A Functional Spiking Neural Network of Ultra Compact Neurons
| Autor: | Marcelo J. Rozenberg, Olivier Schneegans, Pablo Stoliar |
|---|---|
| Přispěvatelé: | National Institute of Advanced Industrial Science and Technology (AIST), Laboratoire Génie électrique et électronique de Paris (GeePs), CentraleSupélec-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique des Solides (LPS), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Dynamical systems theory
Computer science Complex system lcsh:RC321-571 03 medical and health sciences 0302 clinical medicine leaky-integrated-and-fire neuron models lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Jeffress model 030304 developmental biology Original Research Spiking neural network [PHYS]Physics [physics] 0303 health sciences neuromorphic computers Computational neuroscience neuromorphic electronic circuits Mathematical model business.industry General Neuroscience artificial intelligence Neuromorphic engineering visual_art Electronic component spiking neural networks visual_art.visual_art_medium Artificial intelligence Neural coding business 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Frontiers in Neuroscience Frontiers in Neuroscience, Frontiers, 2021, 15, ⟨10.3389/fnins.2021.635098⟩ Frontiers in Neuroscience, Vol 15 (2021) |
| ISSN: | 1662-4548 1662-453X |
| Popis: | We demonstrate that recently introduced ultra-compact neurons (UCN) with a minimal number of components can be interconnected to implement a functional spiking neural network. For concreteness we focus on the Jeffress model, which is a classic neuro-computational model proposed in the 40’s to explain the sound directionality detection by animals and humans. In addition, we introduce a long-axon neuron, whose architecture is inspired by the Hodgkin-Huxley axon delay-line and where the UCNs implement the nodes of Ranvier. We then interconnect two of those neurons to an output layer of UCNs, which detect coincidences between spikes propagating down the long-axons. This functional spiking neural neuron circuit with biological relevance is built from identical UCN blocks, which are simple enough to be made with off-the-shelf electronic components. Our work realizes a new, accessible and affordable physical model platform, where neuroscientists can construct arbitrary mid-size spiking neuronal networks in a lego-block like fashion that work in continuous time. This should enable them to address in a novel experimental manner fundamental questions about the nature of the neural code and to test predictions from mathematical models and algorithms of basic neurobiology research. The present work aims at opening a new experimental field of basic research in Spiking Neural Networks to a potentially large community, which is at the crossroads of neurobiology, dynamical systems, theoretical neuroscience, condensed matter physics, neuromorphic engineering, artificial intelligence, and complex systems. |
| Databáze: | OpenAIRE |
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