Li , Zhaoping z.li@ucl.ac.uk Psychology, University College London
Lindsey, Delwin lindsey.43@osu.edu Psychology, The Ohio State University
Lou, Yuan lou@math.ohio-state.edu MBI - Long Term Visitor, The Ohio State University
Lund, Jennifer jennifer.lund@hsc.utah.edu Department of Ophthalmology, University of Utah
Mangel, Stuart mangel.1@osu.edu Dept of Neuroscience, The Ohio State University
Marre, Olivier marre@unic.cnrs-gif.fr Institute of Neurobiology, U.N.I.C
McLelland, Douglas douglas.mclelland@linacre.oxford.ac.uk Dept. of Physiology, Anatomy, & Genetics, University of Oxford
Miikkulainen , Risto risto@cs.utexas.edu Dept. of Computer Sciences & Inst. for Neuroscience, University of Texas
Miller, Ken kendmiller@gmail.com Ctr. for Neurobiology and Behavior, Columbia University
Nevai, Andrew anevai@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Omer, David B. david.backlash@weizmann.ac.il The Neurobiology Dept., The Weizmann Institute of Science
Oster, Andrew aoester@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Qian , Ning nq6@columbia.edu Ctr. Neurobiology & Behavior, Columbia University
Rempe, Michael mrempe@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Richards, Blake Dept. of Physiology, Anatomy, & Medical Genetics, University of Oxford
Schugart, Richard richard.schugart@wku.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Schwabe, Lars lars.schwabe@epfl.ch Lab. of Cognitive Neuroscience, Brain Mind Institute, Swiss Federal Institute of Technology
Shapley , Robert shapley@cns.nyu.edu Center for Neural Science, New York University
Sherman, Murray msherman@bsd.uchicago.edu Neurobiology, University of Chicago
Shushruth, Shushruth shushruth.s@utah.edu Moran Eye Center, University of Utah
Sit, Yiu Fai yfsit@cs.utexas.edu Computer Science, University of Texas
Smith, Gregory greg@as.wm.edu Applied Science, College of William and Mary
Solomon, Sam samuels@physiol.usyd.edu.au Bosch Institute and School of Medical Sciences, University of Sydney
Spratling , Michael michael.spratling@kcl.ac.uk Division of Engineering, King's College
Srinivasan, Partha p.srinivasan35@csuohio.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Stigler, Brandy bstigler@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Sun, Shuying ssun@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Sur , Mriganka Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
Szomolay, Barbara b.szomolay@imperial.ac.uk Mathematical Biosciences Institute (MBI), The Ohio State University
Terman, David terman@math.ohio-state.edu Mathemathics Department, The Ohio State University
Thaler, Lore thaler.11@osu.edu Psychology, The Ohio State University
Thomas, Evelyn ethomas@mbi.osu.edu MBI - Long Term Visitor, Howard University
Tian, Paul tianjj@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Tucker, James jamestucker@gmail.com Moran Eye Center, University of Utah
Usrey, Martin wmusrey@ucdavis.edu Center for Neuroscience, University of California, Davis
Varela, Carmen cvarela@bsd.uchicago.edu Biological Sciences, University of Chicago
Von Der Heydt, Rudiger von.der.heydt@jhu.edu Krieger Mind/Brain Institute, Johns Hopkins University
Wang, Ying wang@math.ohio-state.edu Math Department, The Ohio State University
Williamson, Janelle janelle.jeffs@hsc.utah.edu Moran Eye Center, University of Utah
Zucker , Steven steven.zucker@yale.edu Computer Science, Yale University
The contribution of top-own feedback to the far surround of V1 neurons

n area V1 and other visual cortical areas, the response of neurons to stimuli within the classical receptive field (RF) are modulated by stimuli outside the classical RF (in the "surround"). Surround modulation is likely to be the neural correlate of context-dependent perception. We have investigated the neural circuits underlying surround modulation in macaque V1.


In a first set of studies, we quantitatively compared the spatial dimensions of V1 neurons RF center and surround with the visuotopic extent of geniculocortical feedforward, intra-V1 horizontal and extrastriate feedback connections to V1. We found that the surround of V1 neurons can extend well beyond the monosynaptic range of feedforward (Angelucci & Sainsbury, J Comp. Neurol. 2006) and horizontal connections (Angelucci et al., J. Neurosci. 2002), but well within the range of feedback connections (Angelucci et al., J. Neurosci. 2002). These results and evidence from other laboratories on the slow propagation of signals along horizontal axons, suggested that feedforward connections and polysynaptic chains of horizontal connections cannot underlie the fast onset of modulation arising from the "far" surround of V1 neurons. Feedback connections are thus the most likely substrate for far surround modulation. Feedforward and horizontal connections, however, are likely to underlie "near" surround modulation.


