Optimal stimulus scheduling for active estimation of evoked brain networks.

Abstract:

OBJECTIVE:We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. APPROACH:We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. MAIN RESULTS:By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. SIGNIFICANCE:The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

journal_name

J Neural Eng

authors

Kafashan M,Ching S

doi

10.1088/1741-2560/12/6/066011

subject

Has Abstract

pub_date

2015-12-01 00:00:00

pages

066011

issue

6

eissn

1741-2560

issn

1741-2552

journal_volume

12

pub_type

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