Workshop 4: Olfaction Systems

(April 3,2003 - April 5,2003 )

Organizers


Bard Ermentrout
Dept. of Mathematics, University of Pittsburgh
Alan Gelperin
Monell Chemical Sciences Center

Olfaction provides an ideal model for a distributed neural code. Unlike other sensory systems, from the receptor level onward, there is no simple spatial organization of the inputs. The output from receptors terminates on the olfactory bulb (or its analogues, the antennal lobe in insects) where it is processed and sent on to the olfactory cortex (mushroom body, in insects) Thus complex processing occurs at the earliest levels of input.

At the first level of processing, the olfactory bulb (and the analogue regions) is characterized by complex oscillations. These oscillations appear to be crucial in order for the animal to discriminate between odors, particularly those which are closely related. Furthermore, animals can learn a new odor with only a single presentation. Thus, part of this workshop will focus on models and experiments for olfactory oscillations and learning.

The mathematical areas that are expected to be strongly involved in this workshop are dynamical systems (oscillations, perturbation methods, bifurcations) and other areas of differential equations.

Accepted Speakers

Eugene Balkovski
Dept. of Physics and Astronomy, Rutgers University
Maxim Bazhenov
The Salk Inst. for Biological Studies
Thom Cleland
Dept. of Neurobiology & Behavior, Cornell University
Kerry Delaney
Dept. of Biological Sciences, Simon Fraser University
Bard Ermentrout
Dept. of Mathematics, University of Pittsburgh
Alan Gelperin
Monell Chemical Sciences Center
John Hopfield
Dept. of Molecular Biology, Princeton University
Leslie Kay
Dept. of Psychology, University of Chicago
Daniel Lee
Dept. of Electrical Engineering, University of Pennsylvania
Mikhail Rabinovich
Inst. for Nonlinear Science, University of California, San Diego
Brian Smith
Dept. of Entomology, The Ohio State University
Mark Stopfer
National Institutes of Health
Thursday, April 3, 2003
Time Session
09:30 AM
10:30 AM
Brian Smith - Sculpting an Odor Image: Is the olfactory system synthetic, analytic, or both?

Animals modify responses to both pheromones and to nonpheromonal odors based on experience. The behavioral mechanisms through which responses become modified depend on a number of factors. The neural systems that underlie olfactory processing in phylogenetically diverse groups of organisms appear to have evolved similar means to accomplish olfactory coding. Dozens to thousands of somewhat broadly tuned receptors set up a combinatorial code for odors. Yet recent evidence indicates that neural plasticity occurs at early stages of sensory processing. I propose that this plasticity is used to help early processing extract important features from a variable olfactory environment in which targeted odor "objects" differ from time-to-time and place-to-place. My laboratory has identified several behavioral mechanisms (synthetic/configural processing, blocking and attention-like components) used to analyze odor objects. And I propose that neural mechanisms that underlie this behavioral plasticity are those that are set up in early sensory processing. These hypotheses are evaluated using several different insect species, each of which can be used to approach these issues from different combinations of behavioral, neurophysiological and molecular perspectives. The presentation will summarize information about olfactory plasticity from these species. But the point will also be made that we still lack a detailed understanding of the "scene statistics" of odor objects. Any thorough understanding of behavioral and neural mechanisms of olfactory plasticity cannot be complete without a knowledge of how natural odor objects vary from time-to-time and place-to-place.

11:00 AM
12:00 PM
Mark Stopfer - Spatiotemporal Codes for Odor Identity and Concentration

We examined the encoding and decoding of odor identity and concentration by neurons in the first and second relays of the locust olfactory system, the functional analogues of the vertebrate olfactory bulb and olfactory cortex. We found that both the identity and concentration of odorants are represented as spatiotemporal patterns of activity across projection neurons (PNs, analogous to mitral cells) in the first relay. The distributed activity patterns can be classified by similarity as concentration groups contained within larger odor identity clusters. When analyzed as functions of time, the high-dimensional PN activity patterns can be described as families of trajectories lying on low-dimensional manifolds. Each manifold represents odor identity and contains trajectories representing different concentrations. The spatiotemporal activity patterns are then decoded piecewise over very short time periods and compressed into highly informative responses about both identity and concentration by downstream neurons in the second relay.Some references:



  1. Laurent, G. (2002, November 3). Olfactory network dynamics and the coding of multidimensional signals. Nat Rev Neurosci., 11, 884-95.