We have used an anatomically and physiologically constrained recurrent network model of macaque V1, to investigate the relative contribution of horizontal and feedback connections to the size tuning properties of V1 neurons, and to surround modulation in V1 (Schwabe et al., J. Neurosci. 2006). In the model, both horizontal and feedback connections contribute to contrast-dependent increases in RF size and to near surround modulation, whereas feedback connections underlie far surround modulation. To account for the anatomical observation that excitatory feedback axons target almost exclusively excitatory neurons in V1, in our model feedback neurons in the far surround exert their suppressive influence via contacts with excitatory neurons in the near surround sending monosynaptic horizontal connections to excitatory and inhibitory neurons in the RF center. Our model can account for a wide range of physiological data and generates several testable predictions. We are currently working to incorporate surround suppression of LGN afferents into our network model, so as to investigate the specific contribution of feedforward connections to surround modulation in V1.


A central prediction of our feedback model is that the "suppressive" far surround of V1 neurons can be facilitatory under conditions that weakly activate neurons in the RF center. We have tested this model prediction (Ichida et al., 2007) by recording the response of single neurons in macaque V1 to a center-surround stimulus in which the surround grating does not directly activate feedforward and monosynaptic horizontal connections to the RF center. This stimulus allowed us to isolate the modulatory signals arising from the far surround. As predicted by the model, we found iso-orientation far surround facilitation when the RF center was driven by a low-contrast stimulus, and the far surround by a small annular stimulus. Suppression occurred when the center stimulus contrast or the size of the surround stimulus were increased. This suggests that center-surround interactions result from excitatory and inhibitory mechanisms of similar spatial extent, and changes in their balance determine whether facilitation or suppression occurs.


In layer 4C, the main target of geniculocortical afferents which lacks long-range intra-cortical connections, far surround facilitation was rare and large surround fields (suppressive and facilitatory) were absent. In contrast, near surround facilitation was strongest in layer 4C and other layers of V1 that receive direct LGN input. This suggests that while feedforward connections may contribute to near surround facilitation, they do not contribute to far surround facilitation or suppression; the latter is thus generated by intra-cortical mechanisms, likely involving top-down feedback.

Discrete predictive feedback can account for biphasic responses of LGN cells

Many receptive fields in lateral geniculate nucleus (LGN) are biphasic in time, i.e. a bright (dark) excitatory phase is followed by a dark (bright) excitatory phase. We describe a hierarchical model of predictive coding and simulations that capture these changing neuronal response properties. The model is composed of two areas, resembling the LGN and primary visual cortex (V1). Model V1 attempts to predict its LGN inputs, while neurons in LGN signal the difference between actual input and the V1 predictions. After training on natural images, model V1 receptive fields resemble simple cell receptive fields. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN.


Furthermore we show that special care must be taken in using signed line codings in such circuits. The biophysics of nerve cells does not allow for a straightforward implementation of simple signed algebraic relationships, but these can still be implemented if sufficient care is taken.


The model predicts a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses and neurons of opposite polarity increase their responses due to feedback. This phase-reversed pattern of influence was recently confirmed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.

The contribution of top-own feedback to the far surround of V1 neurons

n area V1 and other visual cortical areas, the response of neurons to stimuli within the classical receptive field (RF) are modulated by stimuli outside the classical RF (in the "surround"). Surround modulation is likely to be the neural correlate of context-dependent perception. We have investigated the neural circuits underlying surround modulation in macaque V1.


In a first set of studies, we quantitatively compared the spatial dimensions of V1 neurons RF center and surround with the visuotopic extent of geniculocortical feedforward, intra-V1 horizontal and extrastriate feedback connections to V1. We found that the surround of V1 neurons can extend well beyond the monosynaptic range of feedforward (Angelucci & Sainsbury, J Comp. Neurol. 2006) and horizontal connections (Angelucci et al., J. Neurosci. 2002), but well within the range of feedback connections (Angelucci et al., J. Neurosci. 2002). These results and evidence from other laboratories on the slow propagation of signals along horizontal axons, suggested that feedforward connections and polysynaptic chains of horizontal connections cannot underlie the fast onset of modulation arising from the "far" surround of V1 neurons. Feedback connections are thus the most likely substrate for far surround modulation. Feedforward and horizontal connections, however, are likely to underlie "near" surround modulation.


We have used an anatomically and physiologically constrained recurrent network model of macaque V1, to investigate the relative contribution of horizontal and feedback connections to the size tuning properties of V1 neurons, and to surround modulation in V1 (Schwabe et al., J. Neurosci. 2006). In the model, both horizontal and feedback connections contribute to contrast-dependent increases in RF size and to near surround modulation, whereas feedback connections underlie far surround modulation. To account for the anatomical observation that excitatory feedback axons target almost exclusively excitatory neurons in V1, in our model feedback neurons in the far surround exert their suppressive influence via contacts with excitatory neurons in the near surround sending monosynaptic horizontal connections to excitatory and inhibitory neurons in the RF center. Our model can account for a wide range of physiological data and generates several testable predictions. We are currently working to incorporate surround suppression of LGN afferents into our network model, so as to investigate the specific contribution of feedforward connections to surround modulation in V1.


A central prediction of our feedback model is that the "suppressive" far surround of V1 neurons can be facilitatory under conditions that weakly activate neurons in the RF center. We have tested this model prediction (Ichida et al., 2007) by recording the response of single neurons in macaque V1 to a center-surround stimulus in which the surround grating does not directly activate feedforward and monosynaptic horizontal connections to the RF center. This stimulus allowed us to isolate the modulatory signals arising from the far surround. As predicted by the model, we found iso-orientation far surround facilitation when the RF center was driven by a low-contrast stimulus, and the far surround by a small annular stimulus. Suppression occurred when the center stimulus contrast or the size of the surround stimulus were increased. This suggests that center-surround interactions result from excitatory and inhibitory mechanisms of similar spatial extent, and changes in their balance determine whether facilitation or suppression occurs.


In layer 4C, the main target of geniculocortical afferents which lacks long-range intra-cortical connections, far surround facilitation was rare and large surround fields (suppressive and facilitatory) were absent. In contrast, near surround facilitation was strongest in layer 4C and other layers of V1 that receive direct LGN input. This suggests that while feedforward connections may contribute to near surround facilitation, they do not contribute to far surround facilitation or suppression; the latter is thus generated by intra-cortical mechanisms, likely involving top-down feedback.

Inhibitory control of excitation in feedforward and feedback circuits between lower and higher areas of mouse visual cortex

Primate visual cortex contains multiple functionally specialized areas which are linked by feedforward (FF) and feedback (FB) connections within a hierarchical network. We have found that mouse visual cortex has a similar organization in that it contains at least ten visuotopically organized areas which are interconnected by reciprocal FF and FB circuits. Our anatomical studies have shown that interareal FF and FB connections are made almost exclusively by pyramidal neurons which in the target area form excitatory synapses onto pyramidal cells and GABAergic interneurons. Both pathways provide 12-15% of inputs to interneurons, whereas the rest goes to pyramidal cells. Whole cell patch clamp recordings in acute slices have further shown that FF and FB circuits contain similar distributions of fast spiking (45-48%), regular spiking nonpyramidal cells (31-33%), irregular spiking (4-16%) and low threshold spiking (6-7%) neurons and that these cells provide disynaptic 'feedforward' inhibition to pyramidal cells. Each of these cell types receives approximately 10 to 16 FF and FB inputs. Despite these similarities, we have found that FB inputs generate smaller fast GABA-A receptor-mediated postsynaptic inhibitory currents than FF inputs. One reason for this is that FB synapses are more strongly depressing than FF synapses and that they deplete more quickly during repetitive stimulation. Another reason is that fast spiking neurons in the FB pathway have a more positive spike threshold (-36 mV) than in the FF pathway (-44 mV), which may decrease the number of simultaneously active interneurons and lower the inhibitory output of the FB circuit. In addition to the pathway-differences in fast synaptic inhibition, we found that slow GABA-B receptor-mediated inhibition is much more powerful in the FF than in the FB circuit. Strong GABA-B receptor-mediated inhibition was also found after stimulation of thalamocortical (TC) afferents and horizontal connections (HC) within V1. As a result of the strong slow inhibition, FF, TC and HC circuits generate much less recurrent polysynaptic excitation than FB circuits.


