Multi-Layer Neuromorphic Synapse for Reconfigurable Networks

In pulse-based neural networks, synaptic dynamics can have direct influence on learning of neural codes, and encoding of spatiotemporal spike patterns. In this paper, we propose an adaptive synapse circuit for increased flexibility and efficacy of signal processing units in neuromorphic structures. The synapse acts as a multi-layer computational network, and includes multi-compartment dendrites and different types of post-synaptic back propagating signals. With built-in temporal control mechanisms, the resulting reconfigurable network allows the implementation of synaptic homeostatics.

[1]  D. Buonomano,et al.  Differential Effects of Excitatory and Inhibitory Plasticity on Synaptically Driven Neuronal Input-Output Functions , 2009, Neuron.

[2]  Chiara Bartolozzi,et al.  Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems , 2014, Proceedings of the IEEE.

[3]  Chiara Bartolozzi,et al.  Synaptic Dynamics in Analog VLSI , 2007, Neural Computation.

[4]  D. Buonomano,et al.  Cortical plasticity: from synapses to maps. , 1998, Annual review of neuroscience.

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

[6]  Wulfram Gerstner,et al.  A Model of Synaptic Reconsolidation , 2016, Front. Neurosci..

[7]  Yingxue Wang,et al.  A Two-Dimensional Configurable Active Silicon Dendritic Neuron Array , 2011, IEEE Transactions on Circuits and Systems I: Regular Papers.

[8]  Nace L. Golding,et al.  Dendritic Sodium Spikes Are Variable Triggers of Axonal Action Potentials in Hippocampal CA1 Pyramidal Neurons , 1998, Neuron.

[9]  U. Frey,et al.  Synaptic tagging and long-term potentiation , 1997, Nature.

[10]  Narayan Srinivasa,et al.  Energy-Efficient Neuron, Synapse and STDP Integrated Circuits , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[11]  Niraj S. Desai,et al.  Activity-dependent scaling of quantal amplitude in neocortical neurons , 1998, Nature.

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

[13]  Ramón Huerta,et al.  On the equivalence of Hebbian learning and the SVM formalism , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[14]  Ph. Häfliger,et al.  Spike Based Normalizing Hebbian Learning in an Analog VLSI Artificial Neuron , 1999, NIPS 1999.

[15]  I Segev,et al.  Untangling dendrites with quantitative models. , 2000, Science.

[16]  Joseph E LeDoux,et al.  Reply — reconsolidation: The labile nature of consolidation theory , 2000, Nature Reviews Neuroscience.

[17]  G. Stuart,et al.  Role of dendritic synapse location in the control of action potential output , 2003, Trends in Neurosciences.

[18]  Minija Tamosiunaite,et al.  Self-influencing synaptic plasticity: Recurrent changes of synaptic weights can lead to specific functional properties , 2007, Journal of Computational Neuroscience.

[19]  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.

[20]  Minija Tamosiunaite,et al.  Erratum to: Self-influencing synaptic plasticity: recurrent changes of synaptic weights can lead to specific functional properties , 2010, Journal of Computational Neuroscience.

[21]  Tomoki Fukai,et al.  Computational Implications of Lognormally Distributed Synaptic Weights , 2014, Proceedings of the IEEE.