Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems

By Elisabetta Chicca, Fabio Stefanini, Chiara Bartolozzi, Giacomo Indiveri. Proceedings of the IEEE, 2014

487 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/73e667e8c75289fbe5159b06e7b4e6bd38576ed3

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In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE) (2018, background): “This corresponds to a learning update that is gated by the post-synaptic membrane potential, and is reminiscent of membrane voltage-based rules, where spike-driven plasticity is induced only when membrane voltage is inside an eligibility window (Brader et al., 2007; Chicca et al., 2013).”
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses (2015, methodology): “…of detailed neural models, using mixed signal analog-digital circuits to reproduce faithfully neural and synaptic dynamics, in a basic research eﬀort to understand the principles of neural computation in physical hardware systems (Douglas et al., 1995; Liu et al., 2002; Chicca et al., 2014).”
Memory and Information Processing in Neuromorphic Systems (2015, background): “I. INTRODUCTION Neuromorphic information processing systems consist of electronic circuits and devices built using design principles that are based on those of biological nervous systems [1], [2], [3], [4].”
A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs) (2017, methodology): “and to implement biophysically realistic neural and synaptic behaviors, with biologically plausible temporal dynamics [17].”