Contextual information is represented by influences of the receptive field surround on neuronal firing evoked by visual stimulation of the receptive field center. It is thought that surround inputs represent modulatory HC and/or FB influences on TC responses. We have simulated these interactions by co-activating HC+TC and FB+TC inputs in cortical slices. The results show that in both conditions the combined inputs produce stronger monosynaptic excitation, but the combined responses were always smaller that the arithmetic sum of the separate responses. In contrast, polysynaptic excitation evoked by co-activation of FB+TC was larger than the arithmetic sum, whereas recurrent excitation due to stimulation of HC+TC was suppressed by strong activation of GABA-B receptor-mediated slow inhibition. These results suggest that the reported suppressive and facilitating effects of the receptive field surround on its center are due to diverse 'feedforward' inhibitory mechanisms in TC, HC, FF and FB circuits.

Contrast-invariant orientation tuning, surround suppression, and inhibition-stabilized networks

In area V1 and other visual cortical areas, the response of neurons to stimuli within the classical receptive field (RF) are modulated by stimuli outside the classical RF (in the "surround"). Surround modulation is likely to be the neural correlate of context-dependent perception. We have investigated the neural circuits underlying surround modulation in macaque V1.


In a first set of studies, we quantitatively compared the spatial dimensions of V1 neurons RF center and surround with the visuotopic extent of geniculocortical feedforward, intra-V1 horizontal and extrastriate feedback connections to V1. We found that the surround of V1 neurons can extend well beyond the monosynaptic range of feedforward (Angelucci & Sainsbury, J Comp. Neurol. 2006) and horizontal connections (Angelucci et al., J. Neurosci. 2002), but well within the range of feedback connections (Angelucci et al., J. Neurosci. 2002). These results and evidence from other laboratories on the slow propagation of signals along horizontal axons, suggested that feedforward connections and polysynaptic chains of horizontal connections cannot underlie the fast onset of modulation arising from the "far" surround of V1 neurons. Feedback connections are thus the most likely substrate for far surround modulation. Feedforward and horizontal connections, however, are likely to underlie "near" surround modulation.


We have used an anatomically and physiologically constrained recurrent network model of macaque V1, to investigate the relative contribution of horizontal and feedback connections to the size tuning properties of V1 neurons, and to surround modulation in V1 (Schwabe et al., J. Neurosci. 2006). In the model, both horizontal and feedback connections contribute to contrast-dependent increases in RF size and to near surround modulation, whereas feedback connections underlie far surround modulation. To account for the anatomical observation that excitatory feedback axons target almost exclusively excitatory neurons in V1, in our model feedback neurons in the far surround exert their suppressive influence via contacts with excitatory neurons in the near surround sending monosynaptic horizontal connections to excitatory and inhibitory neurons in the RF center. Our model can account for a wide range of physiological data and generates several testable predictions. We are currently working to incorporate surround suppression of LGN afferents into our network model, so as to investigate the specific contribution of feedforward connections to surround modulation in V1.


A central prediction of our feedback model is that the "suppressive" far surround of V1 neurons can be facilitatory under conditions that weakly activate neurons in the RF center. We have tested this model prediction (Ichida et al., 2007) by recording the response of single neurons in macaque V1 to a center-surround stimulus in which the surround grating does not directly activate feedforward and monosynaptic horizontal connections to the RF center. This stimulus allowed us to isolate the modulatory signals arising from the far surround. As predicted by the model, we found iso-orientation far surround facilitation when the RF center was driven by a low-contrast stimulus, and the far surround by a small annular stimulus. Suppression occurred when the center stimulus contrast or the size of the surround stimulus were increased. This suggests that center-surround interactions result from excitatory and inhibitory mechanisms of similar spatial extent, and changes in their balance determine whether facilitation or suppression occurs.


In layer 4C, the main target of geniculocortical afferents which lacks long-range intra-cortical connections, far surround facilitation was rare and large surround fields (suppressive and facilitatory) were absent. In contrast, near surround facilitation was strongest in layer 4C and other layers of V1 that receive direct LGN input. This suggests that while feedforward connections may contribute to near surround facilitation, they do not contribute to far surround facilitation or suppression; the latter is thus generated by intra-cortical mechanisms, likely involving top-down feedback.


Work done in collaboration with J.S. Lund, J.B.Levitt, L. Schwabe, J.M. Ichida, and S. Shushruth.

Learning to see: The experience-dependent emergence of direction selectivity in visual cortex

Development of the selective response properties that define columns in sensory cortex is thought to begin early in cortical maturation, without the need for visual experience. My talk will focus on recent experiments that explore the development of direction selectivity in visual cortex of the ferret using intrinsic signal and in vivo 2-photon calcium signal imaging. Our results indicate that direction columns, unlike other columnar systems, emerge a few days after eye opening and this emergence depends on visual experience. The impact of visual experience on the emergence of direction columns can be visualized in individual visually naive animals that are exposed to a motion training stimulus. After 10-12 hours of stimulation, direction columns become evident and gradually strengthen. These effects of visual experience are strikingly limited to the cortical neurons that are activated by the training stimulus, and reflect a rapid increase in the direction selectivity of individual layer 2/3 neurons. Taken together, these results suggest that visual experience plays a critical role in the emergence of direction selective cortical responses.



 
Dynamic spatial processing originates in early visual pathways

Several investigations in the visual system have established that coarse spatial features are processed before those of fine detail. Although it is generally assumed that this is a cortical function, there are features of early visual pathways that provide the basis for a coarse-to-fine sequence. We have investigated this possibility by neurophysiological studies in the lateral geniculate nucleus. Our findings show clear coarse-to-fine properties for nearly all LGN neurons in our sample. By use of a model, we estimate that the known temporal dynamic features in the visual cortex may be accounted for by a feed-forward process.

Feature-based attention dynamically changes shape representation in area V4

Top-down processes such as attention and memory enable selective filtering of sensory information in order to meet behavioral demands, but the mechanisms underlying this process remain unknown. Most evidence supports the idea that attention highlights attended locations or features by enhancing neuronal responses but does not change stimulus selectivity. However, some theoretical work suggests that attention might alter the way neurons encode visual information. In two experiments we have demonstrated that feature-based attention alters the multidimensional tuning curves of many V4 neurons, thereby dynamically changing the way that shape is represented in this area. This attentional modulation enhances responses to attended features, consistent with a matched filter model of visual attention. Our results demonstrate that some V4 neurons do not act as "labeled lines," and they have important implications for the way the visual network must be designed in order to permit correct decoding and interpretation of the information in V4 by higher visual areas. Taken together, our results suggest that memory and decision-making processes required for natural vision are integrated into the basic processes of visual representation and distributed widely across the neocortex.



 
Neuronal Processing of Natural Scenes in Visual Cortex

The most common paradigm for studying visual cortical processing has been to examine the activity of single neurons in response to artificial stimuli such as bars and gratings. While this approach has been highly successful, very little has been learned about how groups of neurons jointly respond to natural scenes. To address this issue, we have recorded spike activity from small groups of 3-10 well isolated, single units in the striate cortex of anesthetized cats and alert monkeys and analyzed their responses to the repeated presentation of short episodes of time-varying natural scenes (movies). We find that the responses of striate neurons to movies are brief, decorrelated, and exhibit high population sparseness. Adjacent neurons differ significantly in their peak firing rates, even when they responding to the same frames of a movie. Pairs of adjacent neurons recorded by the same probe exhibit as much heterogeneity in their responses as pairs recorded by different probes. During periods of joint activity, some cell pairs exhibit transient episodes of synchronous firing indicating a time- and stimulus-dependence on the occurrence and strength of synchronous activity. These findings demonstrate that complex natural scenes evoke highly heterogeneous, but sparse, responses within local populations that can be transiently synchronized, and thereby reveal features of visual cortical dynamics not readily apparent in response to simple stimuli.



 
Natural scene statistics and visual inference

Recurrent feedback in the visual cortex can potentially be conceptualized as a mechanism for mediating the influence of prior beliefs in a hierarchical Bayesian inference framework. We consider the computational problem of 3D shape inference based on monocular and binocular cues. We present evidence suggesting that neuronal tuning and neuronal interaction in the primary visual cortex encode ecological statistical priors between 3D scene structures and 2D images relevant for 3D inference. These sensitivities, together with evidence on the response dynamics of neurons in the early visual cortex, are consistent with the hierarchical Bayesian perspective on visual processing.



 
Surface border ownership in V2 by Intra-cortical mechanisms --- a model

A border between two image regions normally belongs to only one of the regions; determining which one it belongs to is essential for surface perception and figure-ground segmentation. Von der Heydt and colleagues have observed that border ownership is signaled by a class of V2 neurons, even though the ownership value depends on information coming from well outside the classical receptive fields of the cells. I use a model of V2 to show that this visual area is able to generate the ownership signal by itself, without requiring any top-down mechanism or external explicit labels for figures, T junctions, or corners. In the model, neurons have spatially local classical receptive fields, are tuned to orientation, and receive information (from V1) about the location and orientation of borders. Border ownership signals that model physiological observations arise through finite range, intraareal interactions. Additional effects from surface features and attention are discussed. The model licenses testable predictions.

