Scene segmentation by spike synchronization in reciprocally connected visual areas. II. Global assemblies and synchronization on larger space and time scales

Abstract. We present further simulation results of the model of two reciprocally connected visual areas proposed in the first paper [Knoblauch and Palm (2002) Biol Cybern 87:151–167]. One area corresponds to the orientation–selective subsystem of the primary visual cortex, the other is modeled as an associative memory representing stimulus objects according to Hebbian learning. We examine the scene-segmentation capability of our model on larger time and space scales, and relate it to experimental findings. Scene segmentation is achieved by attention switching on a time-scale longer than the gamma range. We find that the time-scale can vary depending on habituation parameters in the range of tens to hundreds of milliseconds. The switching process can be related to findings concerning attention and biased competition, and we reproduce experimental poststimulus time histograms (PSTHs) of single neurons under different stimulus and attentional conditions. In a larger variant the model exhibits traveling waves of activity on both slow and fast time-scales, with properties similar to those found in experiments. An apparent weakness of our standard model is the tendency to produce anti-phase correlations for fast activity from the two areas. Increasing the inter-areal delays in our model produces alternations of in-phase and anti-phase oscillations. The experimentally observed in-phase correlations can most naturally be obtained by the involvement of both fast and slow inter-areal connections; e.g., by two axon populations corresponding to fast-conducting myelinated and slow-conducting unmyelinated axons.

[1]  A. Harvey.  A physiological analysis of subcortical and commissural projections of areas 17 and 18 of the cat. , 1980, The Journal of physiology.

[2]  R. Desimone,et al.  The Role of Neural Mechanisms of Attention in Solving the Binding Problem , 1999, Neuron.

[3]  Ch. von der Malsburg,et al.  A neural cocktail-party processor , 1986, Biological Cybernetics.

[4]  Wulfram Gerstner,et al.  A biologically motivated and analytically soluble model of collective oscillations in the cortex , 1993, Biological Cybernetics.

[5]  Günther Palm,et al.  Improved bidirectional retrieval of sparse patterns stored by Hebbian learning , 1999, Neural Networks.

[6]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[7]  Kunihiko Fukushima,et al.  Neocognitron: A hierarchical neural network capable of visual pattern recognition , 1988, Neural Networks.

[8]  E. Pöppel.  Temporal mechanisms in perception. , 1994, International review of neurobiology.

[9]  L. Shastri,et al.  From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony , 1993, Behavioral and Brain Sciences.

[10]  A. Grinvald,et al.  Linking spontaneous activity of single cortical neurons and the underlying functional architecture. , 1999, Science.

[11]  Thomas Wennekers,et al.  On the relation between neural modelling and experimental neuroscience , 1997 .

[12]  Günther Palm,et al.  Bidirectional Retrieval from Associative Memory , 1997, NIPS.

[13]  Paul Antoine Salin,et al.  Spatial and temporal coherence in cortico-cortical connections: a cross-correlation study in areas 17 and 18 in the cat. , 1992, Visual neuroscience.

[14]  M. Usher,et al.  Segmentation, Binding, and Illusory Conjunctions , 1991, Neural Computation.

[15]  Kimron Shapiro,et al.  Direct measurement of attentional dwell time in human vision , 1994, Nature.

[16]  D. Contreras,et al.  Synchronization of fast (30-40 Hz) spontaneous cortical rhythms during brain activation , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[17]  P König,et al.  Synchronization of oscillatory neuronal responses between striate and extrastriate visual cortical areas of the cat. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[18]  R. Traub,et al.  A mechanism for generation of long-range synchronous fast oscillations in the cortex , 1996, Nature.

[19]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[20]  Wulfram Gerstner,et al.  A biologically motivated and analytically soluble model of collective oscillations in the cortex , 2004, Biological Cybernetics.

[21]  C. von der Malsburg,et al.  Am I Thinking Assemblies , 1986 .

[22]  P König,et al.  Direct physiological evidence for scene segmentation by temporal coding. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[23]  A Treisman,et al.  Feature binding, attention and object perception. , 1998, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[24]  A. M. Dale,et al.  BORDERS OF MULTIPLE VISUAL AREAS IN HUMANS REVEALED BY FUNCTIONAL MRI , 1995 .

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

[26]  Heiko Neumann,et al.  Recurrent V1–V2 interaction in early visual boundary processing , 1999, Biological Cybernetics.

[27]  G. Palm,et al.  On associative memory , 2004, Biological Cybernetics.

[28]  Joachim M. Buhmann,et al.  Pattern Segmentation in Associative Memory , 1990, Neural Computation.

[29]  H Sompolinsky,et al.  Global processing of visual stimuli in a neural network of coupled oscillators. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Innocenti Gm,et al.  The primary visual pathway through the corpus callosum: morphological and functional aspects in the cat , 1980 .