  2. Perez-Orive, J., Mazor, O., Turner, G., Cassenaer, S., Wilson, R., & Laurent, G. (2002). Oscillations and sparsening of odor representations in the mushroom body. Science, 297, 359-365.

  3. Laurent, G., Stopfer, M., Friedrich, R., Rabinovich, M.I., Volkovskii, A., & Abarbanel, H.D.I. (2001). Odor encoding as an active, dynamical process: Experiments, computation and theory. Annu. Rev. Neurosci., 24, 263-297.

02:00 PM
03:00 PM
John Hopfield - Computational Olfaction and Action Potential Timing

Olfactory computation by synchrony in a spike timing synchronization across different neurons can be selective for the situation where the neurons are driven at similar firing rates, a "many are equals" computation. Based on this principle, we instantiate an abstract algorithm for robust odor recognition into a model network of spiking neurons whose main features are taken from properties of the primary stages of biological olfactory systems, and demonstrate its computational abilities in tasks such as intensity-independent recognition and background rejection. Synaptic plasticity enables a neural system to 'learn' or 'adapt'. We derive a plasticity rule based on the idea that synapse change must also be capable of keeping a network in a functional state by repairing the damage that spontaneously occurs. The derived timing-based plasticity rule also makes the system capable of learning new odors in an unsupervised fashion. The rule is strikingly similar to those experimentally found in LTP.

03:30 PM
04:30 PM
Mikhail Rabinovich - Spatio-temporal Neural Coding: Winner-less-competition Principle. From the experiments to the understanding

Based on experiments with olfactory network dynamics, Clione hunting activity and generation of neural sequence in a songbird, we discuss the dynamical principles that underlie the sequential activity of complex neural ensembles. It looks like context or stimuli dependent sequential activity of the groups of neurons that are able to organize the sequential order in thoughts and actions is one of the most powerful ideas that nature uses in nervous systems. We think that the Winnerless Competition (WLC) principle is controlled such neural activity.The main point of this principle is the transformation of the incoming identity code or spatial code into ensemble (spatio)-temporal output based on the intrinsic switching dynamics of the neural network. In the presence of stimuli, the sequence of the switching, whose geometrical image in phase space is a heteroclinic contour, uniquely depends on the incoming information. Together with the results of computer modeling of the networks with different levels of complexity, we present rigorous results about the existence and stability of the WLC dynamics and discuss the advantages of sequential coding of neural information.

Friday, April 4, 2003
Time Session
09:00 AM
10:00 AM
Alan Gelperin - Learning About Odors With Oscillations and Waves

The olfactory system performs remarkable feats of molecular detection and pattern recognition using what are, in engineering terms, very imprecise computing elements (neurons). Olfactory systems are comprised of an array of odor sensors providing input to a computational engine that performs pattern recognition and sensory integration on the input pattern. By using large numbers of each type of sensory neuron and steadily replacing and renewing each individual sensor, biological olfactory systems have evolved odor sensing and analysis capabilities unmatched by current technology (Gelperin & Hopfield, 2002). The olfactory system of the terrestrial slug Limax maximus provides a convenient experimental system in which to address general issues of olfactory computation. The olfactory system of Limax incorporates many of the general design features of the mammalian olfactory system (Hildebrand & Shepherd, 1997), including coherent oscillatory activity dependent on nitric oxide (Gelperin, 1999). Synaptic plasticity and learning are essential components of olfactory processing (Hudson, 1999) and involve the earliest stages of central synaptic processing of olfactory input (Ermentrout et al., 2001; Yuan, Harley & McLean, 2003).


An early model of learning in the Limax olfactory system (Gelperin, Hopfield & Tank, 1986) delt with central odor representations that could mediate several forms of higher-order conditioning exhibited in behavioral experiments with Limax. More recent work has incorporated the wave propagation and band-like odor representations found in this system [Ermentrout, 2001 #927}. Recent extensions involving adult neurogenesis of olfactory interneurons and learning-activated gene expression in the Limax olfactory centers will be described.