The computational role of lateral and feedback connections in the primary visual cortex

The primary visual cortex can be modeled computationally as a self-organizing map in a dynamic equilibrium with afferent, lateral, and feedback input. Simulated experiments with such a unified model, LISSOM, demonstrate how a wide variety of phenomena follow from this principle. The map organizes into orientation, direction, ocular dominance, and color selective patches, and develops selective patchy lateral connections between them. The model demonstrates how the map can recover from retinal and cortical injury, as well as how psychophysical phenomena such as tilt aftereffects and contour integration may arise in it. For instance, the model suggests how Kanizsa-type illusory contours can arise based on feedback from a higher area, a prediction that was subsequently verified through VSD optical imaging on macaque V1. In this manner, the model can be used to gain a precise computational understanding of existing data, and to make specific predictions for future biological experiments.



 
Contrast-invariant orientation tuning, surround suppression, and inhibition-stabilized networks

In area V1 and other visual cortical areas, the response of neurons to stimuli within the classical receptive field (RF) are modulated by stimuli outside the classical RF (in the "surround"). Surround modulation is likely to be the neural correlate of context-dependent perception. We have investigated the neural circuits underlying surround modulation in macaque V1.


In a first set of studies, we quantitatively compared the spatial dimensions of V1 neurons RF center and surround with the visuotopic extent of geniculocortical feedforward, intra-V1 horizontal and extrastriate feedback connections to V1. We found that the surround of V1 neurons can extend well beyond the monosynaptic range of feedforward (Angelucci & Sainsbury, J Comp. Neurol. 2006) and horizontal connections (Angelucci et al., J. Neurosci. 2002), but well within the range of feedback connections (Angelucci et al., J. Neurosci. 2002). These results and evidence from other laboratories on the slow propagation of signals along horizontal axons, suggested that feedforward connections and polysynaptic chains of horizontal connections cannot underlie the fast onset of modulation arising from the "far" surround of V1 neurons. Feedback connections are thus the most likely substrate for far surround modulation. Feedforward and horizontal connections, however, are likely to underlie "near" surround modulation.


We have used an anatomically and physiologically constrained recurrent network model of macaque V1, to investigate the relative contribution of horizontal and feedback connections to the size tuning properties of V1 neurons, and to surround modulation in V1 (Schwabe et al., J. Neurosci. 2006). In the model, both horizontal and feedback connections contribute to contrast-dependent increases in RF size and to near surround modulation, whereas feedback connections underlie far surround modulation. To account for the anatomical observation that excitatory feedback axons target almost exclusively excitatory neurons in V1, in our model feedback neurons in the far surround exert their suppressive influence via contacts with excitatory neurons in the near surround sending monosynaptic horizontal connections to excitatory and inhibitory neurons in the RF center. Our model can account for a wide range of physiological data and generates several testable predictions. We are currently working to incorporate surround suppression of LGN afferents into our network model, so as to investigate the specific contribution of feedforward connections to surround modulation in V1.


A central prediction of our feedback model is that the "suppressive" far surround of V1 neurons can be facilitatory under conditions that weakly activate neurons in the RF center. We have tested this model prediction (Ichida et al., 2007) by recording the response of single neurons in macaque V1 to a center-surround stimulus in which the surround grating does not directly activate feedforward and monosynaptic horizontal connections to the RF center. This stimulus allowed us to isolate the modulatory signals arising from the far surround. As predicted by the model, we found iso-orientation far surround facilitation when the RF center was driven by a low-contrast stimulus, and the far surround by a small annular stimulus. Suppression occurred when the center stimulus contrast or the size of the surround stimulus were increased. This suggests that center-surround interactions result from excitatory and inhibitory mechanisms of similar spatial extent, and changes in their balance determine whether facilitation or suppression occurs.


In layer 4C, the main target of geniculocortical afferents which lacks long-range intra-cortical connections, far surround facilitation was rare and large surround fields (suppressive and facilitatory) were absent. In contrast, near surround facilitation was strongest in layer 4C and other layers of V1 that receive direct LGN input. This suggests that while feedforward connections may contribute to near surround facilitation, they do not contribute to far surround facilitation or suppression; the latter is thus generated by intra-cortical mechanisms, likely involving top-down feedback.


Work done in collaboration with J.S. Lund, J.B.Levitt, L. Schwabe, J.M. Ichida, and S. Shushruth.

