Correlated Neuronal Activity in Networks of Neurons Stimulated with Nanomachines

Abstract

Recent research and development of nanotechnology have been providing innovative instruments for health-care applications. One of the most promising is the stimulation of the cerebral cortex for the analysis and treatment of brain diseases. In this paper, we introduce and analyse the impact of a theoretical bio-inspired nanoscale neuronal stimulator named synaptic nanomachine (SnM) driven by an input signal. We resort to an equivalent neuron-nanomachine model (EqNN) to describe the neuronal spiking pattern of a population of stimulated neurons. In the considered scenarios, we use statistical analysis methods to exploit the correlation, which may arise in a population of excitatory/inhibitory neurons, in order to observe the neuronal behaviour for different stimulation patterns. The obtained results suggest that structure and frequency of the input stimuli impact on the oscillatory behavior in the neuronal activity. Along with resulting synchronization effects, the neuronal nanoscale stimulation may play an important role to regulate phenomenas like learning and memory and in the design of future bio-inspired nanoscale systems.