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

Abstract

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.

Review of the Brain’s Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

“…Their "perceptron" [435], organised in layers (input variables, hidden dense-i.e., fully connected-layers, and output targets), aims at a stochastic, biologically inspired regression, like that of least squares. This allows for the reproduction of some brain features like metastability and other related phenomena (e.g., Sherrington-Adrian observations [436]). The Dartmouth Workshop in 1956 [437], ignited by the perceptron and Turing's notions on ML [438], can be cited as the official birth of artificial intelligence as such.…”