KIP publications

 
year 2017
author(s) Johannes Schemmel, Laura Kriener, Paul Müller, Karlheinz Meier
title An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites
KIP-Nummer HD-KIP 17-22
KIP-Gruppe(n) F9
document type Paper
source Proceedings of the 2017 IEEE International Joint Conference on Neural Networks
doi 10.1109/IJCNN.2017.7966124
Abstract (en)

This paper presents an extension of the BrainScaleS accelerated analog neuromorphic hardware model. The scalable neuromorphic architecture is extended by the support for multi-compartment models and non-linear dendrites. These features are part of a 65 nm prototype ASIC. It allows to emulate different spike types observed in cortical pyramidal neurons: NMDA plateau potentials, calcium and sodium spikes. By replicating some of the structures of these cells, they can be configured to perform coincidence detection within a single neuron. Built-in plasticity mechanisms can modify not only the synaptic weights, but also the dendritic synaptic composition to efficiently train large multi-compartment neurons. Transistor-level simulations demonstrate the functionality of the analog implementation and illustrate analogies to biological measurements.

bibtex
@article{schemmel2017accelerated,
  author   = {Johannes Schemmel, Laura Kriener, Paul M\"uller, Karlheinz Meier},
  title    = {An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites},
  journal  = {Proceedings of the 2017 IEEE International Joint Conference on Neural Networks},
  year     = {2017},
  volume   = {},
  pages    = {},
  doi      = {10.1109/IJCNN.2017.7966124},
  url      = {http://ieeexplore.ieee.org/document/7966124/}
}
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