Research On The Closed-loop Control Of The Neuron And Neural Mass’s Epileptic State

Epilepsy is a chronic disease of the nervous system, which is originated from the brain neurons’ sudden abnormal firing, resulting in brain dysfunction. In view of the drug non-responsive epilepsy, electrical stimulation is becoming one of the most effective treatments, for example, deep brain stimulation（DBS）, transcranial direct current stimulation（tDCS）. Currently, the open-loop electrical stimulation therapies in clinic have no universal applicability. To solve this problem, the closed-loop electrical stimulation for epilepsy and other mental diseases has become a current research hot spot. Because the pathogenesis of epilepsy is unclear, together with the limitation of electrophysiology experiment, the neural computation analysis has become an effective method to study the dynamic characteristics of neural system and the physiological phenomenon. Therefore, based on epileptic neuronal model and neural mass model, a closed-loop iterative learning control（ILC） strategy and a closed-loop mixed control scheme are separately proposed in this paper to achieve the goal of controlling epileptic firing state. The research contents of this paper mainly include the following three aspects:Firstly, according to Pinsky-Rinzel（PR） model in hippocampal CA3 area and the neural mass model（NMM） in cerebral cortex neurons, this paper analyzes the brain dynamic characteristics. Analyze the influences on neurons and neural mass’ s firing states which are making by critical parameters: back electromotive force of potassium ionic channelKV, coupling conductance strengthcg between soma and dendrite, strength of chemical coupling g_NMDA,g_AMPA, excitatory average synaptic gain A and coupling strength K_ij between neural masses. Furthermore, these researches lay the foundation for exploring the mechanism of epilepsy.Secondly, based on the effects of critical parameters, this paper utilizes a ILC strategy, a mixed control strategy consist of delayed feedback and PI control respectively to realize the closed-loop control of epileptic state in neuron; Employing the Unscented Kalman Filter（UKF） to estimate critical parameters V_K and g_c , and then forming feedback signals; By means of tracking and adjusting the feedback critical parameters, PR model could switch firing pattern. Besides, according to the influences of chemical coupling, this paper adopts ILC to desynchronize based on neurons with various chemical coupling.Finally, a closed-loop ILC DBS system based on NMM is put forward in this paper. On account of the effects of excitatory average synaptic gain and coupling strength, diverse local field potentials（LFP） including epileptic state and normal state can be produced. LFPs’ features can be extracted according to the analysis of complexity degree C₀ and the ratios between different frequency bands’ energy. The local field potentials are classified by BP neural network and the system could choose stimulation intensity. On account of the various firing patterns, the ILC controller could provide different output control signals; By modulating the amplitude and the period of the stimulation current, the wave-form generator would produce exact DBS stimulation current which realizes closed-loop control of epileptic state and prevents the further spread of epileptic firing.The above research results demonstrate the effectiveness of the closed-loop control strategies proposed in this paper in controlling epileptic state. The strategies provide theoretical basis in closed-loop electrophysiological study of neuron or neural mass, and lay the foundation for the study about electrical stimulation devices for treating epilepsy.