Workshop 6: Sensory Systems and Coding

(May 6,2013 - May 10,2013 )

Organizers


Brent Doiron
Mathematics, University of Pittsburgh
Adrienne Fairhall
Physiology and Biophysics, University of Washington School of Medicine
David Kleinfeld
Physics Department, University of California, San Diego
John Rinzel
Center for Neural Science & Courant Institute, New York University

Mathematical analysis and modeling have played influential roles in the current and classical descriptions of sensory processing, object identification and representation. The bases for these descriptions have involved the properties of feedforward interactions, receptive-fields, and firing rates or spike counts and stimuli have typically been static in time and stereotypical (oriented bars, pure tones, ...). Successes include Hubel and Weisel (1981 Nobel Prize shared with Roger Sperry) and Barlow (Swartz Prize for Computational Neuroscience, 2009). There is a growing awareness that processing is not passive but active (e.g., Kleinfeld, Bower) that involves dynamic feedback loops and recurrent processing and that feedback may extend down to the sensory receptor level. This workshop will address the evolving research area of active sensory processing, such as the top-down responsive control of whiskers in the rat somatosensory system, and the mathematical modeling of these feedback systems and the principles and optimizations that might pertain. The notion of static receptive fields as described in over-idealized and restricted stimulus sets in laboratory settings is also under challenge when one considers that in real-world settings the scenes are much more complex and they are dynamic, constantly changing. A statistical framework for natural scene analysis seems much more appropriate. The workshop will consider the approaches of statistical representation of scenes and their possible realization in the brain. Futhermore, sensory systems are capable of rapid adaptation to scene dynamics, including the statistics of changing scenes, and models for such are under development (Fairhall, Riecke). So, what does the brain do with the processed sensory input? What scene aspects/cues are used in object identification and segregation; what commonalities group different individuals together; how do we categorize objects? Modeling challenges are presented by these questions and some will be addressed during the workshop. An interesting paradigm arises in the context of ambiguous scenes, such as the Necker cube or the face-vase image, in which multiple interpretations are perceived alternately. The dynamics of such alternations are stochastic and the differential equation models typically involve competition through mutual inhibition amongst the model neural subpopulations that are hypothesized to represent the two or more percepts. In the auditory context there are dynamic ambiguous stimuli that introduce another temporal layer and raise issues of what cues are used to define and track an auditory object through time. Issues that arise in the neural representations of scenes lead naturally to neural coding. What language/means do neuronal systems use internally to encode the features of an image? These questions are usually addressed from an information theory point of view. In which context is the temporal patterning of spike trains significant or is the mean firing adequate to carry the information? How do cell ensembles mutually represent features, i.e., what is the population code? Perceptions must be developed on the fly. Given some sensory tuning properties how might the parameters be chosen amongst cells to give the most efficient and rapid population code? Throughout the workshop we will ask about plausible mechanistic models that can implement the notions of active processing, coding strategies, adaptation features, and so on.

Accepted Speakers

Stephen Baccus
Neurobiology, Stanford University
Daniel Butts
Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland
Kathleen Cullen
Physiology, McGill University
Bernhard Englitz
Cognitice Science, University
Fabrizio Gabbiani
Neuroscience, Baylor College of Medicine
Surya Ganguli
Department of Applied Physics, Stanford University
Christopher Harvey
Neurobiology, Harvard Medical School
Steven Hsiao
Neuroscience, Johns Hopkins University
Stephanie Jones
Neuroscience, Brown University
Kresimir Josic
Department of Mathematics, University of Houston
David Kleinfeld
Physics Department, University of California, San Diego
Adam Kohn
Neuroscience, Albert Einstein College of Medicine
Nathan Kutz
Applied Mathematics, University of Washington
Anitha Pasupathy
Jonathan Pillow
Psychology & Neurobiology, University of Texas
Alex Reyes
Center for Neural Science, New York University
Dima Rinberg
Physiology and Neuroscience, NYU Neuroscience Institute
Elad Schneidman
Department of Neurobiology, Weizmann Institute of Science
Charles Schroeder
Psychiatry, Columbia University
Tatyana Sharpee
Computational Neurobiology Laboratory, The Salk Institute for Biological Studies
Garrett Stanley
Department of Biomedical Engineering, Georgia Institute of Technology
Andreas Tolias
Neuroscience, Baylor College of Medicine
Jing Wang
Division of Biological Sciences, University of California, San Diego
Monday, May 6, 2013
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
08:45 AM

Breakfast

08:45 AM
09:00 AM

Welcome and Introductions: Marty Golubitsky

09:00 AM
09:00 AM
Adrienne Fairhall - Excitability in neural coding: information processing in neurons and networks
Excitability in neural coding: information processing in neurons and networks
09:00 AM
09:00 AM
Brent Doiron - Sensory Systems and Coding Workshop Introduction
Sensory Systems and Coding Workshop Introduction
09:00 AM
09:50 AM
Garrett Stanley
09:50 AM
10:10 AM

Break

10:10 AM
11:00 AM
David Kleinfeld - Active spatial perception in the vibrissa scanning sensorimotor system

How do we know where objects are relative our body? How do we use touch information to plan the next motor act? I will discuss experimental results that address these and related issues in active sensation, using the rodent vibrissa sensorimotor system as a model.

11:00 AM
11:50 AM
Jonathan Pillow - Optimality and neural codes: Bayesian inference meets Barlow's efficient coding hypothesis

Barlow's "efficient coding hypothesis" asserts that neurons should maximize the information they convey about stimuli. This idea has provided a guiding theoretical framework for the study of coding in neural systems, and has sparked a great many studies of decorrelation and efficiency in early sensory areas. A more recent theory, the "Bayesian brain hypothesis", asserts that neural responses encode posterior distributions in order to support Bayesian inference.


However, these two theories have not yet been formally connected. In this talk, I will introduce a Bayesian theory of efficient coding, which has Barlow's framework as a special case. I will argue that there is nothing privileged about information-maximizing codes: they are ideal when one wishes minimize entropy, but they can be substantially suboptimal in other cases. Moreover, codes optimized for information transfer may differ strongly from codes optimized for other loss functions. Bayesian efficient coding substantially enlarges the family of normatively optimal codes and provides a general framework for understanding the principles of sensory encoding. I will derive Bayesian efficient codes for a few simple examples and show an application to neural data.

