@misc{10498/26691,
year = {2022},
url = {http://hdl.handle.net/10498/26691},
abstract = {Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) is a learning method for Spiking Neural Network (SNN) that makes use of an external learning signal to modulate the synaptic plasticity produced by Spike-Timing-Dependent Plasticity (STDP). Combining the advantages of reinforcement learning and the biological plausibility of STDP, online learning on SNN in real-world scenarios can be applied. This paper presents a fully digital architecture, implemented on an Field-Programmable Gate Array (FPGA), including the R-STDP learning mechanism in a SNN. The hardware results obtained are comparable to the software simulations results using the Brian2 simulator. The maximum error is of 0.083 when a 14-bits fix-point precision is used in realtime. The presented architecture shows an accuracy of 95% when tested in an obstacle avoidance problem on mobile robotics with a minimum use of resources.},
publisher = {SPRINGER},
keywords = {R-STDP},
keywords = {STDP},
keywords = {Synaptic plasticity},
keywords = {Neuromorphic system},
keywords = {FPGA},
keywords = {Spiking neural network},
title = {Bio-plausible digital implementation of a reward modulated STDP synapse},
doi = {10.1007/s00521-022-07220-6},
author = {Quintana Velázquez, Fernando Manuel and Pérez Peña, Fernando and Galindo Riaño, Pedro Luis},
}