Montans, F. | Search bioRxiv

bioRxiv2023

Numerical reproduction of the Sherrington-Adrian observations through a community of McCulloch-Pitts neurons with plastic remodelling

Neurons form a highly complex network that produces cognition from simple associative rules. From previous results, this work shows the natural capability of the numerical network produced to modulate the output signal with independence of the intensity of the stimuli. Moreover, the plastic remodelling implemented in the model is capable to change the latency of a wide range of stimuli to synchronize them and adjust to a required delay of the signal.

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bioRxiv2023

An agent based model (ABM) to reproduce the boolean logic behaviour of neuronal self organized communities through pulse delay modulation and generation of logic gates

The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modeled. Neurological processes have logical signal-processing aspects and biophysical aspects, and both affect the brain structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence approaches inspired by its logical functioning. In this article, we present an approach to model some aspects of the brain learning and signal processing, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model, to demonstrate how dynamic neuroplasticity, neural inhibition and neurons migration can remodel the brain logical connectivity to syncronize signal processing and obtain target latencies. This work demonstrates the importance of dynamic logical and biophysical remodelling in brain plasticity.

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