Cortical Networks at work: Using transfer entropy to quantify effective connectivity at the source level in perceptual closure

Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. Our focus is on how to include the TE in the learning mechanisms of a Convolutional Neural Network (CNN) architecture. We introduce a novel training mechanism for CNN architectures which integrates the TE feedback connections. Adding the TE feedback parameter accelerates the training process, as fewer epochs are needed. On the flip side, it adds computational overhead to each epoch. According to our experiments on CNN classifiers, to achieve a reasonable computational overhead–accuracy trade-off, it is efficient to consider only the inter-neural information transfer of the neuron pairs between the last two fully connected layers. The TE acts as a smoothing factor, generating stability and becoming active only periodically, not after processing each input sample. Therefore, we can consider the TE is in our model a slowly changing meta-parameter.

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On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles

Neural Computation ◽

10.1162/neco.2009.11-08-900 ◽

2010 ◽

Vol 22 (1) ◽

pp. 158-189 ◽

Cited By ~ 38

Author(s):

Seif Eldawlatly ◽

Yang Zhou ◽

Rong Jin ◽

Karim G. Oweiss

Keyword(s):

Bayesian Networks ◽

Spike Train ◽

Cortical Neurons ◽

Effective Connectivity ◽

Dynamic Bayesian Networks ◽

Time Varying ◽

Cortical Networks ◽

Highly Nonlinear ◽

Wide Range ◽

High Level

Coordination among cortical neurons is believed to be a key element in mediating many high-level cortical processes such as perception, attention, learning, and memory formation. Inferring the structure of the neural circuitry underlying this coordination is important to characterize the highly nonlinear, time-varying interactions between cortical neurons in the presence of complex stimuli. In this work, we investigate the applicability of dynamic Bayesian networks (DBNs) in inferring the effective connectivity between spiking cortical neurons from their observed spike trains. We demonstrate that DBNs can infer the underlying nonlinear and time-varying causal interactions between these neurons and can discriminate between mono- and polysynaptic links between them under certain constraints governing their putative connectivity. We analyzed conditionally Poisson spike train data mimicking spiking activity of cortical networks of small and moderately large size. The performance was assessed and compared to other methods under systematic variations of the network structure to mimic a wide range of responses typically observed in the cortex. Results demonstrate the utility of DBN in inferring the effective connectivity in cortical networks.

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Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v8i2.6515 ◽

2021 ◽

Author(s):

Ali Ekhlasi ◽

Ali Motie Nasrabadi ◽

Mohammadreza Mohammadi

Keyword(s):

Frequency Band ◽

Phase Transfer ◽

Effective Connectivity ◽

Brain Regions ◽

Transfer Entropy ◽

Healthy Children ◽

Eeg Signals ◽

Children With Adhd ◽

Frequency Bands ◽

Artificial Neural Network Ann

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods. Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

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