11:50 AM
01:10 PM

Lunch Break

01:10 PM
02:00 PM
Stephen Baccus - Principles of Biological Design
Principles of Biological Design
02:00 PM
02:10 PM

Flashes(two talks, five minutes each)

02:10 PM
02:10 PM
Stephanie Palmer - Sensory prediction in the natural world
Sensory prediction in the natural world
02:10 PM
02:40 PM

Break

02:40 PM
03:30 PM
Fabrizio Gabbiani - Neural Information Processing Underlying Collision Avoidance Behaviors

Understanding how the brain processes sensory information in real-time to generate meaningful behaviors is one of the outstanding contemporary challenges of neuroscience. Visually guided collision avoidance behaviors are nearly universal in animals endowed with spatial vision and offer a favorable opportunity to address this question. This talk will summarize the current understanding of their generation at the level of neural networks, single neurons and their ion channels. The focus will be on a model system that has proven particularly suitable for this purpose, the locust brain, but will also tie the results learned in this preparation to studies carried out in a wide range of other species.

03:30 PM
03:40 PM

Flashes(two talks, five minutes)

03:40 PM
05:10 PM

Reception and poster session in MBI Lounge

03:40 PM
03:40 PM
Jason Ritt - Control strategies for underactuated neural ensembles
Control strategies for underactuated neural ensembles.
05:25 PM

Shuttle pick-up from MBI

Tuesday, May 7, 2013
Time Session
04:20 AM

Shuttle to MBI

04:20 AM
04:20 AM
Vincent Billock - Forbidden colors and hidden aspects of perceptual opponencies
Opponent processing is one of the oldest and best established principles in sensory neuroscience, but there are still surprises to be found in this area. Color opponency is one of the best established facts in perception, but I found a reliable way to make it break down by retinally stabilizing equiluminous red/green or blue/yellow bipartite fields. The border perceptually melts away and the colors flow and mix into one another, creating forbidden colors in a variety of multistable percepts (Scientific American, 2010). Making the colors equiluminant is crucial; if the luminances of the retinally-stabilized colors are not properly equated, subjects see multistable color switching or hallucinatory colored textures instead. The results can be understood if color opponency is softwired, like a winner-take-all network, with interactions that are disabled under the same conditions that disable perceptual binding (TINS, 2004). In addition to disabling a perceptual opponency, it is also possible to find hidden opponencies in spatial vision. Flicker-induced hallucinations are normally chaotic, but we found ways to bias and stabilize these hallucinations. Interestingly, this unveils a geometric opponency: concentric circular geometries bias photopic hallucinations to illusory fan-shapes and vice versa; and similarly for clockwise and counter-clockwise spirals (PNAS, 2007; Psychological Bulletin, 2012). These phenomena obey a variety of familiar perceptual principles. Forbidden colors and biased hallucinations are examples of ordinary neural mechanisms stimulated in extraordinary ways.
04:35 AM
05:05 AM

Breakfast

05:05 AM
05:55 AM
Tatyana Sharpee
05:55 AM
06:45 AM
Ben Strowbridge
06:45 AM
07:05 AM

Break

07:05 AM
07:55 AM
Adam Kohn - Coordinated neuronal activity and its role in corticocortical signaling

Spiking activity in cortex is coordinated on a range of spatial and temporal scales. Numerous studies have shown that external events and internal states can alter this coordination, and suggested that this affects encoding by neuronal populations. Much less explored is how coordinated activity influences the relaying of signals between cortical areas and the computations they perform. To tackle this issue, we recorded simultaneously from populations of neurons in the superficial layers of primary visual cortex (V1) of macaque monkeys, and from their downstream targets in the middle layers of V2. We find that spiking activity in V2 neurons is associated with a brief increase in V1 spiking correlations. Stimulus manipulations that enhance brief timescale V1 synchrony lead to stronger coupling between these networks. Our results suggest that the coordination of spiking activity within a cortical area influences its coupling with downstream areas.

07:55 AM
08:45 AM
Lai-Sang Young - Emergent dynamics in a model of visual cortex

I will report on recent work which proposes that the network dynamics of the mammalian visual cortex are neither homogeneous nor synchronous but highly structured and strongly shaped by temporally localized barrages of excitatory and inhibitory firing we call `multiple-firing events' (MFEs).

Our proposal is based on careful study of a network of spiking neurons built to reflect the coarse physiology of a small patch of layer 2/3 of V1.

When appropriately benchmarked this network is capable of reproducing the qualitative features of a range of phenomena observed in the real visual cortex, including orientation tuning, spontaneous background patterns, surround suppression and gamma-band oscillations. Detailed investigation into the relevant regimes reveals causal relationships among dynamical events driven by a strong competition between the excitatory and inhibitory populations. Testable predictions are proposed; challenges for mathematical neuroscience will also be discussed. This is joint work with Aaditya Rangan.

08:45 AM
10:05 AM

Lunch Break

10:05 AM
10:55 AM
Nathan Kutz - Spatiotemporal encoding/decoding of nonlinear dynamics with compressive sensing: neuro-sensory encoding in moth olfaction and flight

Neuro-sensory systems encode their functionality into persistent spatio-temporal patterns of neuron activity, or so-called neural codes. Networks of neurons in the antennal lobe (AL) of moths form non-local neural codes that compete dynamically with each other through lateral inhibition, thus producing a robust signal-processing unit that increases signal-to-noise and enhances the contrast between neural codes. More broadly, many high-dimensional complex systems often exhibit dynamics that evolve on a slow-manifold and/or a low-dimensional attractor. Thus we propose a data-driven modeling strategy that encodes/decodes the dynamical evolution using compressive (sparse) sensing (CS) in conjunction with machine learning (ML) strategies for constructing the observed low-dimensional manifolds. The integration of ML and CS techniques also provide an ideal basis for applying control algorithms to the underlying dynamical systems, thus revealing a method of how robust flight control, for instance, can be accomplished.

10:55 AM
11:05 AM

Flashes (two talks, five minutes each)

11:05 AM
11:05 AM
Alla Borisyuk - Alla Borisyuk's Flash talk at the Sensory Systems and Coding Workshop
Alla Borisyuk's Flash talk at the Sensory Systems and Coding Workshop
11:05 AM
11:05 AM
Heather Read - Who's got rhythm?, Envelop Temporal Coding in Primary and Non-primary Cortices
Who's got rhythm?, Envelop Temporal Coding in Primary and Non-primary Cortices
11:05 AM
11:35 AM

Break

11:35 AM
12:25 PM
Bernhard Englitz - Cortical adaptation predicts perception during an auditory task - with a twist

Perception is dependent on context, but whether and how sensory areas encode the context is debated. We used a bistable auditory stimulus - a tritone pair - to investigate the trace left by a preceding bias sequence, which reliably switches the tritone pair’s perception between an ascending and descending step in pitch.