  1. Ermentrout, B., Wang, J. W., Flores, J., & Gelperin, A. (2001). Model for olfactory discrimination and learning in Limax procerebrum incorporating oscillatory dynamics and wave propagation. J Neurophysiol, 85, 1444-1452.

  2. Gelperin, A. (1999). Oscillatory dynamics and information processing in olfactory systems. J Exp Biol, 202, 1855-1864.

  3. Gelperin, A., & Hopfield, J. J. (2002). Electronic and computational olfaction. In P. Given, ed., Chemistry of taste. Washington, DC: American Chemical Society, pp. 289-317.

  4. Gelperin, A., Hopfield, J. J., & Tank, D. W. (1986). The logic of Limax learning. In A. I. Selverston, ed., Model neural networks and behavior. New York: Plenum Press, pp. 237 - 261.

  5. Hilderbrand, J. G., & Shepherd, G. M. (1997). Mechanisms of olfactory discrimination: Converging evidence for common principles across phyla. Ann Rev Neurosci, 20, 595-631.

  6. Hudson, R. (1999). From molecule to mind: the role of experience in shaping olfactory function. J Comp Physiol A, 185, 297-304.

  7. Yuan, Q., Harley, C. W. & McLean, J. H. (2003). Mitral cell beta(1) and 5-HT(2A) receptor colocalization and cAMP coregulation: A new model of norepinephrine-induced learning in the olfactory bulb. Learn Mem, 10, 5-15.

10:30 AM
11:30 AM
Bard Ermentrout - Learning at a Slug's Pace: The role of oscillations and waves in odor learning in Limax

In this talk I will describe several different approaches to the modeling of oscillations and waves found in the procerebral lobe of the terrestrial mollusc, Limax. I will start with a very simple model, laying out several possible mechanisms for waves and show how experimental results select one of them. Next I describe some consequences of waves and synchrony for the learning of new odors. Here I cite some rather surprising experiments which show that certain groups of neurons are selectively stained only after learning. Finally I close with some detailed biophysical models which explain the role of gap junctions and nitric oxide in the synchronization of rhythms in the PC lobe.

01:30 PM
02:30 PM
Leslie Kay - Behavior and Context-dependent Changes in Olfactory Bulb Dynamics

The mammalian olfactory bulb (OB) is a unique cortical structure as it receives input directly from peripheral olfactory receptor neurons. It is also unique as a first sensory relay in that it receives massive centrifugal input from central brain areas, primarily from other parts of the olfactory system and nearly every part of the limbic system. Single mitral cells receive receptor neuron input and project out of the OB. Inhibitory interneurons (granule cells) receive much of the centrifugal input and form lateral connections with mitral cells, and putatively with each other. I will discuss behavioral-state dependent activity and activity changes at the single cell and population level. Activity of mitral cells, as measured by firing rates during operant behavior, is modulated strongly by the behavioral association of an odorant. OB gamma oscillations (40-100 Hz), which have been shown to be associated with odor sniffing in some cases are also dependent on behavioral state and change character dramatically dependent on the type of behavior (waiting, alert immobility, odor discrimination, exploratory sniffing, etc.). The gamma band is in fact two behaviorally distinct bands, and preliminary studies from knockout mice show that the two types of oscillations, termed type 1 and type 2 gamma, may be dependent on different types of synaptic interactions and behavioral states.

02:30 PM
03:30 PM
Thom Cleland - Sensory Acuity and the Construction of Olfactory Representations

The spatial patterning of odor-evoked glomerular activity in the vertebrate olfactory bulb and arthropod antennal lobe reflects an important component of the first-order olfactory representation and contributes to odorant identification. Higher concentration odor stimuli evoke broader glomerular activation patterns, resulting in greater spatial overlap among different odor representations. However, behavioral studies demonstrate results contrary to what these data suggest: both mice and honeybees are more, not less, able to discriminate among odorants presented at higher concentrations. Using a computational model incorporating the honeybee antennal lobe and an identified reward interneuron, and utilizing spike timing-dependent synaptic plasticity (STDP), a hebbian learning rule derived from physiological studies in mammalian hippocampus, we show that changes in spike synchronization patterns among projection neurons, as observed electrophysiologically, could parsimoniously underlie these observations. Existing physiological and psychophysical data suggest that this model may be appropriately extended to the vertebrate olfactory bulb as well. The results suggest that effective activity among projection neurons, defined as spiking activity that influences the firing of a postsynaptic neuron, may be considerably sparser than overall spiking activity, and that stimulus salience, as defined behaviorally, is modified by stimulus intensity and directly correlated with the degree of spike synchronization among second-order olfactory neurons.

