Neural Comput - Fragility in dynamic networks: application to neural networks in the epileptic cortex.

Tópicos

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Resumo

Epilepsy is a network phenomenon characterized by atypical activity at the neuronal and population levels during seizures, including tonic spiking, increased heterogeneity in spiking rates, and synchronization. The etiology of epilepsy is unclear, but a common theme among proposed mechanisms is that structural connectivity between neurons is altered. It is hypothesized that epilepsy arises not from random changes in connectivity, but from specific structural changes to the most fragile nodes or neurons in the network. In this letter, the minimum energy perturbation on functional connectivity required to destabilize linear networks is derived. Perturbation results are then applied to a probabilistic nonlinear neural network model that operates at a stable fixed point. That is, if a small stimulus is applied to the network, the activation probabilities of each neuron respond transiently but eventually recover to their baseline values. When the perturbed network is destabilized, the activation probabilities shift to larger or smaller values or oscillate when a small stimulus is applied. Finally, the structural modifications to the neural network that achieve the functional perturbation are derived. Simulations of the unperturbed and perturbed networks qualitatively reflect neuronal activity observed in epilepsy patients, suggesting that the changes in network dynamics due to destabilizing perturbations, including the emergence of an unstable manifold or a stable limit cycle, may be indicative of neuronal or population dynamics during seizure. That is, the epileptic cortex is always on the brink of instability and minute changes in the synaptic weights associated with the most fragile node can suddenly destabilize the network to cause seizures. Finally, the theory developed here and its interpretation of epileptic networks enables the design of a straightforward feedback controller that first detects when the network has destabilized and then applies linear state feedback control to steer the network back to its stable state.

Resumo Limpo

epilepsi network phenomenon character atyp activ neuron popul level seizur includ tonic spike increas heterogen spike rate synchron etiolog epilepsi unclear common theme among propos mechan structur connect neuron alter hypothes epilepsi aris random chang connect specif structur chang fragil node neuron network letter minimum energi perturb function connect requir destabil linear network deriv perturb result appli probabilist nonlinear neural network model oper stabl fix point small stimulus appli network activ probabl neuron respond transient eventu recov baselin valu perturb network destabil activ probabl shift larger smaller valu oscil small stimulus appli final structur modif neural network achiev function perturb deriv simul unperturb perturb network qualit reflect neuron activ observ epilepsi patient suggest chang network dynam due destabil perturb includ emerg unstabl manifold stabl limit cycl may indic neuron popul dynam seizur epilept cortex alway brink instabl minut chang synapt weight associ fragil node can sudden destabil network caus seizur final theori develop interpret epilept network enabl design straightforward feedback control first detect network destabil appli linear state feedback control steer network back stabl state

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