Neural Information Processing with Feedback Modulations

Descending feedback connections, together with ascending feedforward ones, are the indispensable parts of the sensory pathways in the central nervous system. This study investigates the potential roles of feedback interactions in neural information processing. We consider a two-layer continuous attractor neural network (CANN), in which neurons in the first layer receive feedback inputs from those in the second one. By utilizing the intrinsic property of a CANN, we use a projection method to reduce the dimensionality of the network dynamics significantly. The simplified dynamics allows us to elucidate the effects of feedback modulation analytically. We find that positive feedback enhances the stability of the network state, leading to an improved population decoding performance, whereas negative feedback increases the mobility of the network state, inducing spontaneously moving bumps. For strong, negative feedback interaction, the network response to a moving stimulus can lead the actual stimulus position, achieving an anticipative behavior. The biological implications of these findings are discussed. The simulation results agree well with our theoretical analysis.

[1]  Jian-Young Wu,et al.  Propagating Waves of Activity in the Neocortex: What They Are, What They Do , 2008, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[2]  A. Georgopoulos,et al.  Cognitive neurophysiology of the motor cortex. , 1993, Science.

[3]  Si Wu,et al.  Computing with Continuous Attractors: Stability and Online Aspects , 2005, Neural Computation.

[4]  K. Martin,et al.  Termination of the geniculocortical projection in the striate cortex of macaque monkey: A quantitative immunoelectron microscopic study , 2000, The Journal of comparative neurology.

[5]  S. Sherman,et al.  Relative distribution of synapses in the A‐laminae of the lateral geniculate nucleus of the cat , 2000, The Journal of comparative neurology.

[6]  J. O’Keefe,et al.  Phase relationship between hippocampal place units and the EEG theta rhythm , 1993, Hippocampus.

[7]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[8]  B L McNaughton,et al.  Path Integration and Cognitive Mapping in a Continuous Attractor Neural Network Model , 1997, The Journal of Neuroscience.

[9]  Si Wu,et al.  A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks , 2008, Neural Computation.

[10]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[11]  Si Wu,et al.  Population Coding and Decoding in a Neural Field: A Computational Study , 2002, Neural Computation.

[12]  H. Sompolinsky,et al.  13 Modeling Feature Selectivity in Local Cortical Circuits , 2022 .

[13]  Si Wu,et al.  Dynamics and Computation of Continuous Attractors , 2008, Neural Computation.

[14]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

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

[16]  Y. Dan,et al.  An arithmetic rule for spatial summation of excitatory and inhibitory inputs in pyramidal neurons , 2009, Proceedings of the National Academy of Sciences.

[17]  S. Amari.  Dynamics of pattern formation in lateral-inhibition type neural fields , 1977, Biological Cybernetics.

[18]  L. Lagae,et al.  Corticofugal feedback influences the responses of geniculate neurons to moving stimuli , 2004, Experimental Brain Research.

[19]  Christopher C. Pack,et al.  Temporal dynamics of a neural solution to the aperture problem in visual area MT of macaque brain , 2001, Nature.

[20]  Bard Ermentrout,et al.  Spatially Structured Activity in Synaptically Coupled Neuronal Networks: I. Traveling Fronts and Pulses , 2001, SIAM J. Appl. Math..

[21]  D. Heeger.  Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[22]  Mark C. W. van Rossum,et al.  Recurrent networks with short term synaptic depression , 2009, Journal of Computational Neuroscience.

[23]  H. Sompolinsky,et al.  Theory of orientation tuning in visual cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[24]  L. Optican,et al.  Cortical feedback increases visual information transmitted by monkey parvocellular lateral geniculate nucleus neurons , 1994, Visual Neuroscience.

[25]  A. Pouget,et al.  Reading population codes: a neural implementation of ideal observers , 1999, Nature Neuroscience.

[26]  K. Zhang,et al.  Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[27]  J. Lund,et al.  Anatomical substrates for functional columns in macaque monkey primary visual cortex. , 2003, Cerebral cortex.

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

[29]  A. Sillito,et al.  Always returning: feedback and sensory processing in visual cortex and thalamus , 2006, Trends in Neurosciences.

[30]  P. C. Murphy,et al.  Corticofugal feedback influences the generation of length tuning in the visual pathway , 1987, Nature.

[31]  H. T. Blair,et al.  Anticipatory head direction signals in anterior thalamus: evidence for a thalamocortical circuit that integrates angular head motion to compute head direction , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.