Modulating the Granularity of Category Formation by Global Cortical States

The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category formation, we consider an unbroken continuum of stimuli without intrinsic category boundaries. We show that a competitive network, shaped by recurrent inhibition and endowed with Hebbian and homeostatic synaptic plasticity, can enforce stimulus categorization. The degree of competition is internally controlled by the neuronal gain and the strength of inhibition. Strong competition leads to the formation of many attracting network states, each being evoked by a distinct subset of stimuli and representing a category. Weak competition allows more neurons to be co-active, resulting in fewer but larger categories. We conclude that the granularity of cortical category formation, i.e., the number and size of emerging categories, is not simply determined by the richness of the stimulus environment, but rather by some global internal signal modulating the network dynamics. The model also explains the salient non-additivity of visual object representation observed in the monkey inferotemporal (IT) cortex. Furthermore, it offers an explanation of a previously observed, demand-dependent modulation of IT activity on a stimulus categorization task and of categorization-related cognitive deficits in schizophrenic patients.

[1]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[2]  Y. Miyashita.  Neuronal correlate of visual associative long-term memory in the primate temporal cortex , 1988, Nature.

[3]  Gustavo Deco,et al.  Learning to Attend: Modeling the Shaping of Selectivity in Infero-temporal Cortex in a Categorization Task , 2006, Biological Cybernetics.

[4]  D. Sagi,et al.  Dynamics of Memory Representations in Networks with Novelty-Facilitated Synaptic Plasticity , 2006, Neuron.

[5]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[6]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[7]  W. Senn,et al.  Dopamine increases the gain of the input–output response of rat prefrontal pyramidal neurons. J. Neurophysiol. (in press). doi: 10.1152/jn.01098.2007 [epub ahead of print , 2008 .

[8]  Keiji Tanaka,et al.  Object category structure in response patterns of neuronal population in monkey inferior temporal cortex. , 2007, Journal of neurophysiology.

[9]  I. Fujita,et al.  Organization of horizontal axons in the inferior temporal cortex and primary visual cortex of the macaque monkey. , 2005, Cerebral cortex.

[10]  Peter E. Latham,et al.  A Balanced Memory Network , 2007, PLoS Comput. Biol..

[11]  N Brunel,et al.  Correlations of cortical Hebbian reverberations: theory versus experiment , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[12]  Gustavo Deco,et al.  A Dynamical Systems Hypothesis of Schizophrenia , 2007, PLoS Comput. Biol..

[13]  K. Miller.  Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns* , 1996 .

[14]  Xiao-Jing Wang,et al.  An Integrated Microcircuit Model of Attentional Processing in the Neocortex , 2007, The Journal of Neuroscience.

[15]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[16]  L. Cooper,et al.  Synaptic homeostasis and input selectivity follow from a calcium-dependent plasticity model. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[17]  C G Gross,et al.  Stimulus selectivity and state dependence of activity in inferior temporal cortex of infant monkeys. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[18]  S. Nelson,et al.  Selective reconfiguration of layer 4 visual cortical circuitry by visual deprivation , 2004, Nature Neuroscience.

[19]  M. Gluck,et al.  The cognitive neuroscience of category learning , 2008, Neuroscience & Biobehavioral Reviews.

[20]  Alberto Bernacchia,et al.  Impact of spatiotemporally correlated images on the structure of memory , 2007, Proceedings of the National Academy of Sciences.

[21]  S. Hestrin,et al.  Electrical synapses define networks of neocortical GABAergic neurons , 2005, Trends in Neurosciences.

[22]  Carrie J. McAdams,et al.  Effects of Attention on the Reliability of Individual Neurons in Monkey Visual Cortex , 1999, Neuron.

[23]  C. Koch,et al.  Attention activates winner-take-all competition among visual filters , 1999, Nature Neuroscience.

[24]  P. J. Sjöström,et al.  Rate, Timing, and Cooperativity Jointly Determine Cortical Synaptic Plasticity , 2001, Neuron.

[25]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[26]  W. Senn,et al.  Top-down dendritic input increases the gain of layer 5 pyramidal neurons. , 2004, Cerebral cortex.

[27]  Karl J. Friston,et al.  Where bottom-up meets top-down: neuronal interactions during perception and imagery. , 2004, Cerebral cortex.

[28]  L. Abbott.  Cortical Remapping through Spike Timing-Dependent Plasticity , 2005 .

[29]  J. Fuster.  Inferotemporal units in selective visual attention and short-term memory. , 1990, Journal of neurophysiology.

[30]  D. Katz,et al.  State-dependent modulation of time-varying gustatory responses. , 2006, Journal of neurophysiology.

[31]  E. Rolls,et al.  Object perception in natural scenes: encoding by inferior temporal cortex simultaneously recorded neurons. , 2005, Journal of neurophysiology.

[32]  Jonathan D. Cohen,et al.  Prefrontal cortex and dynamic categorization tasks: representational organization and neuromodulatory control. , 2002, Cerebral cortex.

[33]  J. Reynolds,et al.  Attentional modulation of visual processing. , 2004, Annual review of neuroscience.

[34]  E T Rolls,et al.  Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. , 1995, Journal of neurophysiology.

[35]  Martin Sarter,et al.  Abnormal Neurotransmitter Release Underlying Behavioral and Cognitive Disorders: Toward Concepts of Dynamic and Function-Specific Dysregulation , 2007, Neuropsychopharmacology.

[36]  Michel Magnin,et al.  Emotional Modulation of Pain: Is It the Sensation or What We Recall? , 2006, The Journal of Neuroscience.

[37]  E. Rolls,et al.  Attention, short-term memory, and action selection: A unifying theory , 2005, Progress in Neurobiology.

[38]  Keiji Tanaka,et al.  Neuronal Responses to Object Images in the Macaque Inferotemporal Cortex at Different Stimulus Discrimination Levels , 2006, The Journal of Neuroscience.

[39]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[40]  A. Antal,et al.  Category learning and perceptual categorization in schizophrenia. , 1999, Schizophrenia bulletin.

[41]  L. Abbott,et al.  Cortical Development and Remapping through Spike Timing-Dependent Plasticity , 2001, Neuron.

[42]  N. Logothetis,et al.  The Effect of Learning on the Function of Monkey Extrastriate Visual Cortex , 2004, PLoS biology.

[43]  I. Fujita,et al.  Neuronal mechanisms of selectivity for object features revealed by blocking inhibition in inferotemporal cortex , 2000, Nature Neuroscience.

[44]  M. Tsodyks,et al.  The Enhanced Storage Capacity in Neural Networks with Low Activity Level , 1988 .

[45]  M. Laruelle,et al.  Neurobiology of dopamine in schizophrenia. , 2007, International review of neurobiology.

[46]  Dany Arsenault,et al.  Gain modulation by serotonin in pyramidal neurones of the rat prefrontal cortex , 2005, The Journal of physiology.

[47]  H. Komatsu,et al.  Effects of task demands on the responses of color-selective neurons in the inferior temporal cortex , 2007, Nature Neuroscience.

[48]  Y. Yamane,et al.  Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns , 2001, Nature Neuroscience.

[49]  John H. R. Maunsell,et al.  Effects of task difficulty and target likelihood in area V4 of macaque monkeys. , 2006, Journal of neurophysiology.

[50]  Karl J. Friston,et al.  Attentional modulation of effective connectivity from V2 to V5/MT in humans. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[51]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[52]  KD Miller.  A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON- and OFF-center inputs , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[53]  Thomas H. Brown,et al.  Hebbian Synaptic Plasticity: Evolution of the Contemporary Concept , 1994 .

[54]  T J Sejnowski,et al.  Learning viewpoint-invariant face representations from visual experience in an attractor network. , 1998, Network.

[55]  Keiji Tanaka,et al.  Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.

[56]  S. Grossberg,et al.  ART 2: self-organization of stable category recognition codes for analog input patterns. , 1987, Applied optics.

[57]  Edmund T. Rolls,et al.  What determines the capacity of autoassociative memories in the brain? Network , 1991 .

[58]  R. Desimone,et al.  Visual properties of neurons in area V4 of the macaque: sensitivity to stimulus form. , 1987, Journal of neurophysiology.

[59]  Xiao-Jing Wang,et al.  Probabilistic Decision Making by Slow Reverberation in Cortical Circuits , 2002, Neuron.

[60]  S. Nelson,et al.  Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.

[61]  M. Behrmann,et al.  Impact of learning on representation of parts and wholes in monkey inferotemporal cortex , 2002, Nature Neuroscience.

[62]  David J. Freedman,et al.  A Comparison of Primate Prefrontal and Inferior Temporal Cortices during Visual Categorization , 2003, The Journal of Neuroscience.

[63]  S Fusi,et al.  Forming classes by stimulus frequency: Behavior and theory , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[64]  Karl J. Friston,et al.  Dynamic representations and generative models of brain function , 2001, Brain Research Bulletin.

[65]  Walter Senn,et al.  Perceptual Learning via Modification of Cortical Top-Down Signals , 2007, PLoS Comput. Biol..

[66]  Quanxin Wang,et al.  Experience-dependent development of feedforward and feedback circuits between lower and higher areas of mouse visual cortex , 2004, Vision Research.

[67]  E. Vaadia,et al.  Firing patterns of single units in the prefrontal cortex and neural network models , 1990 .

[68]  C. A. Frank,et al.  Mechanisms Underlying the Rapid Induction and Sustained Expression of Synaptic Homeostasis , 2006, Neuron.