A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System

Persistent activity refers to the phenomenon that cortical neurons keep firing even after the stimulus triggering the initial neuronal responses is moved. Persistent activity is widely believed to be the substrate for a neural system retaining a memory trace of the stimulus information. In a conventional view, persistent activity is regarded as an attractor of the network dynamics, but it faces a challenge of how to be closed properly. Here, in contrast to the view of attractor, we consider that the stimulus information is encoded in a marginally unstable state of the network which decays very slowly and exhibits persistent firing for a prolonged duration. We propose a simple yet effective mechanism to achieve this goal, which utilizes the property of short-term plasticity (STP) of neuronal synapses. STP has two forms, short-term depression (STD) and short-term facilitation (STF), which have opposite effects on retaining neuronal responses. We find that by properly combining STF and STD, a neural system can hold persistent activity of graded lifetime, and that persistent activity fades away naturally without relying on an external drive. The implications of these results on neural information representation are discussed.

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

[2]  P. Goldman-Rakic,et al.  Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. , 1989, Journal of neurophysiology.

[3]  Anatol C. Kreitzer,et al.  Interplay between Facilitation, Depression, and Residual Calcium at Three Presynaptic Terminals , 2000, The Journal of Neuroscience.

[4]  Thomas K. Berger,et al.  Heterogeneity in the pyramidal network of the medial prefrontal cortex , 2006, Nature Neuroscience.

[5]  W. Regehr,et al.  Short-term synaptic plasticity. , 2002, Annual review of physiology.

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

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

[8]  Daniel J. Amit,et al.  Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .

[9]  G. E. Alexander,et al.  Neuron Activity Related to Short-Term Memory , 1971, Science.

[10]  Misha Tsodyks,et al.  Correction: Persistent Activity in Neural Networks with Dynamic Synapses , 2007, PLoS Comput. Biol..

[11]  X. Wang,et al.  Synaptic Basis of Cortical Persistent Activity: the Importance of NMDA Receptors to Working Memory , 1999, The Journal of Neuroscience.

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

[13]  M. Tsodyks,et al.  Synaptic Theory of Working Memory , 2008, Science.

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

[15]  L. Abbott,et al.  Synaptic computation , 2004, Nature.

[16]  H. Markram,et al.  Differential signaling via the same axon of neocortical pyramidal neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Hilbert J. Kappen,et al.  Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks , 2006, Neural Computation.

[18]  Boris S. Gutkin,et al.  Turning On and Off with Excitation: The Role of Spike-Timing Asynchrony and Synchrony in Sustained Neural Activity , 2001, Journal of Computational Neuroscience.

[19]  R. Romo,et al.  Neuronal correlates of parametric working memory in the prefrontal cortex , 1999, Nature.