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PAX: A mixed hardware/software simulation platform for spiking neural networks

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Title: PAX: A mixed hardware/software simulation platform for spiking neural networks
Authors: Renaud, S.1 sylvie.renaud@ims-bordeaux.fr, Tomas, J.1, Lewis, N.1, Bornat, Y.1, Daouzli, A.1, Rudolph, M.2, Destexhe, A.2, Saïghi, S.1
Source: Neural Networks. Sep2010, Vol. 23 Issue 7, p905-916. 12p.
Subjects: Artificial neural networks, Computer input-output equipment, Computer software, Computer simulation, Integrated circuits, Computer science, Neurobiology, Bionics
Abstract: Abstract: Many hardware-based solutions now exist for the simulation of bio-like neural networks. Less conventional than software-based systems, these types of simulators generally combine digital and analog forms of computation. In this paper we present a mixed hardware–software platform, specifically designed for the simulation of spiking neural networks, using conductance-based models of neurons and synaptic connections with dynamic adaptation rules (Spike-Timing-Dependent Plasticity). The neurons and networks are configurable, and are computed in ‘biological real time’ by which we mean that the difference between simulated time and simulation time is guaranteed lower than 50 μs. After presenting the issues and context involved in the design and use of hardware-based spiking neural networks, we describe the analog neuromimetic integrated circuits which form the core of the platform. We then explain the organization and computation principles of the modules within the platform, and present experimental results which validate the system. Designed as a tool for computational neuroscience, the platform is exploited in collaborative research projects together with neurobiology and computer science partners. [Copyright &y& Elsevier]
Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: PAX: A mixed hardware/software simulation platform for spiking neural networks
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  Data: <searchLink fieldCode="AR" term="%22Renaud%2C+S%2E%22">Renaud, S.</searchLink><relatesTo>1</relatesTo><i> sylvie.renaud@ims-bordeaux.fr</i><br /><searchLink fieldCode="AR" term="%22Tomas%2C+J%2E%22">Tomas, J.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lewis%2C+N%2E%22">Lewis, N.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Bornat%2C+Y%2E%22">Bornat, Y.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Daouzli%2C+A%2E%22">Daouzli, A.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Rudolph%2C+M%2E%22">Rudolph, M.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Destexhe%2C+A%2E%22">Destexhe, A.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Saïghi%2C+S%2E%22">Saïghi, S.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Neural+Networks%22">Neural Networks</searchLink>. Sep2010, Vol. 23 Issue 7, p905-916. 12p.
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  Data: Abstract: Many hardware-based solutions now exist for the simulation of bio-like neural networks. Less conventional than software-based systems, these types of simulators generally combine digital and analog forms of computation. In this paper we present a mixed hardware–software platform, specifically designed for the simulation of spiking neural networks, using conductance-based models of neurons and synaptic connections with dynamic adaptation rules (Spike-Timing-Dependent Plasticity). The neurons and networks are configurable, and are computed in ‘biological real time’ by which we mean that the difference between simulated time and simulation time is guaranteed lower than 50 μs. After presenting the issues and context involved in the design and use of hardware-based spiking neural networks, we describe the analog neuromimetic integrated circuits which form the core of the platform. We then explain the organization and computation principles of the modules within the platform, and present experimental results which validate the system. Designed as a tool for computational neuroscience, the platform is exploited in collaborative research projects together with neurobiology and computer science partners. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.neunet.2010.02.006
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