Detection and analysis of polychronous groups emerging in spiking neural network models.

• Thesis title in Czech: Detekce a analýza polychronních skupin neuronů v spikujících sítích.
• Thesis title in English: Detection and analysis of polychronous groups emerging in spiking neural network models.
• Key words: neuronové sítě, polychronní skupiny, spikující neurony, sluchová kůra
• English key words: neural networks, polychronous groups, spiking neurons, auditory cortex
• Academic year of topic announcement: 2015/2016
• Thesis type: diploma thesis
• Thesis language: angličtina
• Department: Department of Software and Computer Science Education (32-KSVI)
• Supervisor: doc. Mgr. Cyril Brom, Ph.D.
• Author: hidden - assigned and confirmed by the Study Dept.
• Date of registration: 26.11.2015
• Date of assignment: 03.12.2015
• Confirmed by Study dept. on: 16.12.2015
• Date and time of defence: 29.01.2018 00:00
• Date of electronic submission: 04.01.2018
• Date of submission of printed version: 04.01.2018
• Date of proceeded defence: 29.01.2018
• Opponents: Mgr. Josef Moudřík
• Advisors: Ing. Markéta Tomková

Guidelines

Efforts in increasing realism of neural network models have led to development of spiking neural networks. It has been proposed that due to the nature of synaptic connections with varying delays, such networks can exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision. Neurons participating in such firing pattens are called polychronous groups.

The aim of this thesis is to adapt an existing spiking neural network model (Popelova et al., 2015) and to provide a software tool for polychronous groups detection. Several algorithms for detection of stimuli-based polychronous groups as well as polychrounous groups emerging during spontaneous activity will be implemented. The existing neural model will be revised and extended to provide more plausible computation and polychronous group detection. In addition to the software implementation, several experiments with varying model inputs will be performed to observe how polychronous groups develop in response to different stimuli. Such observations could provide an aid in understanding how information is processed and represented in neural networks which remains an open question in cognitive science.

References

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Eugene M. Izhikevich. 2006. Polychronization: Computation with Spikes.
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Kiselev, M. V. Homogenous Chaotic Network Serving as a
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Tomková, M., Tomek, J., Novák, O., Zelenka, O., Syka, J., & Brom, C.
(2015). Formation and disruption of tonotopy in a large-scale model of the
auditory cortex. Journal of computational neuroscience, 39(2), 131-153.