04:00 PM
05:00 PM
Kerry Delaney - Interactions between Synaptic Transmission and the Intrinsic Electrophysiological Properties of Olfactory Bulb Neurons

Neural circuits that process inputs from sensory organs, such as those in the olfactory bulb (OB), must detect the presence of a stimulus and differentiate one stimulus from another. To achieve this they need to have mechanisms to selectively emphasize salient features of the input stimulus, in other words, to increase signal to noise and to enhance ?contrast? between different stimuli. Such computations by neural circuits depend upon the intrinsic electro-physiological characteristics of neurons, the properties of the synapses that link them and the interaction between these cellular and synaptic properties In granule cells we have found a calcium activated non-specific current that can be induced by action potential activity or large amplitude excitatory synaptic currents that are capable of elevating cytoplasmic [Ca2+]. It can thereby amplify large but not small synaptic potentials. Furthermore, the channel?s sensitivity to Ca2+ is potentiated for many seconds after a brief period of [Ca2+] elevation. Thus a burst of action potentials that precede a synaptic input by several seconds will enhance the synaptic potential provided the potential is large enough to generate a Ca2+ influx itself. In mitral cells we have found dopaminergic D2-type receptors are capable of inhibiting transmitter release from the basal dendrites. This results in reduced inhibitory feedback from granule cells/ A portion of the inhibition of release arises from a activation of a transient K+ current that causes attenuation of the propagation of action potentials into the dendrites resulting from D2-receptor mediated hyperpolarization. Consequences of this offsetting combination of reduced excitability due to hyperpolarization and disinhibition resulting from reduced excitation of granule cells will be presented and discussed.

Saturday, April 5, 2003
Time Session
09:00 AM
10:00 AM
Daniel Lee - Computational Olfaction in Mobile Robots

Olfactory systems in biological organisms are capable of performing amazing feats of molecular identification and analysis. These systems manage to overcome the limitations of imprecise neuronal hardware and identify components in complex mixtures over twelve orders of magnitude in concentration. Is it possible to replicate some of these sensory and computational capabilities in artificial systems? I will describe some computational algorithms needed for artificial olfactory systems to approach some of the capabilities of biological organisms. I will also demonstrate a mobile robotic olfactory system that is equipped with electronic chemical sensors, otherwise known as an ``E-nose.'' The challenge for computational algorithms is to obtain the maximal amount of information about the external olfactory environment from the sensor readings, without being corrupted by noise. I will describe recently developed learning algorithms for dimensionality reduction that are able to process the data from the sensors, and to recognize the different patterns of activity.

10:30 PM
11:30 PM
Eugene Balkovski - Olfactory Search at High Reynolds Number and the Flying Moth

Locating the source of odor in a turbulent environment --- a common behavior for living organisms --- is non-trivial because of the random nature of mixing. We analyze the statistical physics aspects of the problem and propose an efficient strategy for olfactory search which can work in turbulent plumes. The algorithm combines the maximum likelihood inference of the source position with an active search. Our approach provides the theoretical basis for the design of olfactory robots and the quantitative tools for the analysis of the observed olfactory search behavior of living creatures (e.g. odor modulated optomotor anemotaxis of moth).

