Biologically inspired spiking neurons: Piecewise linear models and digital implementation

Soleimani, H ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1109/TCSI.2012.2206463
  3. Publisher: 2012
  4. Abstract:
  5. There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation. Hardware synthesis and physical implementations on FPGA show that the proposed models can produce precise neural behaviors with higher performance and considerably lower implementation costs compared with the original model. Accordingly, a compact structure of the models which can be trained with supervised and unsupervised learning algorithms has been developed. Using this structure and based on a spike rate coding, a character recognition case study has been implemented and tested
  6. Keywords:
  7. piecewise linear model ; Biological neuron ; Biologically inspired ; Compact structures ; Digital implementation ; Hardware implementations ; Hardware synthesis ; Implementation cost ; Neuromorphic ; Original model ; Piecewise linear ; Piecewise linear models ; Single neuron ; spike rate learning ; Spiking neural networks ; Spiking neuron ; Spiking neuron models ; Character recognition ; Field programmable gate arrays (FPGA) ; Hardware ; Inference engines ; Learning algorithms ; Models ; Neurons ; Piecewise linear techniques ; Neural networks
  8. Source: IEEE Transactions on Circuits and Systems I: Regular Papers ; Volume 59, Issue 12 , 2012 , Pages 2991-3004 ; 15498328 (ISSN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6268301