Dynamic population coding of category information in inferior temporal and prefrontal cortex.

Most electrophysiology studies analyze the activity of each neuron separately. While such studies have given much insight into properties of the visual system, they have also potentially overlooked important aspects of information coded in changing patterns of activity that are distributed over larger populations of neurons. In this work, we apply a population decoding method to better estimate what information is available in neuronal ensembles and how this information is coded in dynamic patterns of neural activity in data recorded from inferior temporal cortex (ITC) and prefrontal cortex (PFC) as macaque monkeys engaged in a delayed match-to-category task. Analyses of activity patterns in ITC and PFC revealed that both areas contain "abstract" category information (i.e., category information that is not directly correlated with properties of the stimuli); however, in general, PFC has more task-relevant information, and ITC has more detailed visual information. Analyses examining how information coded in these areas show that almost all category information is available in a small fraction of the neurons in the population. Most remarkably, our results also show that category information is coded by a nonstationary pattern of activity that changes over the course of a trial with individual neurons containing information on much shorter time scales than the population as a whole.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[3]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[4]  Moshe Abeles,et al.  Corticonics: Neural Circuits of Cerebral Cortex , 1991 .

[5]  H Sompolinsky,et al.  Simple models for reading neuronal population codes. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[6]  C. Gross,et al.  Neural ensemble coding in inferior temporal cortex. , 1994, Journal of neurophysiology.

[7]  G. Kane Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[8]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

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

[10]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[11]  Alexandre Pouget,et al.  Probabilistic Interpretation of Population Codes , 1996, Neural Computation.

[12]  G B Stanley,et al.  Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus , 1999, The Journal of Neuroscience.

[13]  E. Miller,et al.  Prospective Coding for Objects in Primate Prefrontal Cortex , 1999, The Journal of Neuroscience.

[14]  Y. Miyashita,et al.  Top-down signal from prefrontal cortex in executive control of memory retrieval , 1999, Nature.

[15]  Tomita H, Ohbayashi M, Nakahara K, Hasegawa I, Miyashita Y: Comments , 1999 .

[16]  M. Gazzaniga,et al.  The new cognitive neurosciences , 2000 .

[17]  A. Zador,et al.  Neural representation and the cortical code. , 2000, Annual review of neuroscience.

[18]  Tomaso Poggio,et al.  Models of object recognition , 2000, Nature Neuroscience.

[19]  Miguel A. L. Nicolelis,et al.  Advances in neural population coding , 2001 .

[20]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[21]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[22]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[23]  David J. Freedman,et al.  Categorical representation of visual stimuli in the primate prefrontal cortex. , 2001, Science.

[24]  J. Mcilwain Population coding: a historical sketch. , 2001, Progress in Brain Research.

[25]  N. Sigala,et al.  Visual categorization shapes feature selectivity in the primate temporal cortex , 2002, Nature.

[26]  Glenn C. Turner,et al.  Oscillations and Sparsening of Odor Representations in the Mushroom Body , 2002, Science.

[27]  Gilles Laurent,et al.  Olfactory network dynamics and the coding of multidimensional signals , 2002, Nature Reviews Neuroscience.

[28]  Inés Samengo,et al.  Information Loss in an Optimal Maximum Likelihood Decoding , 2001, Neural Computation.

[29]  E. Miller,et al.  Timecourse of object‐related neural activity in the primate prefrontal cortex during a short‐term memory task , 2002, The European journal of neuroscience.

[30]  David J. Freedman,et al.  Representation of the Quantity of Visual Items in the Primate Prefrontal Cortex , 2002, Science.

[31]  K. D. Punta,et al.  An ultra-sparse code underlies the generation of neural sequences in a songbird , 2002 .

[32]  David J. Freedman,et al.  Visual categorization and the primate prefrontal cortex: neurophysiology and behavior. , 2002, Journal of neurophysiology.

[33]  Richard Hans Robert Hahnloser,et al.  An ultra-sparse code underliesthe generation of neural sequences in a songbird , 2002, Nature.

[34]  Stefano Panzeri,et al.  Coding of Sensory Signals by Neuronal Populations: The Role of Correlated Activity , 2003, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[35]  Bruno A. Olshausen,et al.  Book Review , 2003, Journal of Cognitive Neuroscience.

[36]  Liam Paninski,et al.  Estimation of Entropy and Mutual Information , 2003, Neural Computation.

[37]  M. Jung,et al.  Dynamics of Population Code for Working Memory in the Prefrontal Cortex , 2003, Neuron.

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

[39]  Leslie G. Ungerleider,et al.  Projections from inferior temporal cortex to prefrontal cortex via the uncinate fascicle in rhesus monkeys , 2004, Experimental Brain Research.

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

[41]  C. Koch,et al.  Invariant visual representation by single neurons in the human brain , 2005, Nature.

[42]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[43]  Thomas Serre,et al.  A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , 2005 .

[44]  T. Pasternak,et al.  Directional Signals in the Prefrontal Cortex and in Area MT during a Working Memory for Visual Motion Task , 2006, The Journal of Neuroscience.

[45]  Daeyeol Lee,et al.  Effects of noise correlations on information encoding and decoding. , 2006, Journal of neurophysiology.

[46]  R. Andersen,et al.  Movement Intention Is Better Predicted than Attention in the Posterior Parietal Cortex , 2006, The Journal of Neuroscience.

[47]  Keiji Tanaka,et al.  Reward Association Affects Neuronal Responses to Visual Stimuli in Macaque TE and Perirhinal Cortices , 2006, The Journal of Neuroscience.

[48]  Britt Anderson,et al.  Joint decoding of visual stimuli by IT neurons’ spike counts is not improved by simultaneous recording , 2006, Experimental Brain Research.

[49]  Danko Nikolic,et al.  Temporal dynamics of information content carried by neurons in the primary visual cortex , 2006, NIPS.

[50]  A. Pouget,et al.  Neural correlations, population coding and computation , 2006, Nature Reviews Neuroscience.

[51]  Jonathon Shlens,et al.  Estimating Information Rates with Confidence Intervals in Neural Spike Trains , 2007, Neural Computation.

[52]  J. Tanji,et al.  Categorization of behavioural sequences in the prefrontal cortex , 2007, Nature.