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A Neuromorphic Computational Primitive for Robust Context-Dependent Decision Making and Context-Dependent Stochastic Computation


Liang, Dongchen; Indiveri, Giacomo (2019). A Neuromorphic Computational Primitive for Robust Context-Dependent Decision Making and Context-Dependent Stochastic Computation. IEEE Transactions on Circuits and Systems. Part 2: Express Briefs, 66(5):843-847.

Abstract

The prefrontal cortex (PFC) plays an important role in complex cognitive computations, including planning and decision making. Although recurrent spiking neural network (SNN) software models of PFC have been successful in reproducing many of its cognitive computational aspects, little attention has been devoted to the question of how such systems can perform with low-resolution parameters and be robust to noise and variability in their input signals and state variables. Here, we present a mixed-signal analog/digital neuromorphic implementation of a state-dependent SNN architecture that addresses these issues by construction. The network relies on synaptic dis-inhibition to ensure robust decision making even in the face of very large variability. Depending on its connectivity, the network can either perform robustly in a deterministic way or exploit the device mismatch and noise to explore stochastically multiple states in constraint satisfaction problems (CSPs). We validate the architecture by mapping it onto a network of spiking neurons in a multi-core mixed-signal neuromorphic system and presenting experimental results for three different examples of CSPs.

Abstract

The prefrontal cortex (PFC) plays an important role in complex cognitive computations, including planning and decision making. Although recurrent spiking neural network (SNN) software models of PFC have been successful in reproducing many of its cognitive computational aspects, little attention has been devoted to the question of how such systems can perform with low-resolution parameters and be robust to noise and variability in their input signals and state variables. Here, we present a mixed-signal analog/digital neuromorphic implementation of a state-dependent SNN architecture that addresses these issues by construction. The network relies on synaptic dis-inhibition to ensure robust decision making even in the face of very large variability. Depending on its connectivity, the network can either perform robustly in a deterministic way or exploit the device mismatch and noise to explore stochastically multiple states in constraint satisfaction problems (CSPs). We validate the architecture by mapping it onto a network of spiking neurons in a multi-core mixed-signal neuromorphic system and presenting experimental results for three different examples of CSPs.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Electrical and Electronic Engineering
Language:English
Date:1 May 2019
Deposited On:14 Feb 2020 10:17
Last Modified:29 Jul 2020 14:14
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1549-7747
Additional Information:© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
OA Status:Green
Publisher DOI:https://doi.org/10.1109/tcsii.2019.2907848
Project Information:
  • : FunderH2020
  • : Grant ID724295
  • : Project TitleNeuroAgents - Neuromorphic Electronic Agents: from sensory processing to autonomous cognitive behavior

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