Int J Neural Syst - Multiple oscillatory states in models of collective neuronal dynamics.

Tópicos

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Resumo

In our previous studies, we showed that the both realistic and analytical computational models of neural dynamics can display multiple sustained states (attractors) for the same values of model parameters. Some of these states can represent normal activity while other, of oscillatory nature, may represent epileptic types of activity. We also showed that a simplified, analytical model can mimic this type of behavior and can be used instead of the realistic model for large scale simulations. The primary objective of the present work is to further explore the phenomenon of multiple stable states, co-existing in the same operational model, or phase space, in systems consisting of large number of interconnected basic units. As a second goal, we aim to specify the optimal method for state control of the system based on inducing state transitions using appropriate external stimulus. We use here interconnected model units that represent the behavior of neuronal populations as an effective dynamic system. The model unit is an analytical model (S. Kalitzin et al., Epilepsy Behav.?22 (2011) S102-S109) and does not correspond directly to realistic neuronal processes (excitatory-inhibitory synaptic interactions, action potential generation). For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models. To analyze the collective behavior of the system we applied phase synchronization analysis (PSA), principal component analysis (PCA) and stability analysis using Lyapunov exponent (LE) estimation. We obtained a large variety of stable states with different dynamic characteristics, oscillatory modes and phase relations between the units. These states can be initiated by appropriate initial conditions; transitions between them can be induced stochastically by fluctuating variables (noise) or by specific inputs. We propose a method for optimal reactive control, allowing forced transitions from one state (attractor) into another.

Resumo Limpo

previous studi show realist analyt comput model neural dynam can display multipl sustain state attractor valu model paramet state can repres normal activ oscillatori natur may repres epilept type activ also show simplifi analyt model can mimic type behavior can use instead realist model larg scale simul primari object present work explor phenomenon multipl stabl state coexist oper model phase space system consist larg number interconnect basic unit second goal aim specifi optim method state control system base induc state transit use appropri extern stimulus use interconnect model unit repres behavior neuron popul effect dynam system model unit analyt model s kalitzin et al epilepsi behav ss correspond direct realist neuron process excitatoryinhibitori synapt interact action potenti generat certain paramet choic howev display bistabl dynam imit behavior realist neural mass model analyz collect behavior system appli phase synchron analysi psa princip compon analysi pca stabil analysi use lyapunov expon le estim obtain larg varieti stabl state differ dynam characterist oscillatori mode phase relat unit state can initi appropri initi condit transit can induc stochast fluctuat variabl nois specif input propos method optim reactiv control allow forc transit one state attractor anoth

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