Detection and analysis of polychronous groups emerging in spiking neural network models.
| Název práce v češtině: | Detekce a analýza polychronních skupin neuronů v spikujících sítích. |
|---|---|
| Název v anglickém jazyce: | Detection and analysis of polychronous groups emerging in spiking neural network models. |
| Klíčová slova: | neuronové sítě, polychronní skupiny, spikující neurony, sluchová kůra |
| Klíčová slova anglicky: | neural networks, polychronous groups, spiking neurons, auditory cortex |
| Akademický rok vypsání: | 2015/2016 |
| Typ práce: | diplomová práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra softwaru a výuky informatiky (32-KSVI) |
| Vedoucí / školitel: | doc. Mgr. Cyril Brom, Ph.D. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 26.11.2015 |
| Datum zadání: | 03.12.2015 |
| Datum potvrzení stud. oddělením: | 16.12.2015 |
| Datum a čas obhajoby: | 29.01.2018 00:00 |
| Datum odevzdání elektronické podoby: | 04.01.2018 |
| Datum odevzdání tištěné podoby: | 04.01.2018 |
| Datum proběhlé obhajoby: | 29.01.2018 |
| Oponenti: | Mgr. Josef Moudřík |
| Konzultanti: | Ing. Markéta Tomková |
| Zásady pro vypracování |
| 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. |
| Seznam odborné literatury |
| Abeles, M., Gat, I. Detecting precise firing sequences in experimental data,
Journal of Neuroscience Methods, Volume 107, Issues 1–2, 30 May 2001, pp. 141-154, ISSN 0165-0270. Averbeck, B.B., et al., Neural correlations, population coding and computation, Nat. Rev. Neurosci., 7 (2006), pp. 358–366. Eugene M. Izhikevich. 2006. Polychronization: Computation with Spikes. Neural Comput. 18, 2 (February 2006), 245-282. Kiselev, M. V. Homogenous Chaotic Network Serving as a Rate/Population Code to Temporal Code Converter, Computational Intelligence and Neuroscience, vol. 2014, Article ID 476580, 8 pages, 2014. Martinez, R., Paugam-Moisy, H. Algorithms for structural and dynamical polychronous groups detection. ICANN'2009, International Conference on Artificial Neural Networks, Sep 2009, Limassol, Cyprus. Springer, 5769, pp.75-84, 2009, LNCS, Lecture Notes in Computer Science; Artificial Neural Networks - ICANN 2009. Sun, H., Yang, Y., Sourina, O., Huang, G. Runtime detection of activated polychronous neuronal group towards its spatiotemporal analysis, in International Joint Conference on Neural Networks (IJCNN) 2015, pp.1-8, 12-17 July 2015 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. |