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Updated: Jul 15, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
A feed-forward neural logic based on synaptic and volume transmission
Bruno Apolloni1, Simone Bassis
1Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, Via Comelico 39/41, 20135 Milan, Italy. apolloni@dsi.unimi.it
Abstract:
We consider a homeostatic mechanism to maintain a plastic layer of a feed-forward neural network reactive to a long sequence of signals, with neither falling in a fixed point of the state space nor undergoing in overfitting. Homeostasis is achieved without asking the neural network to be able to pursue an offset through local feedbacks. Rather, each neuron evolves monotonically in the direction increasing its own parameter, while a global feedback emerges from volume transmission of a homostatic signal. Namely: 1) each neuron is triggered to increase its own parameter in order to exceed the mean value of all of the other neurons' parameters, and 2) a global feedback on the population emerges from the composition of the single neurons behavior paired with a reasonable rule through which surrounding neurons in the same layer are activated. We provide a formal description of the model that we implement in an ad hoc version of pi-calculus. Some numerical simulations will depict some typical behaviors that seem to show a plausible biological interpretation.
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