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Cortical Models on Neuromorphic Hardware

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Form Versus Function: Theory and Models for Neuronal Substrates

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Abstract

Along with the many advantages it offers, the neuromorphic approach also comes with limitations of its own. These have various causes that lie both in the hardware itself and the control software. We will later identify these causes, which we henceforth refer to as distortion mechanisms. The neural network emulated by the hardware device can therefore differ significantly from the original model, be it in terms of pulse transmission, connectivity between populations or individual neuron or synapse parameters.

As I see it, the only way of overcoming this magical view of what “I” and consciousness are is to keep on reminding oneself, unpleasant though it may seem, that the “teethering bulb of dread and dream” that nestles safely inside one’s own cranium is a purely physical object made up of completely sterile and inanimate components, all of which obey exactly the same laws that govern all the rest of the universe [...]. Only if one keeps bashing up against this disturbing fact can one slowly begin to develop a feel for the way out of the mystery of consciousness: that the key is not the stuff out of which brains are made, but the patterns that can come to exist inside the stuff of a brain.

Douglas Hofstadter, Gödel, Escher, Bach, 1999

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Notes

  1. 1.

    Orthogonal patterns are much more comfortable to study than non-orthogonal ones, since the response of the network to experimental scenarios such as pattern completion is easily classified as “correct” or “wrong”. However, allowing patterns to share MCs can greatly increase the memory capacity of the network, i.e., the number of patterns it can “correctly” recall under certain well-defined conditions, where the “correctness” is, itself, a parameter to be defined in a sensible way. Although not a part of this discussion, we point to two related studies on non-orthogonal patterns in the L2/3 model, namely Breitwieser (2011) and Rivkin (2014).

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  1. Department of Electronic Vision(s), Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany

    Mihai Alexandru Petrovici

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  1. Mihai Alexandru Petrovici
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Petrovici, M.A. (2016). Cortical Models on Neuromorphic Hardware. In: Form Versus Function: Theory and Models for Neuronal Substrates . Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-39552-4_5

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