Name Email Affiliation
Balkovski, Eugene balkovsk@physics.rutgers.edu Dept. of Physics and Astronomy, Rutgers University
Balu, Ramani rxb101@po.cwru.edu Department of Neuroscience, Case Western Reserve University
Bazhenov, Maxim bazhenov@salk.edu The Salk Inst. for Biological Studies
Borisovich, Andrei Department of Mathematics, Univeristy of Gdansk
Borisyuk, Alla borisyuk@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Cleland, Thom tac29@cornell.edu Dept. of Neurobiology & Behavior, Cornell University
Cowen, Carl cowen@mbi.osu.edu Department of Mathematics, The Ohio State University
Cracium, Gheorghe craciun@math.wisc.edu Mathematical Biosciences Institute, The Ohio State University
Crook, Sharon crook@math.umaine.edu Department of Mathematics, University of Maine
Danthi, Sanjay danthi.1@osu.edu Mathematical Biosciences Institute, The Ohio State University
Delaney, Kerry kdelaney@sfu.ca Dept. of Biological Sciences, Simon Fraser University
Dougherty, Daniel dpdoughe@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Ermentrout, Bard bard@pitt.edu Dept. of Mathematics, University of Pittsburgh
Flannery, Rick Mathematical Sciences, University of Cincinnati
French, Donald french@math.uc.edu Dept. of Mathematical Sciences, University of Cincinnati
Gelperin, Alan agelperin@monell.org Monell Chemical Sciences Center
Goel, Pranay goelpra@helix.nih.gov Mathematics Department, University of Pittsburgh
Greenside, Henry hsg@phy.duke.edu Physics Department, Duke University
Hayot, Fernand hayot@mps.ohio-state.edu Department of Physics, The Ohio State University
Hopfield, John hopfield@molbio.princeton.edu Dept. of Molecular Biology, Princeton University
Hugues, Etienne hugues@loria.fr INRIA-Lorraine/LORIA
Josic, Kresimir josic@math.bu.edu Department of Mathematics, University of Houston
Kay, Leslie lkay@uchicago.edu Dept. of Psychology, University of Chicago
Lee, Daniel ddlee@seas.upenn.edu Dept. of Electrical Engineering, University of Pennsylvania
Mainen, Zach mainen@cshl.edu Cold Spring Harbor Lab.
Mead, Kristina meadk@denison.edu Department of Biology, Denison University
Pressler, Todd toddp@cwru.edu Department of Neuroscience, Case Western Reserve University
Pugh, Mary mpugh@math.toronto.edu Department of Mathematics, University of Toronto
Rabinovich, Mikhail mrabinovich@ucsd.edu Inst. for Nonlinear Science, University of California, San Diego
Rejniak, Katarzyna rejniak@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Sauer, Tim tsauer@gmu.edu Department of Mathematics, George Mason University
Smith, Brian smith.210@osu.edu Dept. of Entomology, The Ohio State University
Stopfer, Mark stopferm@mail.nih.gov National Institutes of Health
Strowbridge, Ben bens@cwru.edu Department of Neuroscience, Case Western Reserve University
Terman, David terman@math.ohio-state.edu Department of Mathematics, The Ohio State University
Thomson, Mitchell Mathematical Biosciences Institute, The Ohio State University
Urban, Nathan nurban@cmu.edu Dept. of Biological Sciences, Carnegie-Mellon University
Wechselberger, Martin wm@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Wright, Geraldine wright.572@osu.edu Mathematical Biosciences Institute, The Ohio State University
Yew, Alice yew@math.ohio-state.edu Department of Mathematics, The Ohio State University
Olfactory Search at High Reynolds Number and the Flying Moth

Locating the source of odor in a turbulent environment --- a common behavior for living organisms --- is non-trivial because of the random nature of mixing. We analyze the statistical physics aspects of the problem and propose an efficient strategy for olfactory search which can work in turbulent plumes. The algorithm combines the maximum likelihood inference of the source position with an active search. Our approach provides the theoretical basis for the design of olfactory robots and the quantitative tools for the analysis of the observed olfactory search behavior of living creatures (e.g. odor modulated optomotor anemotaxis of moth).