The dynamics of evoked and ongoing activity in the behaving monkey

Previous findings from Voltage Sensitive Dye Imaging (VSDI) experiments done on anesthetized cats (Grinvald et al., 1989; Arieli et al., 1995; Arieli et al., 1996; Tsodyks et al., 1999; Kenet et al., 2003) indicated that the amplitude of ongoing activity (primarily synaptic potentials) is large, suggesting that it may play an important role in cortical processing by affecting the evoked activity and therefore the final behavior itself. VSDI was recently implemented also on the awake monkey (Slovin et al., 2002; Seidemann et al., 2002;) allowing monitoring of  activity from the same patch of cortex, repetitively,  for more than a year. We investigated the cortical activity in the primary visual cortex of a behaving monkey during both evoked and ongoing conditions. Several questions have been addressed: what are the spatial-temporal characterizations of the ongoing activity in early visual areas of the behaving monkey? How is it related to the functional architecture? We combined simultaneous VSDI with electrophysiological recordings of the local field potential (LFP) single and multi unit activities. In the evoked condition, the monkey was trained to fixate for 10s while presented with a full field moving grating. We found that our fast switching  stimuli abolished the high frequency oscillations at about 30Hz, oscillations that were present in the absence of a stimulus. During the ongoing condition, the monkey was required to sit quietly in a totally dark room. We found that the VSD signals in both conditions are often highly similar to the LFP, just like in the anesthetized cat. The similarity between the VSD signals and LFP was highest within the α (9-14 Hz) frequency band. For the awake monkey, the ratio between amplitude of ongoing and evoked activity was much smaller than what was found in the anesthetized cats. However, extensive spike triggered averaging (STA) of the VSD signals revealed coherent spontaneous activity also in the awake primate. Some cells exhibited coherent activity with large assemblies in both area V1 and V2. Cortical states related to orientation representations, if any had a short life time and short coherence length,  much smaller than those found in the anesthetized cats. These results suggest that ongoing activity is richer in fast spatio-temporal patterns in awake animal. Therefore, it may play multiple functional role in the awake primate, rather than being an epi-phenomenon of anesthetized preparations. However the exact functional role remained to be evaluated.

Physiologically Based Models for Conventional and da Vinci Stereopsis

I will present some of our work on modeling binocular depth perception based on physiological properties of binocular cells in the visual cortex. I will show that binocular disparity maps can be effectively computed from stereograms with a population of complex cells, without explicit feature matching. I will describe a unified theory for understanding depth effects of both horizontal and vertical disparities, and will show that our stereo model can be naturally combined with the motion-energy model to explain a family of Pulfrich depth illusions. I will also address some recent criticisms of our models. Finally, I will discuss da Vinci stereopsis and present a simple model for determining the location and ocularity of monocularly occluded regions using disparity-boundary-selective cells.



 
Visual feature selectivities and spatial interactions in V1 cortex

I will describe a local circuit computational model of a patch of the input layer 4Ca of the primary visual cortex (V1) of the macaque monkey. The model neurons are integrate-and-fire neurons with biologically plausible synaptic conductances. This model can account for the distributions of orientation and spatial frequency selectivity across the population of V1, and also the relative prevalence of linear and nonlinear spatial summation in V1 neurons. The crucial controlling variable appears to be the relative strength of cortico-cortical inhibition relative to excitation, the local circuit. Experiments on responses to spatially extended stimuli indicate that there is also a strong influence of long-distance, possibly feedback, interactions on V1 neurons.



 
The Role of Thalamus: Relay Functions and More

The LGN and pulvinar (a massive but generally mysterious and ignored thalamic relay) are


examples of two different types of relay: the LGN is a first order relay, transmitting information


from a subcortical source (retina), while the pulvinar is mostly a higher order relay, transmitting


information from layer 5 of one cortical area to another area. First and higher order thalamic


relays can also be recognized for the somatosensory and auditory thalamic systems, and this


division of thalamic relays can also be extended beyond sensory systems. Thus the first and


higher order thalamic equivalents of the somatosensory and auditory systems are, respectively,


the ventral posterior nucleus and the posterior medial nucleus (somatosensory), and the ventral


versus dorsal portion of  the medial geniculate nucleus (auditory). Other thalamic nuclei have


also been placed into this framework, and so the medial dorsal nucleus is mostly higher order,


while the ventral anterior and ventral lateral nuclei seem to be a mosaic of first and higher order


relays. It now seems clear that most of thalamus is comprised of higher order relays. Higher


order relays seem especially important to general corticocortical communication, and this


challenges and extends the conventional view that such communication is based on direct


corticocortical connections. Thus the thalamus is not just a simple relay responsible for getting


peripheral information to cortex: instead it both provides a behaviorally relevant, dynamic


control over the nature of information relayed, and it also plays a key role in basic corticocortical


communication

Feedback inhibition and throughput properties of network models of retinogeniculate transmission

