دانلود رایگان مقاله انگلیسی فعالیت الکتریکی عصبی و رشد شبکه عصبی - الزویر 2018

abstract

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization.

5. Conclusions

A fundamental issue in neurobiology defines the mechanisms by which neurons recognize and innervate their targets, because the presence of synapses between neurons and the position of each synapse cannot be predetermined genetically (Ackley & Jin, 2004; Krubitzer & Kahn, 2003; Sur & Rubenstein, 2005). The ability to construct neuronal networks that grow in activity-dependent manner opens up many opportunities in neurobiological studies. These range from developing better methods for analyzing spiking activity of neural networks to studying how large neuronal circuits operate and how different brain regions communicate and cooperate. In this paper, we developed a general theoretical framework with a detailed set of cellular rules that govern the activitydependent neural circuit generation. By computational modeling of growth processes in activity-dependent and activityindependent neural networks we have shown the influence of neural activity on neural network growth and development. We have analyzed the connectivity structures in the generated networks in terms of excitation/inhibition balance. Activity-dependent growth model gives a more better excitatory/inhibitory balance than activity-independent and random growth network models. For activity-independent models, a large number of neurons is constantly in an active state for a long time, and the other part is in inactive state for a long time whereas for activity-dependent growth model active or inactive states of neurons are balanced. We have found that the connectivity structure and activity pattern of activity-dependent growth network strongly depend on the structure of external signal.