Sensory Acuity and the Construction of Olfactory Representations

The spatial patterning of odor-evoked glomerular activity in the vertebrate olfactory bulb and arthropod antennal lobe reflects an important component of the first-order olfactory representation and contributes to odorant identification. Higher concentration odor stimuli evoke broader glomerular activation patterns, resulting in greater spatial overlap among different odor representations. However, behavioral studies demonstrate results contrary to what these data suggest: both mice and honeybees are more, not less, able to discriminate among odorants presented at higher concentrations. Using a computational model incorporating the honeybee antennal lobe and an identified reward interneuron, and utilizing spike timing-dependent synaptic plasticity (STDP), a hebbian learning rule derived from physiological studies in mammalian hippocampus, we show that changes in spike synchronization patterns among projection neurons, as observed electrophysiologically, could parsimoniously underlie these observations. Existing physiological and psychophysical data suggest that this model may be appropriately extended to the vertebrate olfactory bulb as well. The results suggest that effective activity among projection neurons, defined as spiking activity that influences the firing of a postsynaptic neuron, may be considerably sparser than overall spiking activity, and that stimulus salience, as defined behaviorally, is modified by stimulus intensity and directly correlated with the degree of spike synchronization among second-order olfactory neurons.

Interactions between Synaptic Transmission and the Intrinsic Electrophysiological Properties of Olfactory Bulb Neurons

Neural circuits that process inputs from sensory organs, such as those in the olfactory bulb (OB), must detect the presence of a stimulus and differentiate one stimulus from another. To achieve this they need to have mechanisms to selectively emphasize salient features of the input stimulus, in other words, to increase signal to noise and to enhance ?contrast? between different stimuli. Such computations by neural circuits depend upon the intrinsic electro-physiological characteristics of neurons, the properties of the synapses that link them and the interaction between these cellular and synaptic properties In granule cells we have found a calcium activated non-specific current that can be induced by action potential activity or large amplitude excitatory synaptic currents that are capable of elevating cytoplasmic [Ca2+]. It can thereby amplify large but not small synaptic potentials. Furthermore, the channel?s sensitivity to Ca2+ is potentiated for many seconds after a brief period of [Ca2+] elevation. Thus a burst of action potentials that precede a synaptic input by several seconds will enhance the synaptic potential provided the potential is large enough to generate a Ca2+ influx itself. In mitral cells we have found dopaminergic D2-type receptors are capable of inhibiting transmitter release from the basal dendrites. This results in reduced inhibitory feedback from granule cells/ A portion of the inhibition of release arises from a activation of a transient K+ current that causes attenuation of the propagation of action potentials into the dendrites resulting from D2-receptor mediated hyperpolarization. Consequences of this offsetting combination of reduced excitability due to hyperpolarization and disinhibition resulting from reduced excitation of granule cells will be presented and discussed.

Learning at a Slug's Pace: The role of oscillations and waves in odor learning in Limax

In this talk I will describe several different approaches to the modeling of oscillations and waves found in the procerebral lobe of the terrestrial mollusc, Limax. I will start with a very simple model, laying out several possible mechanisms for waves and show how experimental results select one of them. Next I describe some consequences of waves and synchrony for the learning of new odors. Here I cite some rather surprising experiments which show that certain groups of neurons are selectively stained only after learning. Finally I close with some detailed biophysical models which explain the role of gap junctions and nitric oxide in the synchronization of rhythms in the PC lobe.

Learning About Odors With Oscillations and Waves

The olfactory system performs remarkable feats of molecular detection and pattern recognition using what are, in engineering terms, very imprecise computing elements (neurons). Olfactory systems are comprised of an array of odor sensors providing input to a computational engine that performs pattern recognition and sensory integration on the input pattern. By using large numbers of each type of sensory neuron and steadily replacing and renewing each individual sensor, biological olfactory systems have evolved odor sensing and analysis capabilities unmatched by current technology (Gelperin & Hopfield, 2002). The olfactory system of the terrestrial slug Limax maximus provides a convenient experimental system in which to address general issues of olfactory computation. The olfactory system of Limax incorporates many of the general design features of the mammalian olfactory system (Hildebrand & Shepherd, 1997), including coherent oscillatory activity dependent on nitric oxide (Gelperin, 1999). Synaptic plasticity and learning are essential components of olfactory processing (Hudson, 1999) and involve the earliest stages of central synaptic processing of olfactory input (Ermentrout et al., 2001; Yuan, Harley & McLean, 2003).