Computational modeling has played an important role in the dissection of the biophysical basis of rhythmic oscillations in thalamus that are associated with sleep and certain forms of epilepsy. In contrast, the dynamic filter properties of thalamic relay nuclei during states of arousal are not well understood. Here we present two modeling studies of the throughput properties of the visually driven dorsal lateral geniculate nucleus (dLGN) in the presence of feedback inhibition from the perigeniculate nucleus (PGN). In the first study we employ thalamocortical (TC) and thalamic reticular (RE) versions of a minimal integrate-and-fire-or-burst type model and a one-dimensional, two-layered network architecture. Potassium leakage conductances control the neuromodulatory state of the network and eliminate rhythmic bursting in the presence of spontaneous input (i.e., wake up the network). The aroused dLGN/PGN network model is subsequently stimulated by spatially homogeneous spontaneous retinal input or spatio-temporally patterned input consistent with the activity of X-type retinal ganglion cells during full-field or drifting grating visual stimulation. The throughput properties of this visually-driven dLGN/PGN network model are characterized and quantified as a function of stimulus parameters such as contrast, temporal frequency, and spatial frequency. In the second study we show how all-to-all coupled TC-RE networks can be described by a multivariate probability density function that satisfies a conservation equation with appropriately defined probability fluxes and boundary conditions. Consistent with the IFB model, the independent variables of these densities are the membrane potential of both cell types and the inactivation gating variable of the low-threshold Ca current IT. The synaptic coupling of the populations and external excitatory drive are modeled by instantaneous jumps in the membrane potential of postsynaptic neurons. When the aroused population density model is stimulated with constant retinal input (10-100 spikes/sec), a wide range of responses are observed depending on cellular parameters and network connectivity. These include asynchronous burst and tonic spikes, sleep spindle-like rhythmic bursting, and oscillations in population firing rate that are distinguishable from sleep spindles due to their amplitude, frequency, or the presence of tonic spikes. Supported by NSF grants IBN 0228273, MCB 0133132, and DMS/NIGMS 0443843.

Plasticity and Dynamics of V1 Networks

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Feedforward and feedback contributions to visual processing in the lateral geniculate nucleus

At the heart of the retinogeniculocortical pathway are neurons in the lateral geniculate nucleus (LGN) of the thalamus. LGN neurons receive feedforward input from retinal ganglion cells and feedback input from the primary visual cortex. Because LGN neurons are strongly driven by the retina and have receptive fields much like those of their retinal afferents, the LGN is generally regarded as a structure that simply relays retinal activity without doing much in terms of visual processing. Closer examination, however, reveals a more complex picture of LGN function, as LGN neurons can dynamically filter their retinal input. Results will be presented from experiments in anesthetized and alert animals that examined the role of feedforward and feedback pathways in the spike transfer, surround suppression and attentional modulation of LGN neurons.

Neurophysiological Experiments on Figure-Ground Organization and Selective Visual Attention

A number of phenomena in visual perception can be related to the assignment of 'border ownership', a hypothetical process of detecting contours of objects and assigning them to the corresponding image regions. Border ownership assignment relates to the interpretation of the image in terms of a 3D layout of objects and the perception of shadows and transparent overlay; it affects recognition of form and deployment of attention; figures draw attention, while shapes of the ground tend to be ignored. In this lecture I will review evidence for border ownership coding in the visual cortex of macaques and discuss recent neurophysiological experiments on the relationship between figure-ground organization and attention. These new results show that border ownership coding and voluntary (top-down) attention influences are combined in single neurons of area V2. Border ownership coding was found for attended as well as for ignored figures, indicating that figure-ground organization occurs preattentively, and in parallel across the image. Tests with two overlapping figures revealed spatial asymmetry of the attention effect relative to the receptive field of the individual neuron. This asymmetry was correlated with border ownership preference, indicating that border ownership and attention mechanisms share critical neural circuitry. A possible interpretation is that the neural network that creates figure-ground organization also provides the interface for the top-down selection process. Thus, the phenomena of figure-ground organization may reflect the general process of recoding a visual image representation into a more efficient data structure that enables further, object-based processing.