Behavior of a Model for Feedback-Controlled Reverberating Circuit and Immediate Memory Function

Abstract

In this work we implement a mathematical model of synaptic transmission connecting neurons in a circuit of reverberating discharges in order to investigate its behavior in front of parametric variations. Using a program developed in C language, we verified if this model would behave as short-term memory circuit. In the simulation, we used neural parametric values from experimental measures in animal. Our model was able to reproduce polysynaptic activity of a neuronal group of rat brain (looping time of about 102 ms). On the other hand, we verified that the inhibitory feedback synapses in circuit with weight varying with presynaptic firing time and frequency is capable to change the circuit characteristic to reproduce the typical behavior of neural circuits. The results suggest that, differently from some recent considerations, the reverberating circuit model has great potential to reproduce the typical behavior of neural circuits and could be seen as a possible model for immediate memory.

Applying Graph Theory and Mathematical-Computational Modelling to Study a Neurophysiological Circuit

“…These results of computational modelling using the graph theory were different from those observed in a previous study [9]. In that study, we implement a mathematical model of synaptic transmission connecting neurons in a circuit of reverberating discharges, which was treat as a neural network.…”

“…Mathematical modelling has been increasingly present in the fields of Biology and Medicine, in the various types of studies [1] [2], and among many examples, we can cite the study of dynamics for the initiation of immune responses, dynamics of the viral transmission between human groups and the evaluation of the efficacy of therapies addressed to cancer [3] [4] [5]. When it comes to the nervous system, mathematical and computational modelling enables data integration from observations and experiments into a theoretical framework, facilitating the exploitation of the functional role of nervous structures, and neuronal circuits [6] [7] [8] [9]. The use of mathematical and computational modelling to analyze circuits and neuronal networks offers a new way to quantitatively characterize morphofunctional patterns [8] [9] [10]- [16].…”

“…When it comes to the nervous system, mathematical and computational modelling enables data integration from observations and experiments into a theoretical framework, facilitating the exploitation of the functional role of nervous structures, and neuronal circuits [6] [7] [8] [9]. The use of mathematical and computational modelling to analyze circuits and neuronal networks offers a new way to quantitatively characterize morphofunctional patterns [8] [9] [10]- [16].…”

“…Much knowledge has been obtained by modelling neurophysiological circuits using experimental data in the simulations to observe the behavior and limitations of the models [9] [11] [14] [15] [16].…”