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Wide dynamic range weights and biologically realistic synaptic dynamics for spike-based learning circuits


Sumislawska, Dora; Qiao, Ning; Pfeiffer, Michael; Indiveri, Giacomo (2016). Wide dynamic range weights and biologically realistic synaptic dynamics for spike-based learning circuits. In: IEEE International Symposium on Circuits and Systems (ISCAS) 2016, Montreal, Canada, 22 May 2016 - 25 May 2016, 2491-2494.

Abstract

Spike-based neuromorphic learning circuits typically represent their synaptic weights as voltages, and convert them into post-synaptic currents so that they can be integrated by their afferent silicon neuron. This voltage-to-current conversion is often done using a single transistor. This results in an exponential (for weak-inversion) or quadratic (for strong inversion) non-linear transformation which severely restricts the type of learning algorithms that can be implemented. To overcome this problem we propose a range of solutions that perform a linear transformation fro m weight voltage to synaptic current, simplifying the implementation of a spike-based learning rules. We demonstrate the application of these conversion circuits using current-mode integrators that produce alpha-functions with biologically realistic temporal dynamics and amplitudes that are linearly proportional to the synaptic weights. The circuits proposed are low-power, and can be integrated in a wide range of spike-based learning framework s that have been recently proposed. We describe the advantages and disadvantages of the various solutions proposed and validate them with circuit simulation results.

Abstract

Spike-based neuromorphic learning circuits typically represent their synaptic weights as voltages, and convert them into post-synaptic currents so that they can be integrated by their afferent silicon neuron. This voltage-to-current conversion is often done using a single transistor. This results in an exponential (for weak-inversion) or quadratic (for strong inversion) non-linear transformation which severely restricts the type of learning algorithms that can be implemented. To overcome this problem we propose a range of solutions that perform a linear transformation fro m weight voltage to synaptic current, simplifying the implementation of a spike-based learning rules. We demonstrate the application of these conversion circuits using current-mode integrators that produce alpha-functions with biologically realistic temporal dynamics and amplitudes that are linearly proportional to the synaptic weights. The circuits proposed are low-power, and can be integrated in a wide range of spike-based learning framework s that have been recently proposed. We describe the advantages and disadvantages of the various solutions proposed and validate them with circuit simulation results.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:25 May 2016
Deposited On:26 Jan 2017 14:45
Last Modified:29 Jan 2017 07:47
Publisher:Institute of Electrical and Electronics Engineers
Series Name:IEEE International Symposium on Circuits and Systems (ISCAS)
Number of Pages:4
ISSN:2379-447X
ISBN:978-1-4799-5341-7
Free access at:Official URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/ISCAS.2016.7539098
Official URL:http://ncs.ethz.ch/pubs/pdf/Sumislawska_etal16.pdf
Related URLs:http://iscas2016.org/ (Organisation)

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