Network Models of Neural Information Processing

Neurons and synapses are the basic building units of the brain. They form neural circuits of various structures and hence implement different functions. Understanding how neural networks achieve brain functions is at the core of modeling studies. In this chapter, we will introduce some network models, including classical Hopfield model, continuous attractor neural network, and reservoir network. We will also discuss the studies on how short-term plasticity of neuronal synapses affects the dynamics and computations of a neural network.

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

[2]  Boris S. Gutkin,et al.  Spike frequency adaptation , 2014, Scholarpedia.

[3]  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.

[4]  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.

[5]  Xiao-Jing Wang,et al.  Probabilistic Decision Making by Slow Reverberation in Cortical Circuits , 2002, Neuron.

[6]  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.

[7]  W. Maass,et al.  State-dependent computations: spatiotemporal processing in cortical networks , 2009, Nature Reviews Neuroscience.

[8]  Tatsuo K Sato,et al.  Traveling Waves in Visual Cortex , 2012, Neuron.

[9]  Michael J. Berry,et al.  Anticipation of moving stimuli by the retina , 1999, Nature.

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

[11]  Si Wu,et al.  Short-term synaptic plasticity , 2013, Scholarpedia.

[12]  Xiao-Jing Wang Decision Making in Recurrent Neuronal Circuits , 2008, Neuron.

[13]  Kanter,et al.  Associative recall of memory without errors. , 1987, Physical review. A, General physics.

[14]  Adam Kepecs,et al.  Seeing at a glance, smelling in a whiff: rapid forms of perceptual decision making , 2006, Nature Reviews Neuroscience.

[15]  Andreas V. M. Herz,et al.  A Universal Model for Spike-Frequency Adaptation , 2003, Neural Computation.

[16]  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.

[17]  D. Touretzky,et al.  Modeling attractor deformation in the rodent head-direction system. , 2000, Journal of neurophysiology.

[18]  Sompolinsky,et al.  Storing infinite numbers of patterns in a spin-glass model of neural networks. , 1985, Physical review letters.

[19]  J. Gold,et al.  The neural basis of decision making. , 2007, Annual review of neuroscience.

[20]  Sompolinsky,et al.  Information storage in neural networks with low levels of activity. , 1987, Physical review. A, General physics.

[21]  Si Wu,et al.  Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks , 2014, NIPS.

[22]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[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]  Si Wu,et al.  Long-period rhythmic synchronous firing in a scale-free network , 2013, Proceedings of the National Academy of Sciences.

[25]  L. F. Abbott,et al.  Generating Coherent Patterns of Activity from Chaotic Neural Networks , 2009, Neuron.

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

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