A Novel Hardware-Efficient Asynchronous Cellular Automaton Model of Spike-Timing-Dependent Synaptic Plasticity

The spike-timing-dependent synaptic plasticity (STDP) is a form of change of a synaptic weight depending on timings of pre- and post synaptic spikes. In this brief, a novel asynchronous cellular automaton model of the STDP is presented. It is shown that the presented model can reproduce various STDP characteristics observed in physiological experiments. Also, the presented model is implemented in a field-programmable gate array, and laboratory experiments validate the reproductions of the physiological STDP characteristics. It is then shown that the presented model consumes much less hardware resources than a computationally efficient differential equation model of the STDP.

[1]  David W. Nauen,et al.  Coactivation and timing-dependent integration of synaptic potentiation and depression , 2005, Nature Neuroscience.

[2]  Takashi Matsubara,et al.  Bifurcation-based synthesis of asynchronous cellular automaton based neuron , 2013 .

[3]  Tetsuya Hishiki,et al.  A Novel Rotate-and-Fire Digital Spiking Neuron and its Neuron-Like Bifurcations and Responses , 2011, IEEE Transactions on Neural Networks.

[4]  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.

[5]  Y. Dan,et al.  Spike-timing-dependent synaptic modification induced by natural spike trains , 2002, Nature.

[6]  Mostafa Rahimi Azghadi,et al.  Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges , 2014, Proceedings of the IEEE.

[7]  B. Sakmann,et al.  Spine Ca2+ Signaling in Spike-Timing-Dependent Plasticity , 2006, The Journal of Neuroscience.

[8]  Zhen Liu,et al.  Synaptic long-term potentiation realized in Pavlov's dog model based on a NiOx-based memristor , 2014 .

[9]  S. Schultz.  Principles of Neural Science, 4th ed. , 2001 .

[10]  P. J. Sjöström,et al.  Rate, Timing, and Cooperativity Jointly Determine Cortical Synaptic Plasticity , 2001, Neuron.

[11]  P. Bearman,et al.  Correction for Graupner and Brunel, Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location , 2012, Proceedings of the National Academy of Sciences.

[12]  Hiroyuki Torikai,et al.  A Calcium-Based Simple Model of Multiple Spike Interactions in Spike-Timing-Dependent Plasticity , 2013, Neural Computation.

[13]  Takashi Matsubara,et al.  Asynchronous Cellular Automaton-Based Neuron: Theoretical Analysis and On-FPGA Learning , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Mostafa Rahimi Azghadi,et al.  A neuromorphic VLSI design for spike timing and rate based synaptic plasticity , 2013, Neural Networks.

[15]  P. J. Sjöström,et al.  A Cooperative Switch Determines the Sign of Synaptic Plasticity in Distal Dendrites of Neocortical Pyramidal Neurons , 2006, Neuron.

[16]  Hiroyuki Torikai,et al.  A Novel Asynchronous Cellular Automaton Multicompartment Neuron Model , 2015, IEEE Transactions on Circuits and Systems II: Express Briefs.

[17]  Richard A. Chapman,et al.  Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Ronald Tetzlaff,et al.  Neuronal Synapse as a Memristor: Modeling Pair- and Triplet-Based STDP Rule , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[19]  Tetsuya Hishiki,et al.  A Generalized Rotate-and-Fire Digital Spiking Neuron Model and Its On-FPGA Learning , 2011, IEEE Transactions on Circuits and Systems II: Express Briefs.