An early model of learning in the Limax olfactory system (Gelperin, Hopfield & Tank, 1986) delt with central odor representations that could mediate several forms of higher-order conditioning exhibited in behavioral experiments with Limax. More recent work has incorporated the wave propagation and band-like odor representations found in this system [Ermentrout, 2001 #927}. Recent extensions involving adult neurogenesis of olfactory interneurons and learning-activated gene expression in the Limax olfactory centers will be described.



  1. Ermentrout, B., Wang, J. W., Flores, J., & Gelperin, A. (2001). Model for olfactory discrimination and learning in Limax procerebrum incorporating oscillatory dynamics and wave propagation. J Neurophysiol, 85, 1444-1452.

  2. Gelperin, A. (1999). Oscillatory dynamics and information processing in olfactory systems. J Exp Biol, 202, 1855-1864.

  3. Gelperin, A., & Hopfield, J. J. (2002). Electronic and computational olfaction. In P. Given, ed., Chemistry of taste. Washington, DC: American Chemical Society, pp. 289-317.

  4. Gelperin, A., Hopfield, J. J., & Tank, D. W. (1986). The logic of Limax learning. In A. I. Selverston, ed., Model neural networks and behavior. New York: Plenum Press, pp. 237 - 261.

  5. Hilderbrand, J. G., & Shepherd, G. M. (1997). Mechanisms of olfactory discrimination: Converging evidence for common principles across phyla. Ann Rev Neurosci, 20, 595-631.

  6. Hudson, R. (1999). From molecule to mind: the role of experience in shaping olfactory function. J Comp Physiol A, 185, 297-304.

  7. Yuan, Q., Harley, C. W. & McLean, J. H. (2003). Mitral cell beta(1) and 5-HT(2A) receptor colocalization and cAMP coregulation: A new model of norepinephrine-induced learning in the olfactory bulb. Learn Mem, 10, 5-15.

Computational Olfaction and Action Potential Timing

Olfactory computation by synchrony in a spike timing synchronization across different neurons can be selective for the situation where the neurons are driven at similar firing rates, a "many are equals" computation. Based on this principle, we instantiate an abstract algorithm for robust odor recognition into a model network of spiking neurons whose main features are taken from properties of the primary stages of biological olfactory systems, and demonstrate its computational abilities in tasks such as intensity-independent recognition and background rejection. Synaptic plasticity enables a neural system to 'learn' or 'adapt'. We derive a plasticity rule based on the idea that synapse change must also be capable of keeping a network in a functional state by repairing the damage that spontaneously occurs. The derived timing-based plasticity rule also makes the system capable of learning new odors in an unsupervised fashion. The rule is strikingly similar to those experimentally found in LTP.

Behavior and Context-dependent Changes in Olfactory Bulb Dynamics

The mammalian olfactory bulb (OB) is a unique cortical structure as it receives input directly from peripheral olfactory receptor neurons. It is also unique as a first sensory relay in that it receives massive centrifugal input from central brain areas, primarily from other parts of the olfactory system and nearly every part of the limbic system. Single mitral cells receive receptor neuron input and project out of the OB. Inhibitory interneurons (granule cells) receive much of the centrifugal input and form lateral connections with mitral cells, and putatively with each other. I will discuss behavioral-state dependent activity and activity changes at the single cell and population level. Activity of mitral cells, as measured by firing rates during operant behavior, is modulated strongly by the behavioral association of an odorant. OB gamma oscillations (40-100 Hz), which have been shown to be associated with odor sniffing in some cases are also dependent on behavioral state and change character dramatically dependent on the type of behavior (waiting, alert immobility, odor discrimination, exploratory sniffing, etc.). The gamma band is in fact two behaviorally distinct bands, and preliminary studies from knockout mice show that the two types of oscillations, termed type 1 and type 2 gamma, may be dependent on different types of synaptic interactions and behavioral states.

Computational Olfaction in Mobile Robots

Olfactory systems in biological organisms are capable of performing amazing feats of molecular identification and analysis. These systems manage to overcome the limitations of imprecise neuronal hardware and identify components in complex mixtures over twelve orders of magnitude in concentration. Is it possible to replicate some of these sensory and computational capabilities in artificial systems? I will describe some computational algorithms needed for artificial olfactory systems to approach some of the capabilities of biological organisms. I will also demonstrate a mobile robotic olfactory system that is equipped with electronic chemical sensors, otherwise known as an ``E-nose.'' The challenge for computational algorithms is to obtain the maximal amount of information about the external olfactory environment from the sensor readings, without being corrupted by noise. I will describe recently developed learning algorithms for dimensionality reduction that are able to process the data from the sensors, and to recognize the different patterns of activity.

