skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems

Frontiers in neuroinformatics, 2014-08, Vol.8, p.73-73 [Peer Reviewed Journal]

2014. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Copyright © 2014 Stefanini, Neftci, Sheik and Indiveri. 2014 ;ISSN: 1662-5196 ;EISSN: 1662-5196 ;EISSN: 1662-453X ;DOI: 10.3389/fninf.2014.00073 ;PMID: 25232314

Full text available

Citations Cited by
  • Title:
    PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems
  • Author: Stefanini, Fabio ; Neftci, Emre O ; Sheik, Sadique ; Indiveri, Giacomo
  • Subjects: AER ; analog ; Cognitive ability ; Communication ; Computer engineering ; Computer programs ; Decision making ; Experiments ; Firing pattern ; Interfaces ; Mental task performance ; Motor systems ; Neural networks ; Neuroscience ; python ; Real-time ; Software ; Spiking Neural network ; VLSI
  • Is Part Of: Frontiers in neuroinformatics, 2014-08, Vol.8, p.73-73
  • Description: Neuromorphic hardware offers an electronic substrate for the realization of asynchronous event-based sensory-motor systems and large-scale spiking neural network architectures. In order to characterize these systems, configure them, and carry out modeling experiments, it is often necessary to interface them to workstations. The software used for this purpose typically consists of a large monolithic block of code which is highly specific to the hardware setup used. While this approach can lead to highly integrated hardware/software systems, it hampers the development of modular and reconfigurable infrastructures thus preventing a rapid evolution of such systems. To alleviate this problem, we propose PyNCS, an open-source front-end for the definition of neural network models that is interfaced to the hardware through a set of Python Application Programming Interfaces (APIs). The design of PyNCS promotes modularity, portability and expandability and separates implementation from hardware description. The high-level front-end that comes with PyNCS includes tools to define neural network models as well as to create, monitor and analyze spiking data. Here we report the design philosophy behind the PyNCS framework and describe its implementation. We demonstrate its functionality with two representative case studies, one using an event-based neuromorphic vision sensor, and one using a set of multi-neuron devices for carrying out a cognitive decision-making task involving state-dependent computation. PyNCS, already applicable to a wide range of existing spike-based neuromorphic setups, will accelerate the development of hybrid software/hardware neuromorphic systems, thanks to its code flexibility. The code is open-source and available online at https://github.com/inincs/pyNCS.
  • Publisher: Switzerland: Frontiers Research Foundation
  • Language: English
  • Identifier: ISSN: 1662-5196
    EISSN: 1662-5196
    EISSN: 1662-453X
    DOI: 10.3389/fninf.2014.00073
    PMID: 25232314
  • Source: GFMER Free Medical Journals
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait