Universality in the firing of minicolumnar-type neural networks

An open question in biological neural networks is whether changes in firing modalities are mainly an individual network property or whether networks follow a joint pathway. For the early developmental period, our study focusing on a simple network class of excitatory and inhibitory neurons suggests the following answer: Networks with considerable variation of topology and dynamical parameters follow a universal firing paradigm that evolves as the overall connectivity strength and firing level increase, as seen in the process of network maturation. A simple macroscopic model reproduces the main features of the paradigm as a result of the competition between the fundamental dynamical system notions of synchronization vs chaos and explains why in simulations the paradigm is robust regarding differences in network topology and largely independent from the neuron model used. The presented findings reflect the first dozen days of dissociated neuronal in vitro cultures (upon following the developmental period bears similarly universal features but is characterized by the processes of neuronal facilitation and depression that do not require to be considered for the first developmental period). A key element for explaining processes in nature by physics has been the art of choosing the optimal level of description for the effects to be described. In our current challenge to explain important aspects of our brain by means of physics, we still largely miss such a handle at many levels: To what detail, e.g., is it necessary to model neurons and their connectivity to understand what their neural network is doing? For simple small-size networks of minicolumnar type (by many considered as a potential module underlying the function of the cortex), we show that all networks from this large network class follow the same—universal—behavior, as their overall connectivity strength is enhanced. Moreover, the paradigm that they follow can be explained in terms of low-dimensional dynamical systems theory, which reveals the origin of the universal behavior. Our findings suggest that other network classes could be treated in a similar manner. The uncovered universality permits us to substantially limit the degree of details required to model cortical computation, which opens up a novel perspective toward more effective simulations of and investigations into close-to-biology neural networks and sheds a novel perspective on biological multiscale information processing. From the practical side, our findings imply that biological neural networks with strong parallels to the increase of a connectivity strength will develop closely along the uncovered paradigm. Examples are neuronal cultures at the early stage of their development or biochemical processes that globally enhance the connectivity strength among the elements of the neural network