We find the bias sequence to induce localized adaptation in neural recordings from the auditory cortex of ferrets. Human MEG recordings show that this adaptation is present and sustained over several seconds under behavioral conditions as well. Sustained adaptation thus appears to encode a memory-like trace of the stimulus history. Using a neural population decoder we show that a classical pitch-difference estimator cannot account for the percept, since the local adaptation leads to an opposite prediction. Instead, we propose a decoder based on a differential adaptation of local pitch-direction selective cells, which correctly predicts the percept.

These results suggest that the stimulus context may be encoded by sustaining the adapted, negative afterimage of the preceding stimulus, and that this mechanism may generate global pitch-direction judgements from local pitch-direction selectivity.

12:25 PM
12:35 PM

Flashes (two talks, five minutes each)

12:35 PM
01:35 PM

Group Discussion

01:50 PM

Shuttle pick-up from MBI

Wednesday, May 8, 2013
Time Session
08:15 AM

Shuttle to MBI

08:30 AM
09:00 AM

Breakfast

09:00 AM
09:50 AM
Christopher Harvey - Neuronal circuit dynamics in the mouse parietal cortex during virtual navigation

The posterior parietal cortex (PPC) has an important role in many cognitive behaviors; however, the neuronal circuit dynamics underlying PPC function are not well understood. We have studied circuit activity dynamics in the PPC of mice during navigation-based choice tasks using a combination of a virtual reality system and two-photon microscopy. We find that during working memory tasks the PPC activity dynamics are best characterized as choice-specific sequences of neuronal activation, rather than long-lived stable states, implemented using anatomically intermingled microcircuits. I will also discuss on-going work to test if sequence-based circuit dynamics may underlie computations necessary for decision-making.

09:50 AM
10:40 AM
Anitha Pasupathy
10:40 AM
11:00 AM

Break

11:00 AM
11:50 AM
Jing Wang - Perceptual saliency by integrating olfactory context and feature

Sensory systems must extract important information from background noise in a constantly changing environment. For example, blinking or moving objects in a static background are more likely to attract attention. The odor landscape, like the visual world, is also highly cluttered and noisy. Odor information is parsed out by sensory neurons expressing different odorant receptors into glomerular activity. How does olfactory information about one object become more behaviorally salient than other odors in the environment? How does odor context influence the saliency of certain olfactory features? How does internal physiological state such as hunger and satiety modulate olfactory circuit to generate flexible behavioral response? I will present our recent unpublished data from the fruit fly Drosophila to support the idea that the mushroom body, a higher olfactory center, is important for perceptual saliency. Food odor and conspecific social cue, represented by separate glomeruli in the antennal lobe, are integrated in the mushroom to enhance behavioral attraction to food. Hunger-dependent neuropeptide signal modulates neural activity in the mushroom body to control the intensity of foraging behavior in Drosophila.

11:50 AM
12:40 PM
Elad Schneidman - Words, metrics, and a thesaurus for a neural population code.
Words, metrics, and a thesaurus for a neural population code.
12:40 PM
02:00 PM

Lunch (Pizza provided by MBI)

02:00 PM
02:50 PM
Surya Ganguli
02:50 PM
03:00 PM

Flashes (two talks, five minutes each)

03:00 PM
03:00 PM
Andrea Barreiro - Mechanisms for higher-order correlations in microcircuits
Mechanisms for higher-order correlations in microcircuits.
03:00 PM
03:00 PM
Tatjana Tchumatchenko - Recurrent Connectivity in Single Neuron Dynamics
Recurrent Connectivity in Single Neuron Dynamics.
03:00 PM
03:30 PM

Break

03:30 PM
04:20 PM
Kresimir Josic - Measuring and interpreting correlated neuronal responses

Populations of neurons jointly drive behavior. Thus, understanding how population activity is coordinated is a key challenge. Novel recording techniques allow for the simultaneous recording from many cells revealing the joint activity of neuronal population during sensory, motor, and cognitive tasks. This has prompted widespread measurement of pairwise correlations. However, the magnitude, the interpretation, and the underlying neural mechanisms of such neural correlations are being vigorously debated. I will start by reviewing our current understanding of the biological mechanisms that control the correlation between the spiking activity of cortical neurons. In particular, I will discuss the potential pitfalls in simple mechanistic explanations of modulations in the coherence in network activity.


In the second part of the talk I will discuss the role of correlations in neural coding. I will first examine the role of coupling between the neurons of the Vertical System (VS) in the lobula plate of the fly. These 20 non-spiking neurons code for the azimuth of the axis of rotation of the fly during flight. The electrical coupling between the cells is relatively large, and the activity of VS cells is strongly correlated. I will discuss the potential role this coupling plays in the processing of optical flow information. I will end with a comment on the impact of noise correlation in models used in psychophysics.

04:20 PM
04:30 PM

Flashes (two talks, five minutes each)

04:30 PM
05:30 PM

Group Discussion

04:30 PM
04:30 PM
Mark Willis - Interactions of sensor structure, and environmental and locomotory context determine odor plume tracking behavior.
Interactions of sensor structure, and environmental and locomotory context determine odor plume tracking behavior.
04:30 PM
04:30 PM
Remus Osan - Neural population dynamics during sensory and memory processing
The size and complexity of neural data is increasing at a dramatic pace due to rapid advances in experimental technologies. As a result, the data analysis techniques are shifting their focus from single-units to neural populations. We use projection methods, such as Principal Component Analysis PCA and Multiple Discriminant Analysis MDA, to facilitate the understanding and monitoring of the dynamics of neural populations recorded in the hippocampus and olfactory bulb. For the hippocampal date, we examine representation of startle episodes, in order to differentiate between somato-sensory and memory components of the hippocampal representations. For the olfactory data, we focus on how dynamics of odor responses in the olfactory receptor neurons of awake rats are shaped by the temporal features of the active odor sniffing. Our analyses indicate that the dynamics of neural representations depend non-linearly on odor identity and concentration, as well as breathing rhythms of the rats. These results include work done with graduate students Jun Xia and Jie Zhang.
05:30 PM
05:30 PM
Wyeth Bair - iModel.org demostration
iModel.org demostration
05:45 PM

Shuttle pick-up from MBI

Thursday, May 9, 2013
Time Session
08:15 AM

Shuttle to MBI

08:30 AM
09:00 AM

Breakfast

09:00 AM
09:50 AM
Dima Rinberg
09:50 AM
10:40 AM
Charles Schroeder - Neuronal Substrates of Temporal Prediction in Active Sensing

Neuronal oscillations reflecting synchronous, rhythmic fluctuation of neuron ensembles between high and low excitability states, dominate ambient activity in the sensory pathways. Because excitability determines the probability that neurons will respond to input, a top-down process like attention can use oscillations as "instruments" to amplify or suppress the brain's representation of external events. That is, by tuning the frequency and phase of its rhythms to those of behaviorally and/or cognitively-relevant event streams, the brain can use its rhythms to parse event streams and to form internal representations of them. In doing this, the brain is making temporal predictions. I will discuss findings from parallel experiments in humans and non-human primates that outline specific structural and functional components of this temporal prediction mechanism. I will also discuss its possible generalization across temporal scales. Finally, I will discuss motor system contributions to sensory systems' dynamics.

