Statistical Modelling of Artificial Neural Network for Sorting Temporally Synchronous Spikes

Veerabhadrappa, Rakesh; Bhatti, Asim; Lim, Chee Peng; Nguyen, Thanh; Tye, Susannah J.; Monaghan, Paul; Nahavandi, Saeid

doi:10.1007/978-3-319-26555-1_30

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…A network is initially trained using the ground-truth followed by the data classification. Neural networks are perfect examples of such algorithms (Veerabhadrappa et al, 2015 ). Since the raw extracellular data does not possess any ground-truth information, training of a neural network is not possible.…”

Section: Evolution Of Clustering Algorithmsmentioning

confidence: 99%

Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting

Veerabhadrappa

Hassan

Zhang

et al. 2020

Front. Syst. Neurosci.

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Deciphering useful information from electrophysiological data recorded from the brain, in-vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and accurately. The spike analysis mechanisms are heavily reliant on the clustering algorithms that enable separation of spike trends based on their spatio-temporal behaviors. Literature review report several clustering algorithms over decades focused on different applications. Although spike analysis algorithms employ only a small subset of clustering algorithms, however, not much work has been reported on the compliance and suitability of such clustering algorithms for spike analysis. In our study, we have attempted to comment on the suitability of available clustering algorithms and performance capacity when exposed to spike analysis. In this regard, the study reports a compatibility evaluation on algorithms previously employed in spike sorting as well as the algorithms yet to be investigated for application in sorting neural spikes. The performance of the algorithms is compared in terms of their accuracy, confusion matrix and accepted validation indices. Three data sets comprising of easy, difficult, and real spike similarity with known ground-truth are chosen for assessment, ensuring a uniform testbed. The procedure also employs two feature-sets, principal component analysis and wavelets. The report also presents a statistical score scheme to evaluate the performance individually and overall. The open nature of the data sets, the clustering algorithms and the evaluation criteria make the proposed evaluation framework widely accessible to the research community. We believe that the study presents a reference guide for emerging neuroscientists to select the most suitable algorithms for their spike analysis requirements.

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Section: Evolution Of Clustering Algorithmsmentioning

confidence: 99%

Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting

Veerabhadrappa

Hassan

Zhang

et al. 2020

Front. Syst. Neurosci.

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“…However, traditional RL algorithms seem to work in obvious problems where the number of data dimensions is limited. To deal with multi-dimensional environments, a deep neural network is used to approximate the probability distribution of all possible actions [2,3]. However, the use of neural networks may cause divergence while estimating Q-value function [4].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning with Human Strategies

Nguyen

Nahavandi

2019

2019 IEEE International Conference on Industrial Technology (ICIT)

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Deep learning has enabled traditional reinforcement learning methods to deal with highdimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increase the exploration capacity of multiple deep reinforcement learning agents. We also report the development of our own multiagent environment called Multiple Tank Defence to simulate the proposed approach. The results show the significant performance improvement of multiple agents that have learned cooperatively with human strategies. This implies that there is a critical need for human intellect teamed with machines to solve complex problems. In addition, the success of this simulation indicates that our multi-agent environment can be used as a testbed platform to develop and validate other multi-agent control algorithms.

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A Deep Convolutional Neural Network Model for Classification of Emotions from Electroencephalography Data

Veerabhadrappa¹,

Hettiarachchi²,

Algumaei³

et al. 2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Statistical Modelling of Artificial Neural Network for Sorting Temporally Synchronous Spikes

Cited by 3 publications

References 17 publications

Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting

Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting

Multi-Agent Deep Reinforcement Learning with Human Strategies

A Deep Convolutional Neural Network Model for Classification of Emotions from Electroencephalography Data

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