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Principles of neuronal interaction in a brain: the view of the physiologists

Keywords:

P.M. Balaban – Corresponding member оf RAS, Dr.Sc. (Biol.), Professor, Director, FGBUN «Institute of Higher Nervous Activity and Neurophysiology RAS» (Moscow)
E-mail: pmbalaban@gmail.com


Typically, when describing the work of a brain and the formulation of principles of neural network functioning, the neurons inte-raction is described as achievement of the trigger threshold in neurons as a result of the activity of network elements, and the transmission and coding of information are attributed implicitly to nerve impulses arising under the law of "all-or-nothing".
The aim of this short review is to demonstrate and single out some most important principles of interactions in the brain that usually are ignored during construction of the models of nervous system, neural network modeling. First, interaction between neurons is not digital, it is analogous, because only inside one neuron information is transmitted using electrical events, while majority of synapses is chemical, what means that it depends on quantity of released transmitter, effectivity and quantity of receptors in postsynaptic neurons, and implicitly suggests a possibility of influence the neighbors at a time scale that significantly exceeds the impulse conduction time. In fact, in the nervous system the neuromodulation, which implies changes of future responses due to present activity, is one of the most important ways of neural interactions. The neuromodulation principle of neural net activity always is used for achievement of homeostatic state of the nervous system that is necessary for survival. Any system that do not have such homeostatic activity will “run away” very soon after several external inputs, what never happens with the brain – it is always ready to percept new stimuli. One of the mechanisms of such homeostatic plasticity is negative feedback mediated by endocannabinoids. The decision making is another point raised in the present paper. The decision “to spike or not to spike” is made by each neuron in the network, but the overall decision of the network is mediated by a specialized individual neurons (in invertebrates), and by a class of neurons in vertebrates. The “decision neurons” have to be involved in the behavioral act that is triggered, have to be necessary for its triggering, sufficient for its triggering, and to receive polymodal sensory information which potentially can trigger a particular behavior. Major changes due to the learning procedure happen in the synaptic inputs of such decision neurons and simultaneously in the synaptic inputs of modulatory neurons specific for given behavior.

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