10:40 AM
11:00 AM

Break

11:00 AM
11:50 AM
Kathleen Cullen - The neural encoding of vestibular information during natural self-motion

The vestibular system is vital for maintaining an accurate representation of our motion and orientation as we move through the world. As one moves (or is moved) toward a new place in the environment, signals from the vestibular sensors encode head direction and velocity, and are relayed through the vestibular system to premotor areas and higher-order centers. It is generally assumed the vestibular system provides a veridical representation of sensor output for the control of reflexes vital for maintaining stable gaze and balance, the perception of self-motion and orientation, as well as the generation of spatial-memory processes.

This lecture will consider progress that has recently been made towards understanding how the brain encodes and processes self-motion encoded by the vestibular otoliths and semicircular canals during every day life. First, recent findings challenge the traditional notion that the vestibular system uses a linear rate code to transmit information. Instead, nonlinear integration of afferent input extends the coding range of central vestibular neurons and enables them to better extract the high frequency features of self-motion when embedded with low frequency motion during natural movements. Next, under natural conditions, the behavioral context governs how vestibular is encoded at the first central stage of processing. Not only is vestibular (self-motion) processing inherently multimodal, but the manner in which multiple inputs are combined is adjusted to meet the needs of the current behavioral goal. Finally, consideration is given to the mechanisms that underlie these computations, and the functional significance of the information that is ultimately encoded.

11:50 AM
12:40 PM
Steven Hsiao - Tactile object representation and the mechanisms of selective attention

Tactile object recognition depends on the integration of cutaneous inputs from the skin with proprioceptive inputs from the skin and muscles and depends on the attentional state of the animal. While the cutaneous inputs provide information about the spatial form, texture and motion of stimulus patterns on the skin, the proprioceptive inputs provide information about where these inputs are located in three-dimensional space and information about whether the hand or object is moving. Object recognition then is based on matching the inputs from each of the contact points where the skin touches the object with previously stored representations of objects. There are two parts of this talk in the first, we will discuss how cutaneous features are processed in peripheral afferents, neurons in primary and secondary cortex and show how the cutaneous inputs are modified by hand conformation. In the second part of the talk we will discuss the mechanisms of feature selection by attention. Specifically we will show that when animals attend to a specific feature of a stimulus, like the orientation of a bar, neurons with similar tuning functions show increased firing rates and there is an increase in the degree if spike synchrony between neurons.

12:40 PM
02:00 PM

Lunch Break

02:00 PM
04:30 PM

Breakout Sessions

04:45 PM

Shuttle pick-up from MBI

06:30 PM
07:00 PM

Cash Bar

07:00 PM
09:00 PM

Banquet in the Fusion Room @ Crowne Plaza Hotel

Friday, May 10, 2013
Time Session
08:15 AM

Shuttle to MBI

08:30 AM
09:00 AM

Breakfast

09:00 AM
09:50 AM
Daniel Butts - Coordination of visual processing in cortex by network activity during natural viewing

In addition to visual information from thalamus, neurons in primary visual cortex (V1) receive inputs from other V1 neurons, as well as from higher cortical areas. This “non-classical� input to V1 neurons, which can be inferred in part from the local field potential, can modulate the “classical� feed-forward responses of V1 neurons to visual stimuli. Using multielectrode recordings in awake primate, we can characterize this modulation in a variety of stimulus contexts. Because this network activity is by definition shared, it can serve to coordinate single neuron responses across a given region of cortex. Such network modulation plays a clear role during natural viewing, where saccadic eye movements result in stereotyped network activity. Thus these network influences to V1 neuron activity, which likely represent both coordinated processing within V1 and top-down influences, play a fundamental role in natural visual processing.

09:50 AM
10:40 AM
Andreas Tolias
10:40 AM
11:00 AM

Break

11:00 AM
11:50 AM
Stephanie Jones - Mechanisms and functions of human neocortical rhythms in sensory perception

Low frequency neocortical rhythms are among the most prominent activity measured in human brain imaging signals such as electro- and magneto-encephalography (EEG/MEG). Elucidating the role that these dynamics play in perception, cognition and action is a key challenge of modern neuroscience. We have recently combined human brain imaging, computational neural modeling, and electrophysiological recordings in rodents to explore the functional relevance and mechanistic underpinnings of rhythms in primary somatosensory cortex (SI), containing Alpha (7-14Hz) and Beta (15-29Hz) components. In this talk, I will review our findings showing this rhythm impacts tactile detection, changes with healthy aging and practice, and is modulated with attention. Constrained by the human imaging data, our biophysically principled computational modeling work has led to a novel prediction on the origin of this rhythm predicting that it emerges from the combination of two stochastic ~10 Hz thalamic drives to the granular/infragranular and supragranular cortical layers. Relative Alpha/Beta expression depends on the strength and delay between the thalamic drives. This model is able to accurately reproduce numerous key features of the human rhythm and proposes a specific mechanistic link between the Beta component of the rhythm and sensory perception. Further, initial electrophysiological recordings in rodents support out hypotheses and suggest a role for non-lemniscal pallidal thalamus in coordinating Beta rhythmicity, with relevance to understanding disrupt Beta in Parkinson's Disease.

11:50 AM
12:40 PM
Alex Reyes
12:40 PM

One Shuttle back to hotel, One Shuttle to Columbus Airport

Name Affiliation
Amro, Rami ra150909@ohio.edu Physics and Astronomy, Ohio University
Baccus, Stephen baccus@stanford.edu Neurobiology, Stanford University
Bair, Wyeth wyeth0@u.washington.edu Biological Structure, University of Washington
Barreiro, Andrea abarreiro@smu.edu Mathematics, Southern Methodist University
Bijanzadeh, Maryam ma.bijanzadeh@gmail.com Ophthalmology, University of Utah
Billock, Vincent vincent.billock.ctr@wpafb.af.mil National Research Council, U.S. Air Force Research Laboratory
Borisyuk, Alla borisyuk@math.utah.edu Mathematics, University of Utah
Butts, Daniel dab@umd.edu Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland
Chandrasekaran, Lakshmi lac@stowers.org Yu Lab, Stowers Institute for Medical Research
Chariker, Christopher clc450@nyu.edu Mathematics, NYU
Cullen, Kathleen kathleen.cullen@mcgill.ca Physiology, McGill University
Doiron, Brent bdoiron@pitt.edu Mathematics, University of Pittsburgh
Englitz, Bernhard benglitz@gmail.com Cognitice Science, University
Fairhall, Adrienne fairhall@uw.edu Physiology and Biophysics, University of Washington School of Medicine
Gabbiani, Fabrizio gabbiani@bcm.edu Neuroscience, Baylor College of Medicine
Ganguli, Surya sganguli@stanford.edu Department of Applied Physics, Stanford University
Ghezzi, Katherine Katherine.Ghezzi@birkhauser-science.com Editorial, Springer Science+Business Media
Golomb, David golomb@bgumail.bgu.ac.il Department of Physics, University of California, San Diego
Harvey, Christopher Christopher_Harvey@hms.harvard.edu Neurobiology, Harvard Medical School
Hsiao, Steven Steven.Hsiao@jhu.edu Neuroscience, Johns Hopkins University
Hu, Yu huyu@uw.edu Applied Mathematics, University of Washington
Huang, Chengcheng chengchenghuang11@gmail.com Courant Institute of Mathematical Sciences, New York University
Huguet, Gemma gemma.huguet@upc.edu Courant Institute for Mathematical Sciences, Courant Institute for Mathematical Sciences
Jones, Stephanie Stephanie_Jones@brown.edu Neuroscience, Brown University
Josic, Kresimir josic@math.uh.edu Department of Mathematics, University of Houston
Kleinfeld, David dk@physics.ucsd.edu Physics Department, University of California, San Diego
Kohn, Adam adam.kohn@einstein.yu.edu Neuroscience, Albert Einstein College of Medicine
Kumar, Ashok alk@cmu.edu Center for the Neural Basis of Cognition, Carnegie-Mellon University
Kutz, Nathan kutz@amath.washington.edu Applied Mathematics, University of Washington
Liu, Jian jiankliu@gmail.com Department of Ophthalmology, University Medical Center Goettingen
Maheswaranathan, Niru nirum@stanford.edu Neurosciences Program, Stanford University
Meng, Xiangying (Linda) lindamengxy@gmail.com Biology, University of Maryland, College Park
Osan, Remus rosan@gsu.edu Mathematics and Statistics, Georgia State University
Palmer, Stephanie sepalmer@uchicago.edu Organismal Biology and Anatomy, University of Chicago
Pasupathy, Anitha pasupat@u.washington.edu
Patel, Mainak mainak@math.duke.edu Mathematics, Duke University
Pillow, Jonathan pillow@mail.utexas.edu Psychology & Neurobiology, University of Texas
Read, Heather heather.read@uconn.edu Depts. of Psychology and Biomedical Engineering, University of Connecticut
Reyes, Alex reyes@cns.nyu.edu Center for Neural Science, New York University
Riecke, Hermann h-riecke@northwestern.edu Engineering Sciences and Applied Mathematics, Northwestern University
Rinberg, Dmitry Dmitry.Rinberg@nyumc.org Physiology and Neuroscience, NYU Neuroscience Institute
Rinzel, John rinzel@cns.nyu.edu Center for Neural Science & Courant Institute, New York University
Ritt, Jason jritt@bu.edu Biomedical Engineering, Boston University
Sauer, Tim tsauer@gmu.edu Department of Mathematics, George Mason University
Schiff, Steven sjs49@engr.psu.edu Depts. Neurosurgery / Eng Science & Mechanics / Physics, Pennsylvania State University
Schneidman, Elad elad.schneidman@weizmann.ac.il Department of Neurobiology, Weizmann Institute of Science
Schroeder, Charles schrod@nki.rfmh.org Psychiatry, Columbia University
Sharpee, Tatyana sharpee@salk.edu Computational Neurobiology Laboratory, The Salk Institute for Biological Studies
Shen, Jianming shen.419@osu.edu Speech & Hearing Science, The Ohio State University
Stanley, Garrett garrett.stanley@bme.gatech.edu Department of Biomedical Engineering, Georgia Institute of Technology
Strowbridge, Ben bens@case.edu Neurosciences, Case Western Reserve University
Tchumatchenko, Tatjana tatjana@nld.ds.mpg.de Independent Reseach Group, MPI for Brain Reseach
Tolias, Andreas astolias@bcm.edu Neuroscience, Baylor College of Medicine
Travers, Joe travers.1@osu.edu Neuroscience, The Ohio State University
Wang, Jing jw800@ucsd.edu Division of Biological Sciences, University of California, San Diego
Willis, Mark mark.willis@case.edu Biology, Case Western Reserve University
Young , Lai-Sang lsy@courant.nyu.edu Courant Institute of Mathematical Sciences, New York University
Youngs, Nora s-nyoungs1@math.unl.edu Mathematics, University of Nebraska
Zhou, Weijuan zhouwj@msu.edu Mathmatics , Michigan State University
Zylberberg, Joel joelzy@uw.edu Applied Mathematics, University of Washington
Principles of Biological Design
Principles of Biological Design
iModel.org demostration
iModel.org demostration
Mechanisms for higher-order correlations in microcircuits
Mechanisms for higher-order correlations in microcircuits.
Forbidden colors and hidden aspects of perceptual opponencies
Opponent processing is one of the oldest and best established principles in sensory neuroscience, but there are still surprises to be found in this area. Color opponency is one of the best established facts in perception, but I found a reliable way to make it break down by retinally stabilizing equiluminous red/green or blue/yellow bipartite fields. The border perceptually melts away and the colors flow and mix into one another, creating forbidden colors in a variety of multistable percepts (Scientific American, 2010). Making the colors equiluminant is crucial; if the luminances of the retinally-stabilized colors are not properly equated, subjects see multistable color switching or hallucinatory colored textures instead. The results can be understood if color opponency is softwired, like a winner-take-all network, with interactions that are disabled under the same conditions that disable perceptual binding (TINS, 2004). In addition to disabling a perceptual opponency, it is also possible to find hidden opponencies in spatial vision. Flicker-induced hallucinations are normally chaotic, but we found ways to bias and stabilize these hallucinations. Interestingly, this unveils a geometric opponency: concentric circular geometries bias photopic hallucinations to illusory fan-shapes and vice versa; and similarly for clockwise and counter-clockwise spirals (PNAS, 2007; Psychological Bulletin, 2012). These phenomena obey a variety of familiar perceptual principles. Forbidden colors and biased hallucinations are examples of ordinary neural mechanisms stimulated in extraordinary ways.
Alla Borisyuk's Flash talk at the Sensory Systems and Coding Workshop
Alla Borisyuk's Flash talk at the Sensory Systems and Coding Workshop
Coordination of visual processing in cortex by network activity during natural viewing

