RT journal article
T1 Bio-plausible digital implementation of a reward modulated STDP synapse
A1 Quintana Velázquez, Fernando Manuel
A1 Pérez Peña, Fernando
A1 Galindo Riaño, Pedro Luis
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
A2 Ingeniería Informática
K1 R-STDP
K1 STDP
K1 Synaptic plasticity
K1 Neuromorphic system
K1 FPGA
K1 Spiking neural network
AB 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.
PB SPRINGER
SN 0941-0643
YR 2022
FD 2022
LK http://hdl.handle.net/10498/26691
UL http://hdl.handle.net/10498/26691
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026