Literature DB >> 30553486

Insect-inspired neuromorphic computing.

Thomas Dalgaty1, Elisa Vianello2, Barbara De Salvo2, Jerome Casas3.   

Abstract

The steady improvement in the performance of computing systems seen for many decades is levelling off as the miniaturization of semiconducting technology approaches the atomic limit, facing severe physical and technological issues. Neuromorphic computing is an emerging solution which makes use of silicon technology in a different way, inline with the computational principles observed in animal nervous systems. In this article, we argue that the nervous systems of insects in particular offer themselves as an ideal starting point for incorporation into realistic neuromorphic systems and review research in developing insect-inspired neuromorphic systems. We conclude with an exciting yet tangible vision of a full neuromorphic sensory-motor system where a liquid state machine modelling the function of the insect mushroom body links input to output and allows for amalgamation of the work discussed in a hierarchical framework of a full system inspired by the concept of how information flows through insects.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Year:  2018        PMID: 30553486     DOI: 10.1016/j.cois.2018.09.006

Source DB:  PubMed          Journal:  Curr Opin Insect Sci            Impact factor:   5.186


  3 in total

1.  Neuromorphic object localization using resistive memories and ultrasonic transducers.

Authors:  Filippo Moro; Emmanuel Hardy; Bruno Fain; Thomas Dalgaty; Paul Clémençon; Alessio De Prà; Eduardo Esmanhotto; Niccolò Castellani; François Blard; François Gardien; Thomas Mesquida; François Rummens; David Esseni; Jérôme Casas; Giacomo Indiveri; Melika Payvand; Elisa Vianello
Journal:  Nat Commun       Date:  2022-06-18       Impact factor: 17.694

2.  Ear-Bot: Locust Ear-on-a-Chip Bio-Hybrid Platform.

Authors:  Idan Fishel; Yoni Amit; Neta Shvil; Anton Sheinin; Amir Ayali; Yossi Yovel; Ben M Maoz
Journal:  Sensors (Basel)       Date:  2021-01-01       Impact factor: 3.576

3.  Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network.

Authors:  Mohammad H Hasan; Amin Abbasalipour; Hamed Nikfarjam; Siavash Pourkamali; Muhammad Emad-Ud-Din; Roozbeh Jafari; Fadi Alsaleem
Journal:  Micromachines (Basel)       Date:  2021-03-05       Impact factor: 2.891

  3 in total

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