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Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks
, Article NeuroImage ; Volume 196 , 2019 , Pages 302-317 ; 10538119 (ISSN) ; Kiani, M. M ; Aghajan, H ; Sharif University of Technology
Academic Press Inc
2019
Abstract
Having to survive in a continuously changing environment has driven the human brain to actively predict the future state of its surroundings. Oddball tasks are specific types of experiments in which this nature of the human brain is studied. Detailed mathematical models have been constructed to explain the brain's perception in these tasks. These models consider a subject as an ideal observer who abstracts a hypothesis from the previous stimuli, and estimates its hyper-parameters - in order to make the next prediction. The corresponding prediction error is assumed to manifest the subjective surprise of the brain. While the approach of earlier works to this problem has been to suggest an...
Neural Spike Sorting and Improvement of Non-stationary Continuous Hand Movement Decoding
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
Brain is the most complicated organ of body which controls the activity of all other organs. Understanding its function and its language could give us a direct communication pathway for connecting injured motor organ and it could be useful for functional repairing. Neurons are atoms of a vast network that generate the brain signals. Processing these signals would help to translate brain’s language and has three main stages: spike detection from signal, spike sorting, and intention extraction from encoded signal.
In this research, we use a dataset of rat’s extracellular recordings during a time interval in which a rat pressed the liver several times to receive water as an award. Since...
In this research, we use a dataset of rat’s extracellular recordings during a time interval in which a rat pressed the liver several times to receive water as an award. Since...
Decoding Graph based Linear Codes Using Deep Neural Networks
, M.Sc. Thesis Sharif University of Technology ; Amini, Arash (Supervisor) ; Saleh Kaleybar, Saber (Supervisor)
Abstract
One of the most important goals we pursue in telecommunications science is to send and receive information from telecommunication channels. By designing a powerful telecommunication system consisting of a transmitter and a receiver, we achieve this goal. Speed of data transmission, accuracy of received information and speed of data extraction are some of the criteria by which the performance of a telecommunication system can be evaluated. No telecommunication channel is free of noise. For this reason, additional information is added to the original information in the transmitter, which can still be extracted if the original information is noisy. This process is called coding. Following...
Optimal temporal resolution for decoding of visual stimuli in inferior temporal cortex
, Article 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014 ; 2014 , pp. 109-112 ; Karimi, S ; Ghaffari, A ; Hamidinekoo, A ; Vosoughi-Vahdat, B ; Sharif University of Technology
Abstract
Inferior temporal (IT) cortex is the most important part of the brain and plays an important role in response to visual stimuli. In this study, object decoding has been performed using neuron spikes in IT cortex region. Single Unit Activity (SUA) was recorded from 123 neurons in IT cortex. Pseudo-population firing rate vectors were created, then dimension reduction was done and an Artificial Neural Network (ANN) was used for object decoding. Object decoding accuracy was calculated for various window lengths from 50 ms to 200 ms and various window steps from 25 ms to 100 ms. The results show that 150 ms length and 50 ms window step size gives an optimum performance in average accuracy
Optimal temporal resolution for decoding of visual stimuli in inferior temporal cortex
, Article 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014, 26 November 2014 through 28 November 2014 ; November , 2014 , Pages 109-112 ; 9781479974177 (ISBN) ; Karimi, S ; Ghaffari, A ; Hamidinekoo, A ; Vosoughi Vahdat, B ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2014
Abstract
Inferior temporal (IT) cortex is the most important part of the brain and plays an important role in response to visual stimuli. In this study, object decoding has been performed using neuron spikes in IT cortex region. Single Unit Activity (SUA) was recorded from 123 neurons in IT cortex. Pseudo-population firing rate vectors were created, then dimension reduction was done and an Artificial Neural Network (ANN) was used for object decoding. Object decoding accuracy was calculated for various window lengths from 50 ms to 200 ms and various window steps from 25 ms to 100 ms. The results show that 150 ms length and 50 ms window step size gives an optimum performance in average accuracy