In addition to visual information from thalamus, neurons in primary visual cortex (V1) receive inputs from other V1 neurons, as well as from higher cortical areas. This “non-classical� input to V1 neurons, which can be inferred in part from the local field potential, can modulate the “classical� feed-forward responses of V1 neurons to visual stimuli. Using multielectrode recordings in awake primate, we can characterize this modulation in a variety of stimulus contexts. Because this network activity is by definition shared, it can serve to coordinate single neuron responses across a given region of cortex. Such network modulation plays a clear role during natural viewing, where saccadic eye movements result in stereotyped network activity. Thus these network influences to V1 neuron activity, which likely represent both coordinated processing within V1 and top-down influences, play a fundamental role in natural visual processing.

The neural encoding of vestibular information during natural self-motion

The vestibular system is vital for maintaining an accurate representation of our motion and orientation as we move through the world. As one moves (or is moved) toward a new place in the environment, signals from the vestibular sensors encode head direction and velocity, and are relayed through the vestibular system to premotor areas and higher-order centers. It is generally assumed the vestibular system provides a veridical representation of sensor output for the control of reflexes vital for maintaining stable gaze and balance, the perception of self-motion and orientation, as well as the generation of spatial-memory processes.

This lecture will consider progress that has recently been made towards understanding how the brain encodes and processes self-motion encoded by the vestibular otoliths and semicircular canals during every day life. First, recent findings challenge the traditional notion that the vestibular system uses a linear rate code to transmit information. Instead, nonlinear integration of afferent input extends the coding range of central vestibular neurons and enables them to better extract the high frequency features of self-motion when embedded with low frequency motion during natural movements. Next, under natural conditions, the behavioral context governs how vestibular is encoded at the first central stage of processing. Not only is vestibular (self-motion) processing inherently multimodal, but the manner in which multiple inputs are combined is adjusted to meet the needs of the current behavioral goal. Finally, consideration is given to the mechanisms that underlie these computations, and the functional significance of the information that is ultimately encoded.

Sensory Systems and Coding Workshop Introduction
Sensory Systems and Coding Workshop Introduction
Cortical adaptation predicts perception during an auditory task - with a twist

Perception is dependent on context, but whether and how sensory areas encode the context is debated. We used a bistable auditory stimulus - a tritone pair - to investigate the trace left by a preceding bias sequence, which reliably switches the tritone pair’s perception between an ascending and descending step in pitch.

We find the bias sequence to induce localized adaptation in neural recordings from the auditory cortex of ferrets. Human MEG recordings show that this adaptation is present and sustained over several seconds under behavioral conditions as well. Sustained adaptation thus appears to encode a memory-like trace of the stimulus history. Using a neural population decoder we show that a classical pitch-difference estimator cannot account for the percept, since the local adaptation leads to an opposite prediction. Instead, we propose a decoder based on a differential adaptation of local pitch-direction selective cells, which correctly predicts the percept.

These results suggest that the stimulus context may be encoded by sustaining the adapted, negative afterimage of the preceding stimulus, and that this mechanism may generate global pitch-direction judgements from local pitch-direction selectivity.

Excitability in neural coding: information processing in neurons and networks
Excitability in neural coding: information processing in neurons and networks
Neural Information Processing Underlying Collision Avoidance Behaviors

Understanding how the brain processes sensory information in real-time to generate meaningful behaviors is one of the outstanding contemporary challenges of neuroscience. Visually guided collision avoidance behaviors are nearly universal in animals endowed with spatial vision and offer a favorable opportunity to address this question. This talk will summarize the current understanding of their generation at the level of neural networks, single neurons and their ion channels. The focus will be on a model system that has proven particularly suitable for this purpose, the locust brain, but will also tie the results learned in this preparation to studies carried out in a wide range of other species.

Whisker shape changes induced by touch
We study why whiskers of land mammals are approximately conical by considering a tapered whisker under contact with an object. We convert the Euler-Bernoulli quasi-static equation into a boundary-value equation and analyze it using dynamical system theory. The equation has two solutions, one stable and one unstable, that coalesce in a saddle-node bifurcation. Beyond the bifurcation, the whisker slips-off. Slip-off does not occur for cylindrical hairs for realistic parameters. We suggest that slip-off events code radial distances of objects far from the whisker base. Experimental results show that conical whiskers can sweep pass textures in a series of stick-slip events, but cylindrical hairs are stuck.
Neuronal circuit dynamics in the mouse parietal cortex during virtual navigation

The posterior parietal cortex (PPC) has an important role in many cognitive behaviors; however, the neuronal circuit dynamics underlying PPC function are not well understood. We have studied circuit activity dynamics in the PPC of mice during navigation-based choice tasks using a combination of a virtual reality system and two-photon microscopy. We find that during working memory tasks the PPC activity dynamics are best characterized as choice-specific sequences of neuronal activation, rather than long-lived stable states, implemented using anatomically intermingled microcircuits. I will also discuss on-going work to test if sequence-based circuit dynamics may underlie computations necessary for decision-making.

Tactile object representation and the mechanisms of selective attention

Tactile object recognition depends on the integration of cutaneous inputs from the skin with proprioceptive inputs from the skin and muscles and depends on the attentional state of the animal. While the cutaneous inputs provide information about the spatial form, texture and motion of stimulus patterns on the skin, the proprioceptive inputs provide information about where these inputs are located in three-dimensional space and information about whether the hand or object is moving. Object recognition then is based on matching the inputs from each of the contact points where the skin touches the object with previously stored representations of objects. There are two parts of this talk in the first, we will discuss how cutaneous features are processed in peripheral afferents, neurons in primary and secondary cortex and show how the cutaneous inputs are modified by hand conformation. In the second part of the talk we will discuss the mechanisms of feature selection by attention. Specifically we will show that when animals attend to a specific feature of a stimulus, like the orientation of a bar, neurons with similar tuning functions show increased firing rates and there is an increase in the degree if spike synchrony between neurons.

