2014/03/25 by Elisabetta Chicca, Fabio Stefanini, Chiara Bartolozzi +1 · 1 voice · 5 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Neural Networks and Reservoir Computing #Neural dynamics and brain function #cs.ET #q-bio.NC
paper · pdf · doi:10.1109/jproc.2014.2313954
published as Proceedings of IEEE, 102:9, (2014), pg. 1367-1388 · Submitted to Proceedings of IEEE, spiking neural network implementations in full custom VLSI
arxiv created 2014/03/25 · arxiv published 2014/03/25 · openalex publication_date 2014/05/01 · arxiv updated 2017/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real-world and exhibit cognitive abilities still remains open. In this paper we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real-time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them; we describe the computational properties of recurrent neural networks and show how neuromorphic Winner-Take-All circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition.