Invariant representations of visual patterns in a temporal population code

Mammalian visual systems are characterized by their ability to recognize stimuli invariant to various transformations. Here, we investigate the hypothesis that this ability is achieved by the temporal encoding of visual stimuli. By using a model of a cortical network, we show that this encoding is invariant to several transformations and robust with respect to stimulus variability. Furthermore, we show that the proposed model provides a rapid encoding, in accordance with recent physiological results. Taking into account properties of primary visual cortex, the application of the encoding scheme to an enhanced network demonstrates favorable scaling and high performance in a task humans excel at.

[1]  Wolf Singer,et al.  Time as coding space? , 1999, Current Opinion in Neurobiology.

[2]  David I. Perrett,et al.  Neurophysiology of shape processing , 1993, Image Vis. Comput..

[3]  M. Tovée,et al.  Information encoding and the responses of single neurons in the primate temporal visual cortex. , 1993, Journal of neurophysiology.

[4]  Jonathan D. Victor,et al.  Metric-space analysis of spike trains: theory, algorithms and application , 1998, q-bio/0309031.

[5]  I Segev,et al.  Signal delay and input synchronization in passive dendritic structures. , 1993, Journal of neurophysiology.

[6]  P. Milner.  A model for visual shape recognition. , 1974, Psychological review.

[7]  U. Mitzdorf,et al.  Prominent excitatory pathways in the cat visual cortex (A 17 and A 18): A current source density analysis of electrically evoked potentials , 1978, Experimental Brain Research.

[8]  A. Leventhal,et al.  Signal timing across the macaque visual system. , 1998, Journal of neurophysiology.

[9]  E. Rolls.  Functions of the Primate Temporal Lobe Cortical Visual Areas in Invariant Visual Object and Face Recognition , 2000, Neuron.

[10]  J. J. Hopfield,et al.  Pattern recognition computation using action potential timing for stimulus representation , 1995, Nature.

[11]  W. Precht.  The synaptic organization of the brain G.M. Shepherd, Oxford University Press (1975). 364 pp., £3.80 (paperback) , 1976, Neuroscience.

[12]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[13]  Pieter R. Roelfsema,et al.  How Precise is Neuronal Synchronization? , 1995, Neural Computation.

[14]  D. Georgescauld.  Local Cortical Circuits, An Electrophysiological Study , 1983 .

[15]  J. Deuchars,et al.  Temporal and spatial properties of local circuits in neocortex , 1994, Trends in Neurosciences.

[16]  R. Pfeifer,et al.  Connectionism in Perspective , 1989 .

[17]  B J Richmond,et al.  Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. II. Quantification of response waveform. , 1987, Journal of neurophysiology.

[18]  D. Wilkin,et al.  Neuron , 2001, Brain Research.

[19]  Dean V. Buonomano,et al.  A Neural Network Model of Temporal Code Generation and Position-Invariant Pattern Recognition , 1999, Neural Computation.

[20]  P. König,et al.  Correlated firing in sensory-motor systems , 1995, Current Opinion in Neurobiology.

[21]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[22]  R Van Rullen,et al.  Face processing using one spike per neurone. , 1998, Bio Systems.

[23]  Wilfried Brauer,et al.  Translation-invariant pattern recognition based on Synfire chains , 1999, Biological Cybernetics.

[24]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[25]  Wolf Singer,et al.  Neuronal Synchrony: A Versatile Code for the Definition of Relations? , 1999, Neuron.

[26]  W. Bair.  Spike timing in the mammalian visual system , 1999, Current Opinion in Neurobiology.

[27]  C. Malsburg.  Binding in models of perception and brain function , 1995, Current Opinion in Neurobiology.

[28]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[29]  V. Sánchez.  Connectionism in perspective , 1991 .

[30]  Paul F. M. J. Verschure,et al.  On the Role of Biophysical Properties of Cortical Neurons in Binding and Segmentation of Visual Scenes , 1999, Neural Computation.

[31]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[32]  Keiji Tanaka.  Mechanisms of visual object recognition: monkey and human studies , 1997, Current Opinion in Neurobiology.

[33]  W. Singer,et al.  Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex , 1991, Science.

[34]  G. Shepherd.  The Synaptic Organization of the Brain , 1979 .

[35]  Minami Ito,et al.  Columns for visual features of objects in monkey inferotemporal cortex , 1992, Nature.

[36]  James M. Bower,et al.  The Role of Axonal Delay in the Synchronization of Networks of Coupled Cortical Oscillators , 1997, Journal of Computational Neuroscience.

[37]  Minami Ito,et al.  Size and position invariance of neuronal responses in monkey inferotemporal cortex. , 1995, Journal of neurophysiology.

[38]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[39]  E. Rolls,et al.  INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM , 1997, Progress in Neurobiology.

[40]  J. Bullier,et al.  Structural basis of cortical synchronization. I. Three types of interhemispheric coupling. , 1995, Journal of neurophysiology.

[41]  Christopher J. Bishop,et al.  Pulsed Neural Networks , 1998 .

[42]  Wolfgang Maass,et al.  Fast Sigmoidal Networks via Spiking Neurons , 1997, Neural Computation.