Spatio-temporal Neural Coding: Winner-less-competition Principle. From the experiments to the understanding

Based on experiments with olfactory network dynamics, Clione hunting activity and generation of neural sequence in a songbird, we discuss the dynamical principles that underlie the sequential activity of complex neural ensembles. It looks like context or stimuli dependent sequential activity of the groups of neurons that are able to organize the sequential order in thoughts and actions is one of the most powerful ideas that nature uses in nervous systems. We think that the Winnerless Competition (WLC) principle is controlled such neural activity.The main point of this principle is the transformation of the incoming identity code or spatial code into ensemble (spatio)-temporal output based on the intrinsic switching dynamics of the neural network. In the presence of stimuli, the sequence of the switching, whose geometrical image in phase space is a heteroclinic contour, uniquely depends on the incoming information. Together with the results of computer modeling of the networks with different levels of complexity, we present rigorous results about the existence and stability of the WLC dynamics and discuss the advantages of sequential coding of neural information.

Sculpting an Odor Image: Is the olfactory system synthetic, analytic, or both?

Animals modify responses to both pheromones and to nonpheromonal odors based on experience. The behavioral mechanisms through which responses become modified depend on a number of factors. The neural systems that underlie olfactory processing in phylogenetically diverse groups of organisms appear to have evolved similar means to accomplish olfactory coding. Dozens to thousands of somewhat broadly tuned receptors set up a combinatorial code for odors. Yet recent evidence indicates that neural plasticity occurs at early stages of sensory processing. I propose that this plasticity is used to help early processing extract important features from a variable olfactory environment in which targeted odor "objects" differ from time-to-time and place-to-place. My laboratory has identified several behavioral mechanisms (synthetic/configural processing, blocking and attention-like components) used to analyze odor objects. And I propose that neural mechanisms that underlie this behavioral plasticity are those that are set up in early sensory processing. These hypotheses are evaluated using several different insect species, each of which can be used to approach these issues from different combinations of behavioral, neurophysiological and molecular perspectives. The presentation will summarize information about olfactory plasticity from these species. But the point will also be made that we still lack a detailed understanding of the "scene statistics" of odor objects. Any thorough understanding of behavioral and neural mechanisms of olfactory plasticity cannot be complete without a knowledge of how natural odor objects vary from time-to-time and place-to-place.

Spatiotemporal Codes for Odor Identity and Concentration

We examined the encoding and decoding of odor identity and concentration by neurons in the first and second relays of the locust olfactory system, the functional analogues of the vertebrate olfactory bulb and olfactory cortex. We found that both the identity and concentration of odorants are represented as spatiotemporal patterns of activity across projection neurons (PNs, analogous to mitral cells) in the first relay. The distributed activity patterns can be classified by similarity as concentration groups contained within larger odor identity clusters. When analyzed as functions of time, the high-dimensional PN activity patterns can be described as families of trajectories lying on low-dimensional manifolds. Each manifold represents odor identity and contains trajectories representing different concentrations. The spatiotemporal activity patterns are then decoded piecewise over very short time periods and compressed into highly informative responses about both identity and concentration by downstream neurons in the second relay.Some references:



  1. Laurent, G. (2002, November 3). Olfactory network dynamics and the coding of multidimensional signals. Nat Rev Neurosci., 11, 884-95.

  2. Perez-Orive, J., Mazor, O., Turner, G., Cassenaer, S., Wilson, R., & Laurent, G. (2002). Oscillations and sparsening of odor representations in the mushroom body. Science, 297, 359-365.

  3. Laurent, G., Stopfer, M., Friedrich, R., Rabinovich, M.I., Volkovskii, A., & Abarbanel, H.D.I. (2001). Odor encoding as an active, dynamical process: Experiments, computation and theory. Annu. Rev. Neurosci., 24, 263-297.