[31]  G. Palm,et al.  Cell assemblies, coherence, and corticohippocampal interplay , 1993, Hippocampus.

[32]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[33]  Prof. Dr. Valentino Braitenberg,et al.  Anatomy of the Cortex , 1991, Studies of Brain Function.

[34]  M. Carandini,et al.  Stimulus dependence of two-state fluctuations of membrane potential in cat visual cortex , 2000, Nature Neuroscience.

[35]  G. Edelman,et al.  Neural dynamics in a model of the thalamocortical system. I. Layers, loops and the emergence of fast synchronous rhythms. , 1997, Cerebral cortex.

[36]  Günther Palm,et al.  Spiking Associative Memory and Scene Segmentation by Synchronization of Cortical Activity , 2001, Emergent Neural Computational Architectures Based on Neuroscience.

[37]  Reinhard Eckhorn,et al.  Neural mechanisms of scene segmentation: recordings from the visual cortex suggest basic circuits for linking field models , 1999, IEEE Trans. Neural Networks.

[38]  G. Deco,et al.  A hierarchical neural system with attentional top–down enhancement of the spatial resolution for object recognition , 2000, Vision Research.

[39]  Ronald J. MacGregor,et al.  Neural and brain modeling , 1987 .

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

[41]  R. Eckhorn,et al.  Flexible cortical gamma-band correlations suggest neural principles of visual processing , 2001 .

[42]  R. Desimone,et al.  Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4 , 1999, The Journal of Neuroscience.

[43]  Gèunther Palm,et al.  Neural Assemblies: An Alternative Approach to Artificial Intelligence , 1982 .

[44]  M. Steriade,et al.  Intracortical and corticothalamic coherency of fast spontaneous oscillations. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[45]  A. Grinvald,et al.  Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses , 1996, Science.

[46]  W. Singer,et al.  Stimulus-dependent synchronization of neuronal responses in the visual cortex of the awake macaque monkey , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[48]  Bernhard Hellwig,et al.  A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex , 2000, Biological Cybernetics.

[49]  Germán Mato,et al.  Synchrony in Excitatory Neural Networks , 1995, Neural Computation.

[50]  Roger D. Traub,et al.  Self-Organized Synaptic Plasticity Contributes to the Shaping of γ and β Oscillations In Vitro , 2001, The Journal of Neuroscience.

[51]  Roman Borisyuk,et al.  Temporal Structure of Neural Activity and Modelling of Information Processing in the Brain , 2001, Emergent Neural Computational Architectures Based on Neuroscience.

[52]  D. Johnston,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997 .

[53]  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.

[54]  C. Koch,et al.  Towards a neurobiological theory of consciousness , 1990 .

[55]  R. Eckhorn,et al.  Stimulus-specific fast oscillations at zero phase between visual areas V1 and V2 of awake monkey. , 1994, Neuroreport.

[56]  Ron Sun,et al.  Computational Architectures Integrating Neural And Symbolic Processes , 1994 .

[57]  Harvey A Swadlow,et al.  Information Flow along Neocortical Axons , 2000 .

[58]  R. Eckhorn,et al.  Coherent oscillations: A mechanism of feature linking in the visual cortex? , 1988, Biological Cybernetics.

[59]  Günther Palm,et al.  Perceptual rivalry between illusory and real contours , 1991, Biological Cybernetics.

[60]  Nils J. Nilsson,et al.  Artificial Intelligence: A New Synthesis , 1997 .

[61]  Günther Palm,et al.  Kognitive Modellierung mit künstlichen neuronalen Netzen , 2000 .

[62]  Günther Palm,et al.  Scene segmentation by spike synchronization in reciprocally connected visual areas. I. Local effects of cortical feedback , 2002, Biological Cybernetics.

[63]  E. Callaway.  Local circuits in primary visual cortex of the macaque monkey. , 1998, Annual review of neuroscience.

[64]  Leslie G. Ungerleider,et al.  Mechanisms of visual attention in the human cortex. , 2000, Annual review of neuroscience.

[65]  Ron Sun,et al.  On Variable Binding in Connectionist Networks , 1992 .

[66]  Andreas Knoblauch,et al.  Pattern separation and synchronization in spiking associative memories and visual areas , 2001, Neural Networks.

[67]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[68]  R. Desimone,et al.  Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.

[69]  Günther Palm,et al.  Iterative retrieval of sparsely coded associative memory patterns , 1996, Neural Networks.

[70]  J Bullier,et al.  Structural basis of cortical synchronization. II. Effects of cortical lesions. , 1995, Journal of neurophysiology.

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

[72]  G. Henry,et al.  Properties of area 17/18 border neurons contributing to the visual transcallosal pathway in the cat , 1990, Visual Neuroscience.