Special Issue: Modeling Synaptic Transmission

Vito Di Maio, Institute of Applied Sciences and Intelligent Systems (ISASI) Pozzuoli – Italy, Editor for Frontiers in compitational Neuroscience in Cross Linking with Frontiers in Synaptic Transmission.

Abstract

The brain is a very powerful information processing machine, and synaptic transmission is the elementary mean by which neurons communicate and hence the way they exchange and process information contributing, at different levels, to the brain activity. Synapses are not only the foundation of brain information management; they also have the ability to modify their structure and function depending on their activity. This contributes to the formation and destruction of memory traces by mechanisms of plasticity based on Long Term Potentiation (LTP) and Long Term Depression (LTD).

Despite the apparent simplicity of the basic mechanism underlying synaptic transmission, several processes, both at pre- and post-synaptic level, regulate the post-synaptic signals and the information flow. Some of the factors influencing the synaptic information flow are: the synaptic location with respect to the cell body, the properties of the dendrites where the synapse is located, the activity of the post-synaptic neuron, and the concurrent activity of other synapses in the same dendritic district and the surrounding glial cells. Although a huge amount of experimental, theoretical, and modeling studies have been performed in the field, neither the mechanism of a single synaptic event nor the interaction among them in modulating the synaptic outputs are completely understood.

The reason for this arguably stems from the complexity that takes place at synapses.

The mechanisms involved in synaptic transmission operate at different spatial and temporal scales ranging from the changes in molecular conformation, which occurs in a range of nanoseconds, to the time course of a single postsynaptic response occurring in a timescale of milliseconds, to minutes or hours if longer mechanisms such as LTP are considered. The multitude of operations that take place at different scales and other limitations intrinsic to experimental procedures complicate the interpretation of the experimental results, making theoretical and modeling studies essential. The two approaches, however, are neither mutually excluding nor competitive; instead their cooperative interaction provides an added value to deepen our understanding of information transfer in the brain and its emergence via synaptic transmission. While experimental approaches can benefit from hypotheses, ideas, and theories stemming from modeling studies, these in turn, receive knowledge from experimental studies about synaptic structure and functionality which allows for the refinement of more biologically plausible models.

Moreover, theoretical and modeling studies have become the bridge between experimental findings and new areas of Neuroscience. For example, some areas of AI and robotics use biologically-inspired neural networks or “On Chip Synapses”, which, although still at the experimental level, are used in several technological applications.

The goal of the present Research Topic is to collect content focused on synaptic modeling that will generate novel hypotheses (i) for experimental scientists to design further investigations, (ii) for theoretical scientists to improve and compare their models, (iii) and for scientists who aim to apply the  knowledge on synaptic transmission to implement biologically plausible neural networks in other scientific and technological fields.

Welcome papers will be those related, but not strongly restricted to:

– Models of synaptic transmission

– Models of synapse – dendrite interaction

– Models of synaptic integration on a single neuron.

– Biologically plausible synaptic models bridging towards AI, robotics, or other technological applications.