Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory

Version 1 : Received: 12 August 2019 / Approved: 13 August 2019 / Online: 13 August 2019 (10:09:23 CEST)
Version 2 : Received: 17 September 2019 / Approved: 18 September 2019 / Online: 18 September 2019 (13:05:22 CEST)

A peer-reviewed article of this Preprint also exists.

Plaster, B.; Kumar, G. Data-Driven Predictive Modeling of Neuronal Dynamics Using Long Short-Term Memory. Algorithms 2019, 12, 203. Plaster, B.; Kumar, G. Data-Driven Predictive Modeling of Neuronal Dynamics Using Long Short-Term Memory. Algorithms 2019, 12, 203.

Journal reference: Algorithms 2019, 12, 203
DOI: 10.3390/a12100203


Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

Subject Areas

Long short-term memory; Brain dynamics; Data-driven modeling; Complex systems

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