Resolution enhancement in neural networks with dynamical synapses

Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.

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

[2]  Heiko Neumann,et al.  A Model of Motion Transparency Processing with Local Center-Surround Interactions and Feedback , 2011, Neural Computation.

[3]  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.

[4]  Stefanos E. Folias,et al.  Nonlinear Analysis of Breathing Pulses in a Synaptically Coupled Neural Network , 2011, SIAM J. Appl. Dyn. Syst..

[5]  H. Markram,et al.  Redistribution of synaptic efficacy between neocortical pyramidal neurons , 1996, Nature.

[6]  R A Andersen,et al.  Transparent motion perception as detection of unbalanced motion signals. I. Psychophysics , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[8]  Eero P. Simoncelli,et al.  How MT cells analyze the motion of visual patterns , 2006, Nature Neuroscience.

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

[10]  J. M. Herrmann,et al.  Dynamical synapses causing self-organized criticality in neural networks , 2007, 0712.1003.

[11]  Misha Tsodyks,et al.  Short-Term Facilitation may Stabilize Parametric Working Memory Trace , 2011, Front. Comput. Neurosci..

[12]  Stefan Treue,et al.  Seeing multiple directions of motion—physiology and psychophysics , 2000, Nature Neuroscience.

[13]  Misha Tsodyks,et al.  Computation by Ensemble Synchronization in Recurrent Networks with Synaptic Depression , 2002, Journal of Computational Neuroscience.

[14]  M. Luescher,et al.  A Portable High-quality Random Number Generator for Lattice Field Theory Simulations , 1993 .

[15]  Henry Markram,et al.  Neural Networks with Dynamic Synapses , 1998, Neural Computation.

[16]  Si Wu,et al.  Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility , 2011, Neural Computation.

[17]  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.

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

[19]  Peter Dayan,et al.  Distributional Population Codes and Multiple Motion Models , 1998, NIPS.

[20]  B Moulden,et al.  A Simultaneous Shift in Apparent Direction: Further Evidence for a “Distribution-Shift” Model of Direction Coding , 1980, The Quarterly journal of experimental psychology.

[21]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[22]  D C Van Essen,et al.  Functional properties of neurons in middle temporal visual area of the macaque monkey. I. Selectivity for stimulus direction, speed, and orientation. , 1983, Journal of neurophysiology.

[23]  Peter Dayan,et al.  Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity , 2003, Neural Computation.

[24]  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.

[25]  William Curran,et al.  Directional performance in motion transparency , 2002, Vision Research.

[26]  C. Stevens,et al.  Facilitation and depression at single central synapses , 1995, Neuron.

[27]  W. Wildman,et al.  Theoretical Neuroscience , 2014 .

[28]  Kazuyuki Aihara,et al.  Bayesian Inference Explains Perception of Unity and Ventriloquism Aftereffect: Identification of Common Sources of Audiovisual Stimuli , 2007, Neural Computation.

[29]  P. Dayan,et al.  Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials , 2010, Nature Neuroscience.

[30]  Zachary P. Kilpatrick.  Short term synaptic depression improves information transfer in perceptual multistability , 2013, Front. Comput. Neurosci..

[31]  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.

[32]  Mark C. Fuhs,et al.  A Spin Glass Model of Path Integration in Rat Medial Entorhinal Cortex , 2006, The Journal of Neuroscience.

[33]  Misha Tsodyks,et al.  The Emergence of Up and Down States in Cortical Networks , 2006, PLoS Comput. Biol..

[34]  Si Wu,et al.  Delay Compensation with Dynamical Synapses , 2012, NIPS.

[35]  L. Abbott,et al.  Synaptic Depression and Cortical Gain Control , 1997, Science.

[36]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

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