NDLI: Hardware efficient, neuromorphic dendritically enhanced readout for liquid state machines

Content Provider	IEEE Xplore Digital Library
Author	Roy, S. Basu, A. Hussain, S.
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore (Roy, S.; Basu, A.; Hussain, S.)
Abstract	In this article, we describe a new neuro-inspired, hardware-friendly readout stage for the liquid state machine (LSM) that is suitable for on-sensor computing in resource constrained applications. Compared to the state of the art parallel perceptron readout (PPR), our readout architecture and learning algorithm can attain better performance with significantly less synaptic resources making it attractive for VLSI implementation. Inspired by the nonlinear properties of dendrites in biological neurons, our readout stage incorporates neurons having multiple dendrites with a lumped nonlinearity (two compartment model). The number of synaptic connections on each branch is significantly lower than the total number of connections from the liquid neurons and the learning algorithm tries to find the best `combination' of input connections on each branch to reduce the error. Hence, the learning involves network rewiring (NRW) of the readout network similar to structural plasticity observed in its biological counterparts. We show that even while using binary synapses, our method can achieve 2.4 - 3.3 times less error compared to PPR using same number of high resolution synapses. Conversely, PPR requires 40-60 times more synapses to attain error levels comparable to our method.
Starting Page	302
Ending Page	305
File Size	1420741
Page Count	4
File Format	PDF
ISBN	9781479914715
DOI	10.1109/BioCAS.2013.6679699
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-31
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Liquids Microprocessors Neurons Computer architecture Hardware Approximation methods
Content Type	Text
Resource Type	Article

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