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Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit

Author

Listed:
  • Richard H. R. Hahnloser

    (Institute of Neuroinformatics ETHZ/UNIZ
    Department of Brain and Cognitive Sciences)

  • Rahul Sarpeshkar

    (Bell Laboratories
    Department of Electrical Engineering and Computer Science, MIT)

  • Misha A. Mahowald

    (Institute of Neuroinformatics ETHZ/UNIZ)

  • Rodney J. Douglas

    (Institute of Neuroinformatics ETHZ/UNIZ)

  • H. Sebastian Seung

    (Bell Laboratories
    Department of Brain and Cognitive Sciences)

Abstract

Digital circuits such as the flip-flop use feedback to achieve multi-stability and nonlinearity to restore signals to logical levels, for example 0 and 1. Analogue feedback circuits are generally designed to operate linearly, so that signals are over a range, and the response is unique. By contrast, the response of cortical circuits to sensory stimulation can be both multistable and graded1,2,3,4. We propose that the neocortex combines digital selection of an active set of neurons with analogue response by dynamically varying the positive feedback inherent in its recurrent connections. Strong positive feedback causes differential instabilities that drive the selection of a set of active neurons under the constraints embedded in the synaptic weights. Once selected, the active neurons generate weaker, stable feedback that provides analogue amplification of the input. Here we present our model of cortical processing as an electronic circuit that emulates this hybrid operation, and so is able to perform computations that are similar to stimulus selection, gain modulation and spatiotemporal pattern generation in the neocortex.

Suggested Citation

  • Richard H. R. Hahnloser & Rahul Sarpeshkar & Misha A. Mahowald & Rodney J. Douglas & H. Sebastian Seung, 2000. "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit," Nature, Nature, vol. 405(6789), pages 947-951, June.
    • Handle: RePEc:nat:nature:v:405:y:2000:i:6789:d:10.1038_35016072
    DOI: 10.1038/35016072
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    15. Joanne C Wen & Cecilia S Lee & Pearse A Keane & Sa Xiao & Ariel S Rokem & Philip P Chen & Yue Wu & Aaron Y Lee, 2019. "Forecasting future Humphrey Visual Fields using deep learning," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-14, April.
    16. Bernhard Nessler & Michael Pfeiffer & Lars Buesing & Wolfgang Maass, 2013. "Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-30, April.
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