Dynamic Coding of Signed Quantities in Cortical Feedback Circuits

In the early sensory and motor areas of the cortex, individual neurons transmit information about specific sensory features via a peaked response. This concept has been crystallized as “labeled lines,” to denote that axons communicate the specific properties of their sensory or motor parent cell. Such cells also can be characterized as being polarized, that is, as representing a signed quantity that is either positive or negative. We show in a model simulation that there are two important consequences when learning receptive fields using such signed codings in circuits that subtract different inputs. The first is that, in feedback circuits using labeled lines, such arithmetic operations need to be distributed across multiple distinct pathways. The second consequence is that such pathways must be necessarily dynamic, i.e., that synapses can grow and retract when forming receptive fields. The model monitors the breaking and growing of new circuit connections when their synapses need to change polarities and predicts that the rate of such changes should be inversely correlated with the progress of receptive field formation.

[1]  Peter Elias,et al.  Predictive coding-I , 1955, IRE Trans. Inf. Theory.

[2]  D. Hubel,et al.  Spatial and chromatic interactions in the lateral geniculate body of the rhesus monkey. , 1966, Journal of neurophysiology.

[3]  A. Fuchs,et al.  Unit activity in vestibular nucleus of the alert monkey during horizontal angular acceleration and eye movement. , 1975, Journal of neurophysiology.

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

[5]  D. Hubel,et al.  Anatomy and physiology of a color system in the primate visual cortex , 1984, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[6]  G. Orban,et al.  Velocity sensitivity and direction selectivity of neurons in areas V1 and V2 of the monkey: influence of eccentricity. , 1986, Journal of neurophysiology.

[7]  H. Rodman,et al.  Coding of visual stimulus velocity in area MT of the macaque , 1987, Vision Research.

[8]  A. Yonas,et al.  Four-month-old infants' sensitivity to binocular and kinetic information for three-dimensional-object shape. , 1987, Child development.

[9]  S. Levay,et al.  Ocular dominance and disparity coding in cat visual cortex , 1988, Visual Neuroscience.

[10]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[11]  R. Reid,et al.  Specificity of monosynaptic connections from thalamus to visual cortex , 1995, Nature.

[12]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[13]  Eero P. Simoncelli,et al.  A model of neuronal responses in visual area MT , 1998, Vision Research.

[14]  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.

[15]  P. C. Murphy,et al.  Feedback connections to the lateral geniculate nucleus and cortical response properties. , 1999, Science.

[16]  A. Borst Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[17]  R. Reid,et al.  Rules of Connectivity between Geniculate Cells and Simple Cells in Cat Primary Visual Cortex , 2001, The Journal of Neuroscience.

[18]  K. Svoboda,et al.  Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex , 2002, Nature.

[19]  S. Thorpe,et al.  Surfing a spike wave down the ventral stream , 2002, Vision Research.

[20]  John Raymond Smythies The Dynamic Neuron , 2002 .

[21]  K. Miller Understanding layer 4 of the cortical circuit: a model based on cat V1. , 2003, Cerebral cortex.

[22]  J. A. Hirsch Synaptic physiology and receptive field structure in the early visual pathway of the cat. , 2003, Cerebral cortex.

[23]  I. Reichova,et al.  Somatosensory corticothalamic projections: distinguishing drivers from modulators. , 2004, Journal of neurophysiology.

[24]  Y. Dan,et al.  Spike Timing-Dependent Plasticity of Neural Circuits , 2004, Neuron.

[25]  E. Callaway,et al.  Excitatory cortical neurons form fine-scale functional networks , 2005, Nature.

[26]  R. Reid,et al.  Receptive field structure varies with layer in the primary visual cortex , 2005, Nature Neuroscience.

[27]  Dana H. Ballard,et al.  Learning receptive fields using predictive feedback , 2006, Journal of Physiology-Paris.

[28]  Kenneth D. Miller,et al.  Adaptive filtering enhances information transmission in visual cortex , 2006, Nature.

[29]  A. Sillito,et al.  Functional alignment of feedback effects from visual cortex to thalamus , 2006, Nature Neuroscience.

[30]  Roberto Araya,et al.  The spine neck filters membrane potentials , 2006, Proceedings of the National Academy of Sciences.

[31]  Roberto Araya,et al.  Dendritic spines linearize the summation of excitatory potentials , 2006, Proceedings of the National Academy of Sciences.

[32]  C. Gilbert,et al.  Axons and Synaptic Boutons Are Highly Dynamic in Adult Visual Cortex , 2006, Neuron.

[33]  E. Callaway,et al.  The Parvocellular LGN Provides a Robust Disynaptic Input to the Visual Motion Area MT , 2006, Neuron.

[34]  Eero P. Simoncelli,et al.  Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis. , 2006, Journal of vision.

[35]  W. Singer,et al.  Modulation of Neuronal Interactions Through Neuronal Synchronization , 2007, Science.

[36]  J. Bourne,et al.  Do thin spines learn to be mushroom spines that remember? , 2007, Current Opinion in Neurobiology.

[37]  Qingbo Wang,et al.  Feedforward Excitation and Inhibition Evoke Dual Modes of Firing in the Cat's Visual Thalamus during Naturalistic Viewing , 2007, Neuron.

[38]  K. Svoboda,et al.  Genetic Dissection of Neural Circuits , 2008, Neuron.

[39]  Tim Gollisch,et al.  Rapid Neural Coding in the Retina with Relative Spike Latencies , 2008, Science.

[40]  J. Bourne,et al.  Balancing structure and function at hippocampal dendritic spines. , 2008, Annual review of neuroscience.

[41]  Michael W. Spratling Reconciling Predictive Coding and Biased Competition Models of Cortical Function , 2008, Frontiers Comput. Neurosci..

[42]  C. Gilbert,et al.  Rapid Axonal Sprouting and Pruning Accompany Functional Reorganization in Primary Visual Cortex , 2009, Neuron.

[43]  Dana H. Ballard,et al.  Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus , 2009, PLoS Comput. Biol..

[44]  Evan S. Schaffer,et al.  Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression , 2009, Neuron.

[45]  Michael W. Spratling Predictive Coding as a Model of Response Properties in Cortical Area V1 , 2010, The Journal of Neuroscience.

[46]  B. Cubelos,et al.  Cux1 and Cux2 Regulate Dendritic Branching, Spine Morphology, and Synapses of the Upper Layer Neurons of the Cortex , 2010, Neuron.

[47]  Dana H. Ballard,et al.  Dual Roles for Spike Signaling in Cortical Neural Populations , 2011, Front. Comput. Neurosci..

[48]  Rajesh P. N. Rao,et al.  Predictive Coding , 2019, A Blueprint for the Hard Problem of Consciousness.

[49]  Michael W. Spratling Unsupervised Learning of Generative and Discriminative Weights Encoding Elementary Image Components in a Predictive Coding Model of Cortical Function , 2012, Neural Computation.