Abstract
We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 226 (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 236 (64G) synaptic adaptors on a current high-end FPGA platform.
Highlights
Plastic synapses, i.e., synapses that can adapt their gain according to one or more adaptation rules, are extremely important in neural systems, as it is generally accepted that learning in the brain arises from synaptic modifications
We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP)
In our previous work (Wang et al, 2011b, 2012), a delay adaptation algorithm, Spike Timing Dependent Delay Plasticity (STDDP), inspired by STDP was developed to fine-tune delays that had been programmed into the network
Summary
I.e., synapses that can adapt their gain according to one or more adaptation rules, are extremely important in neural systems, as it is generally accepted that learning in the brain arises from synaptic modifications. The Spike Timing Dependent Plasticity (STDP) algorithm (Gerstner et al, 1996; Magee, 1997; Markram et al, 1997; Bi and Poo, 1998), which is one of the adaptation rules observed in biology, modulates the weight of a synapse based on the relative timing between the pre-synaptic spike and the post-synaptic spike. Some observations suggest that the propagation delays of neural spikes, as they are transmitted from one neuron to another, may be adaptive (Stanford, 1987). In our previous work (Wang et al, 2011b, 2012), a delay adaptation algorithm, Spike Timing Dependent Delay Plasticity (STDDP), inspired by STDP was developed to fine-tune delays that had been programmed into the network. We recently showed that the time delays of neural spike propagation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