Mechanisms and functions of human neocortical rhythms in sensory perception

Low frequency neocortical rhythms are among the most prominent activity measured in human brain imaging signals such as electro- and magneto-encephalography (EEG/MEG). Elucidating the role that these dynamics play in perception, cognition and action is a key challenge of modern neuroscience. We have recently combined human brain imaging, computational neural modeling, and electrophysiological recordings in rodents to explore the functional relevance and mechanistic underpinnings of rhythms in primary somatosensory cortex (SI), containing Alpha (7-14Hz) and Beta (15-29Hz) components. In this talk, I will review our findings showing this rhythm impacts tactile detection, changes with healthy aging and practice, and is modulated with attention. Constrained by the human imaging data, our biophysically principled computational modeling work has led to a novel prediction on the origin of this rhythm predicting that it emerges from the combination of two stochastic ~10 Hz thalamic drives to the granular/infragranular and supragranular cortical layers. Relative Alpha/Beta expression depends on the strength and delay between the thalamic drives. This model is able to accurately reproduce numerous key features of the human rhythm and proposes a specific mechanistic link between the Beta component of the rhythm and sensory perception. Further, initial electrophysiological recordings in rodents support out hypotheses and suggest a role for non-lemniscal pallidal thalamus in coordinating Beta rhythmicity, with relevance to understanding disrupt Beta in Parkinson's Disease.

Measuring and interpreting correlated neuronal responses

Populations of neurons jointly drive behavior. Thus, understanding how population activity is coordinated is a key challenge. Novel recording techniques allow for the simultaneous recording from many cells revealing the joint activity of neuronal population during sensory, motor, and cognitive tasks. This has prompted widespread measurement of pairwise correlations. However, the magnitude, the interpretation, and the underlying neural mechanisms of such neural correlations are being vigorously debated. I will start by reviewing our current understanding of the biological mechanisms that control the correlation between the spiking activity of cortical neurons. In particular, I will discuss the potential pitfalls in simple mechanistic explanations of modulations in the coherence in network activity.


In the second part of the talk I will discuss the role of correlations in neural coding. I will first examine the role of coupling between the neurons of the Vertical System (VS) in the lobula plate of the fly. These 20 non-spiking neurons code for the azimuth of the axis of rotation of the fly during flight. The electrical coupling between the cells is relatively large, and the activity of VS cells is strongly correlated. I will discuss the potential role this coupling plays in the processing of optical flow information. I will end with a comment on the impact of noise correlation in models used in psychophysics.

Active spatial perception in the vibrissa scanning sensorimotor system

How do we know where objects are relative our body? How do we use touch information to plan the next motor act? I will discuss experimental results that address these and related issues in active sensation, using the rodent vibrissa sensorimotor system as a model.

Coordinated neuronal activity and its role in corticocortical signaling

Spiking activity in cortex is coordinated on a range of spatial and temporal scales. Numerous studies have shown that external events and internal states can alter this coordination, and suggested that this affects encoding by neuronal populations. Much less explored is how coordinated activity influences the relaying of signals between cortical areas and the computations they perform. To tackle this issue, we recorded simultaneously from populations of neurons in the superficial layers of primary visual cortex (V1) of macaque monkeys, and from their downstream targets in the middle layers of V2. We find that spiking activity in V2 neurons is associated with a brief increase in V1 spiking correlations. Stimulus manipulations that enhance brief timescale V1 synchrony lead to stronger coupling between these networks. Our results suggest that the coordination of spiking activity within a cortical area influences its coupling with downstream areas.

Spatiotemporal encoding/decoding of nonlinear dynamics with compressive sensing: neuro-sensory encoding in moth olfaction and flight

Neuro-sensory systems encode their functionality into persistent spatio-temporal patterns of neuron activity, or so-called neural codes. Networks of neurons in the antennal lobe (AL) of moths form non-local neural codes that compete dynamically with each other through lateral inhibition, thus producing a robust signal-processing unit that increases signal-to-noise and enhances the contrast between neural codes. More broadly, many high-dimensional complex systems often exhibit dynamics that evolve on a slow-manifold and/or a low-dimensional attractor. Thus we propose a data-driven modeling strategy that encodes/decodes the dynamical evolution using compressive (sparse) sensing (CS) in conjunction with machine learning (ML) strategies for constructing the observed low-dimensional manifolds. The integration of ML and CS techniques also provide an ideal basis for applying control algorithms to the underlying dynamical systems, thus revealing a method of how robust flight control, for instance, can be accomplished.

Neural population dynamics during sensory and memory processing
The size and complexity of neural data is increasing at a dramatic pace due to rapid advances in experimental technologies. As a result, the data analysis techniques are shifting their focus from single-units to neural populations. We use projection methods, such as Principal Component Analysis PCA and Multiple Discriminant Analysis MDA, to facilitate the understanding and monitoring of the dynamics of neural populations recorded in the hippocampus and olfactory bulb. For the hippocampal date, we examine representation of startle episodes, in order to differentiate between somato-sensory and memory components of the hippocampal representations. For the olfactory data, we focus on how dynamics of odor responses in the olfactory receptor neurons of awake rats are shaped by the temporal features of the active odor sniffing. Our analyses indicate that the dynamics of neural representations depend non-linearly on odor identity and concentration, as well as breathing rhythms of the rats. These results include work done with graduate students Jun Xia and Jie Zhang.
Sensory prediction in the natural world
Sensory prediction in the natural world
Optimality and neural codes: Bayesian inference meets Barlow's efficient coding hypothesis

Barlow's "efficient coding hypothesis" asserts that neurons should maximize the information they convey about stimuli. This idea has provided a guiding theoretical framework for the study of coding in neural systems, and has sparked a great many studies of decorrelation and efficiency in early sensory areas. A more recent theory, the "Bayesian brain hypothesis", asserts that neural responses encode posterior distributions in order to support Bayesian inference.


However, these two theories have not yet been formally connected. In this talk, I will introduce a Bayesian theory of efficient coding, which has Barlow's framework as a special case. I will argue that there is nothing privileged about information-maximizing codes: they are ideal when one wishes minimize entropy, but they can be substantially suboptimal in other cases. Moreover, codes optimized for information transfer may differ strongly from codes optimized for other loss functions. Bayesian efficient coding substantially enlarges the family of normatively optimal codes and provides a general framework for understanding the principles of sensory encoding. I will derive Bayesian efficient codes for a few simple examples and show an application to neural data.

