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The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

posted on 11.12.2020, 15:14 by Colin Hales

The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue physics or function. Instead of the physics of a general-purpose computer or the physics of abstract models on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. As a result of using the native brain physics, the chip has a functionally relevant endogenous quasistatic electric and magnetic field system of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like electromagnetic signature. This article does an extended analysis to understand an observed generalized lack of the physics replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes the necessary background information in anticipation of the arrival of the first prototyping results over the coming years.


This work has been carried out with support of ‘Anatomy & Neuroscience Pilot Grant’ 39-4020-GEN awarded to the author by the University of Melbourne’s Department of Anatomy and Neuroscience.


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University of Melbourne

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