RT journal article
T1 Digital neuromorphic real-time platform
A1 Pérez Peña, Fernando
A1 Cifredo Chacón, María Ángeles
A1 Quirós Olozábal, Ángel
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 Spiking neural network
K1 Neuromorphic engineering
K1 Leaky-integrate and fire
K1 FPGA
K1 Brian simulator
AB Hardware implementations of spiking neural networks in portable devices can improve many applications of robotics, neurorobotics or prosthetic fields in terms of power consumption, high-speed processing and learning mechanisms. Analog and digital platforms have been previously proposed to run these networks. Analog designs are closer to biology since they implement the original mathematical model. However, digital platforms are, to some extent, abstractions of this model so far. In this paper, a full digital platform to design, implement and run real-time analog-like spiking neural networks is presented. Specifically, we present the design and implementation of digital circuits to run real-time biologically plausible spiking neural networks on a Field Programmable Gate Array (FPGA). The circuit designed for the neuron implements the Leaky Integrate and Fire (LIF) model. The synapsis implemented is a bi-exponential current-based one. The synaptic circuit design consists of one static memory with the baseline current and a dynamic memory which stores the updated contribution over time of each pre-synaptic connection. All the parameters of both the neuron and the synapse are configurable. The results of the circuits are validated by running the same experiments on the Brian simulator. The circuits, which are totally original and independent of the technology, use only 136 slice registers of hardware resources. Thus, these designs allow the scale of the network. These circuits aim to be the basis of the spiking neural networks on digital devices. This platform allows the user to first simulate their network within the Brian simulator and then, confidently, move to the hardware platform replicating the same performance or even replace their analog platform with the digital one.
PB Elsevier
SN 0925-2312
YR 2020
FD 2020-01-01
LK http://hdl.handle.net/10498/34500
UL http://hdl.handle.net/10498/34500
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026