Who's got rhythm?, Envelop Temporal Coding in Primary and Non-primary Cortices
Who's got rhythm?, Envelop Temporal Coding in Primary and Non-primary Cortices
Neurogenesis allows olfactory bulb to learn to decorrelate stimuli
The olfactory bulb exhibits substantial turn-over of the dominant interneuron population, even in adult animals. It is observed that with neurogenesis suppressed the animals' capacity for perceptual learning is impaired. We have developed a simple network model in which the connectivity adapts to the odor environment through the experimentally observed dependence of the survival of the interneurons on their activity. Due to the reciprocity of the connections between the principal neurons and the interneurons this restructuring of the network allows it to reduce the correlation of the representations of similar stimuli.
Control strategies for underactuated neural ensembles
Control strategies for underactuated neural ensembles.
Words, metrics, and a thesaurus for a neural population code.
Words, metrics, and a thesaurus for a neural population code.
Neuronal Substrates of Temporal Prediction in Active Sensing

Neuronal oscillations reflecting synchronous, rhythmic fluctuation of neuron ensembles between high and low excitability states, dominate ambient activity in the sensory pathways. Because excitability determines the probability that neurons will respond to input, a top-down process like attention can use oscillations as "instruments" to amplify or suppress the brain's representation of external events. That is, by tuning the frequency and phase of its rhythms to those of behaviorally and/or cognitively-relevant event streams, the brain can use its rhythms to parse event streams and to form internal representations of them. In doing this, the brain is making temporal predictions. I will discuss findings from parallel experiments in humans and non-human primates that outline specific structural and functional components of this temporal prediction mechanism. I will also discuss its possible generalization across temporal scales. Finally, I will discuss motor system contributions to sensory systems' dynamics.

Recurrent Connectivity in Single Neuron Dynamics
Recurrent Connectivity in Single Neuron Dynamics.
Perceptual saliency by integrating olfactory context and feature

Sensory systems must extract important information from background noise in a constantly changing environment. For example, blinking or moving objects in a static background are more likely to attract attention. The odor landscape, like the visual world, is also highly cluttered and noisy. Odor information is parsed out by sensory neurons expressing different odorant receptors into glomerular activity. How does olfactory information about one object become more behaviorally salient than other odors in the environment? How does odor context influence the saliency of certain olfactory features? How does internal physiological state such as hunger and satiety modulate olfactory circuit to generate flexible behavioral response? I will present our recent unpublished data from the fruit fly Drosophila to support the idea that the mushroom body, a higher olfactory center, is important for perceptual saliency. Food odor and conspecific social cue, represented by separate glomeruli in the antennal lobe, are integrated in the mushroom to enhance behavioral attraction to food. Hunger-dependent neuropeptide signal modulates neural activity in the mushroom body to control the intensity of foraging behavior in Drosophila.

Interactions of sensor structure, and environmental and locomotory context determine odor plume tracking behavior.
Interactions of sensor structure, and environmental and locomotory context determine odor plume tracking behavior.
Emergent dynamics in a model of visual cortex

I will report on recent work which proposes that the network dynamics of the mammalian visual cortex are neither homogeneous nor synchronous but highly structured and strongly shaped by temporally localized barrages of excitatory and inhibitory firing we call `multiple-firing events' (MFEs).

Our proposal is based on careful study of a network of spiking neurons built to reflect the coarse physiology of a small patch of layer 2/3 of V1.

When appropriately benchmarked this network is capable of reproducing the qualitative features of a range of phenomena observed in the real visual cortex, including orientation tuning, spontaneous background patterns, surround suppression and gamma-band oscillations. Detailed investigation into the relevant regimes reveals causal relationships among dynamical events driven by a strong competition between the excitatory and inhibitory populations. Testable predictions are proposed; challenges for mathematical neuroscience will also be discussed. This is joint work with Aaditya Rangan.

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Tatyana Sharpee

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Mechanisms and functions of human neocortical rhythms in sensory perception
Stephanie Jones

Low frequency neocortical rhythms are among the most prominent activity measured in human brain imaging signals such as electro- and magneto-encephalography (EEG/MEG). Elucidating the role that these dynamics play in perception, cognition an

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Mechanisms for higher-order correlations in microcircuits
Andrea Barreiro Mechanisms for higher-order correlations in microcircuits.

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Sensory prediction in the natural world
Stephanie Palmer Sensory prediction in the natural world

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Whisker shape changes induced by touch
David Golomb We study why whiskers of land mammals are approximately conical by considering a tapered whisker under contact with an object. We convert the Euler-Bernoulli quasi-static equation into a boundary-value equation and analyze it using dynamical system t

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Optimality and neural codes: Bayesian inference meets Barlow's efficient coding hypothesis
Jonathan Pillow

Barlow's "efficient coding hypothesis" asserts that neurons should maximize the information they convey about stimuli. This idea has provided a guiding theoretical framework for the study of coding in neural systems, and has s

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Neurogenesis allows olfactory bulb to learn to decorrelate stimuli
Hermann Riecke The olfactory bulb exhibits substantial turn-over of the dominant interneuron population, even in adult animals. It is observed that with neurogenesis suppressed the animals' capacity for perceptual learning is impaired. We have developed a simp

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Neural Information Processing Underlying Collision Avoidance Behaviors
Fabrizio Gabbiani

Understanding how the brain processes sensory information in real-time to generate meaningful behaviors is one of the outstanding contemporary challenges of neuroscience. Visually guided collision avoidance behaviors are nearly universal in

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Measuring and interpreting correlated neuronal responses
Kresimir Josic

Populations of neurons jointly drive behavior. Thus, understanding how population activity is coordinated is a key challenge. Novel recording techniques allow for the simultaneous recording from many cells revealing the joint activity of neu

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Interactions of sensor structure, and environmental and locomotory context determine odor plume tracking behavior.
Mark Willis Interactions of sensor structure, and environmental and locomotory context determine odor plume tracking behavior.

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Coordination of visual processing in cortex by network activity during natural viewing
Daniel Butts

In addition to visual information from thalamus, neurons in primary visual cortex (V1) receive inputs from other V1 neurons, as well as from higher cortical areas. This “non-classical� input to V1 neurons, whi

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Neuronal Substrates of Temporal Prediction in Active Sensing
Charles Schroeder

Neuronal oscillations reflecting synchronous, rhythmic fluctuation of neuron ensembles between high and low excitability states, dominate ambient activity in the sensory pathways. Because excitability determines the probability that neurons

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Principles of Biological Design
Stephen Baccus Principles of Biological Design

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Neural population dynamics during sensory and memory processing
Remus Osan The size and complexity of neural data is increasing at a dramatic pace due to rapid advances in experimental technologies. As a result, the data analysis techniques are shifting their focus from single-units to neural populations. We use projection