Classification-Based Prediction of Effective Connectivity Between Timeseries With a Realistic Cortical Network Model
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Classification-Based Prediction of Effective Connectivity Between Timeseries With a Realistic Cortical Network Model. / Olivetti, Emanuele; Benozzo, Danilo; Bím, Jan; Panzeri, Stefano; Avesani, Paolo.
In: FRONT COMPUT NEUROSC, Vol. 12, 05.06.2018, p. 38.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Classification-Based Prediction of Effective Connectivity Between Timeseries With a Realistic Cortical Network Model
AU - Olivetti, Emanuele
AU - Benozzo, Danilo
AU - Bím, Jan
AU - Panzeri, Stefano
AU - Avesani, Paolo
PY - 2018/6/5
Y1 - 2018/6/5
N2 - Effective connectivity measures the pattern of causal interactions between brain regions. Traditionally, these patterns of causality are inferred from brain recordings using either non-parametric, i.e., model-free, or parametric, i.e., model-based, approaches. The latter approaches, when based on biophysically plausible models, have the advantage that they may facilitate the interpretation of causality in terms of underlying neural mechanisms. Recent biophysically plausible neural network models of recurrent microcircuits have shown the ability to reproduce well the characteristics of real neural activity and can be applied to model interacting cortical circuits. Unfortunately, however, it is challenging to invert these models in order to estimate effective connectivity from observed data. Here, we propose to use a classification-based method to approximate the result of such complex model inversion. The classifier predicts the pattern of causal interactions given a multivariate timeseries as input. The classifier is trained on a large number of pairs of multivariate timeseries and the respective pattern of causal interactions, which are generated by simulation from the neural network model. In simulated experiments, we show that the proposed method is much more accurate in detecting the causal structure of timeseries than current best practice methods. Additionally, we present further results to characterize the validity of the neural network model and the ability of the classifier to adapt to the generative model of the data.
AB - Effective connectivity measures the pattern of causal interactions between brain regions. Traditionally, these patterns of causality are inferred from brain recordings using either non-parametric, i.e., model-free, or parametric, i.e., model-based, approaches. The latter approaches, when based on biophysically plausible models, have the advantage that they may facilitate the interpretation of causality in terms of underlying neural mechanisms. Recent biophysically plausible neural network models of recurrent microcircuits have shown the ability to reproduce well the characteristics of real neural activity and can be applied to model interacting cortical circuits. Unfortunately, however, it is challenging to invert these models in order to estimate effective connectivity from observed data. Here, we propose to use a classification-based method to approximate the result of such complex model inversion. The classifier predicts the pattern of causal interactions given a multivariate timeseries as input. The classifier is trained on a large number of pairs of multivariate timeseries and the respective pattern of causal interactions, which are generated by simulation from the neural network model. In simulated experiments, we show that the proposed method is much more accurate in detecting the causal structure of timeseries than current best practice methods. Additionally, we present further results to characterize the validity of the neural network model and the ability of the classifier to adapt to the generative model of the data.
U2 - 10.3389/fncom.2018.00038
DO - 10.3389/fncom.2018.00038
M3 - SCORING: Journal article
C2 - 29922142
VL - 12
SP - 38
JO - FRONT COMPUT NEUROSC
JF - FRONT COMPUT NEUROSC
SN - 1662-5188
ER -