Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation.

Optical spike-timing-dependent plasticity (STDP) synapses form the basis of learning in photonic neuromorphic system. In biological neural systems, STDP synapses generally have multiplicative boundary mechanisms, and can be modulated by a third factor such as dopamine. Analogously, we introduce a third factor into optical STDP: The current-injection of semiconductor optical amplifiers can be modified in an adaptive way according to local or global feedback signals. The local one is present synaptic weight, which elicits an optical weight-dependent STDP, while the global one is a reward signal. We demonstrate that the optical weight-dependent STDP can emulate the behavior of biological STDP synapses more closely, and can be seen as an intermediate configuration between additive and multiplicative STDP, which balances stability and competition among synapses. Simulation studies with scalable photonic neurons further show that optical STDP with reward modulation enables reward-based reinforcement learning.

[1]  Jean-Claude Simon,et al.  Gain recovery dynamics in semiconductor optical amplifier , 2001 .

[2]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[3]  Daniel D. Lee,et al.  Equilibrium properties of temporally asymmetric Hebbian plasticity. , 2000, Physical review letters.

[4]  Di Liang,et al.  A distributed feedback silicon evanescent laser. , 2008, Optics express.

[5]  J. Seamans,et al.  The principal features and mechanisms of dopamine modulation in the prefrontal cortex , 2004, Progress in Neurobiology.

[6]  P. J. Sjöström,et al.  Dendritic excitability and synaptic plasticity. , 2008, Physiological reviews.

[7]  Omri Raday,et al.  A hybrid AlGaInAs-silicon evanescent waveguide photodetector. , 2007, Optics express.

[8]  P. R. Prucnal,et al.  A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing , 2013, IEEE Journal of Selected Topics in Quantum Electronics.

[9]  Don Monroe,et al.  A new type of mathematics? , 2014, CACM.

[10]  H. Seung,et al.  Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission , 2003, Neuron.

[11]  Paul R. Prucnal,et al.  Simulations of a graphene excitable laser for spike processing , 2014 .

[12]  Paul R Prucnal,et al.  Ultrafast all-optical implementation of a leaky integrate-and-fire neuron. , 2011, Optics express.

[13]  Matthieu Gilson,et al.  Stability versus Neuronal Specialization for STDP: Long-Tail Weight Distributions Solve the Dilemma , 2011, PloS one.

[14]  Johannes Schemmel,et al.  Reward-based learning under hardware constraints—using a RISC processor embedded in a neuromorphic substrate , 2013, Front. Neurosci..

[15]  R Kuszelewicz,et al.  Relative refractory period in an excitable semiconductor laser. , 2014, Physical review letters.

[16]  Areejit Samal,et al.  STDP-driven networks and the C. elegans neuronal network , 2010, 1004.5060.

[17]  M. Farries,et al.  Reinforcement learning with modulated spike timing dependent synaptic plasticity. , 2007, Journal of neurophysiology.

[18]  Trevor Bekolay,et al.  A Large-Scale Model of the Functioning Brain , 2012, Science.

[19]  Wulfram Gerstner,et al.  Spike-timing dependent plasticity , 2010, Scholarpedia.

[20]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[21]  Yue Tian,et al.  Pulse lead/lag timing detection for adaptive feedback and control based on optical spike-timing-dependent plasticity. , 2013, Optics letters.

[22]  Salvador Balle,et al.  Excitability and optical pulse generation in semiconductor lasers driven by resonant tunneling diode photo-detectors. , 2013, Optics express.

[23]  Sylvain Barbay,et al.  Excitability in a semiconductor laser with saturable absorber. , 2011, Optics letters.

[24]  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.