Laboratory of Computing Biological Networks - publication

Active prediction in dynamical systems

cjj — Sat, 25 Nov 2017 05:36:49 +0000

Chen CC., Chen K.S., Chan C.K. (2017) Active Prediction in Dynamical Systems. In: Liu D., Xie S., Li Y., Zhao D., El-Alfy ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science, vol 10637. Springer, Cham
URL: https://doi.org/10.1007/978-3-319-70093-9_67

Abstract
Using a hidden Markov model (HMM) that describes the position of a damped stochastic harmonic oscillator as a stimulus input to a data processing system, we consider the optimal response of the system when it is targeted to predict the coming stimulus at a time shift later. We quantify the predictive behavior of the system by calculating the mutual information (MI) between the response and the stimulus of the system. For a passive sensor, the MI typically peaks at a negative time shift considering the processing delay of the system. Using an iterative approach of maximum likelihood for the predictive response, we show that the MI can peak at a positive time shift, which signifies the functional behavior of active prediction. We find the phenomena of active prediction in bullfrog retinas capable of producing omitted stimulus response under periodic pulse stimuli, by subjecting the retina to the same HMM signals encoded in the pulse interval. We confirm that active prediction requires some hidden information to be recovered and utilized from the observation of past stimulus by replacing the HMM with a Ornstein–Uhlenbeck process, which is strictly Markovian, and showing that no active prediction can be observed.

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Characterization of predictive behavior of a retina by mutual information

cjj — Sat, 19 Aug 2017 08:27:04 +0000

Chen, KS, Chen, CC, Chan, CK, Front Comput Neurosci (2017).
URL: https://doi.org/10.3389/fncom.2017.00066

Abstract
Probing a bullfrog retina with spatially uniform light pulses of correlated stochastic intervals, we calculate the mutual information between the spiking output at the ganglion cells measured with multi-electrode array (MEA) and the interval of the stimulus at a time shift later. The time-integrated information from the output about the future stimulus is maximized when the mean interval of the stimulus is within the dynamic range of the well-established anticipative phenomena of omitted-stimulus responses for the retina. The peak position of the mutual information as a function of the time shift is typically negative considering the processing delay of the retina. However, the peak position can become positive for long enough correlation time of the stimulus when the pulse intervals are generated by a Hidden Markovian model (HMM). This is indicative of a predictive behavior of the retina which is possible only when the hidden variable of the HMM can be recovered from the history of the stimulus for a prediction of its future. We verify that stochastic intervals of the same mean, variance, and correlation time do not result in the same predictive behavior of the retina when they are generated by an Ornstein–Uhlenbeck (OU) process, which is strictly Markovian.

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Propagation and synchronization of reverberatory bursts in developing cultured networks

cjj — Sun, 11 Dec 2016 08:03:11 +0000

Huang, CH., Huang, YT., Chen, CC., Chan, CK, J Comput Neurosci (2016).
URL: http://link.springer.com/article/10.1007/s10827-016-0634-4

Abstract
Developing networks of neural systems can exhibit spontaneous, synchronous activities called neural bursts, which can be important in the organization of functional neural circuits. Before the network matures, the activity level of a burst can reverberate in repeated rise-and-falls in periods of hundreds of milliseconds following an initial wave-like propagation of spiking activity, while the burst itself lasts for seconds. To investigate the spatiotemporal structure of the reverberatory bursts, we culture dissociated, rat cortical neurons on a high-density multi-electrode array to record the dynamics of neural activity over the growth and maturation of the network. We find the synchrony of the spiking significantly reduced following the initial wave and the activities become broadly distributed spatially. The synchrony recovers as the system reverberates until the end of the burst. Using a propagation model we infer the spreading speed of the spiking activity, which increases as the culture ages. We perform computer simulations of the system using a physiological model of spiking networks in two spatial dimensions and find the parameters that reproduce the observed resynchronization of spiking in the bursts. An analysis of the simulated dynamics suggests that the depletion of synaptic resources causes the resynchronization. The spatial propagation dynamics of the simulations match well with observations over the course of a burst and point to an interplay of the synaptic efficacy and the noisy neural self-activation in producing the morphology of the bursts.

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Topological Effects on Bursting and Reverberation Dynamics

cjj — Wed, 25 Jun 2014 16:27:35 +0000

Poster presentation at MEA Meeting 2014:
9th International Meeting on Substrate-Integrated Microelectrode Arrays, July 1-4, 2014
Reutlingen, Germany

Abstract
Dynamical behavior of bursting and reverberation in a cultured network contains important information on its connectivity or topology. Since the structural properties of a network is usually harder to measure directly, it is desirable to recover this information through analysis of the measured dynamics of the network. To understand the relationship between structure and dynamics in a neuronal network, we modify an electrophysiological model of spiking neurons, capable of producing reverberatory bursts that closely resemble what have been observed in cultures, and apply it to networks of different topologies ranging from scale-free to random networks with narrow degree distribution. By varying parameters controlling the excitability of neurons and efficacy of synapses while preserving the time ratio between the bursting and resting states, we show that the two factors compensate each other well only for networks of narrow degree distribution. For these networks, the reverberation remains clearly evident for the entire parameter range considered. For networks of broad degree distribution such as scale-free networks, the mean burst period varies significantly with the parameters, while the reverberation, if exists, is only evident for a limit range of the parameter space.

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