PhD thesis topic - No. 7: Normal and pathological hearing, processing of clinical and psycho-acoustical data

Advisor: Petr Marsalek, MD, PhD


The task for the graduate student is to continue in processing of clinical and experimental data from collaborating institutions. The direction of the work will depend on which of the laboratories will participate in the project. Some of the laboratories and data are:

1) Department of cybernetics and artificial intelligence, Technical University Kosice, psycho-acoustical data, 2) Laboratory of Auditory Physiology and Pathology, Institute of Experimental Medicine, Czech Academy of Sciences, electrophysiological data, 3) Acoustics Research Institute, Austrian Academy of Sciences, psycho-acoustical data of both normal and hearing impaired subjects. We also collaborated previously with the Laboratory of biological cybernetics, Institute of pathological physiology, First Medical Faculty, Charles University on designing physiological models.

Specific aims are: 1) One aim is to obtain experimental data and analyze it using basic statistical methods. 2) The next is to compare the data with the outputs of neuronal models. 3) Skilled use of the Excel and Matlab programs is essential. The graduate student should also work on the web based user interface to the physiological models.

Literature

  1. Hruby, Marsalek, (2003) Event-Related Potentials — the P3 wave. Acta Neurobiol. Exp., 63: 55–63.

  2. R.F. Schmidt, Fundamentals of Sensory Physiology, Springer-Verlag, Berlin, 1985.

  3. Marsalek P. and Kofranek J. Spike encoding mechanisms in sound localization pathway. Biosystems, 79: 191-198, 2005.

  4. Marsalek P. and Drapal M., Mechanisms for Coincidence Detection in the Auditory Brainstem: Examples, In: Mathematical Modeling of Biological Systems, Vol. II., A. Deutsch, R. Bravo de la Parra, R. de Boer, O. Diekmann, P. Jagers, E. Kisdi, M. Kretzschmar, P. Lansky and H. Metz (eds). Birkhaeuser, Boston, 255-264, 2008

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PhD thesis topic - No. 8: Processing of phoneme-like brief complex sounds – modeling of auditory pathway

Advisor: Petr Marsalek, MD, PhD

We have more information about the visual processes in the cerebral cortex than about the auditory cortex. Koch and Ullman (1985) proposed a phenomenological model of visual attention. Based on this description, first Ernst Niebur, and then Laurent Itti (2000) released a software library implementing a „Model of selective visual attention“. Some modules of this library are tailored to simulate specific functions of the visual cortex, but other modules are general and can be used to simulate some other cerebral sensory areas, namely the auditory cortex. One of the modules also contains an implementation of the „leaky-integrate-and-fire neuronal model,“ (Marsalek et al, 1997). In this work, parameters and time constants of simplified neuronal models were identified with electrophysiological recordings in macaque monkeys. Data recorded in rat can be also used to identify model parameters, for example rat data are studied in (Villa et al., 1998). Some processing times of beginning stages in early perceptual processing are longer in humans than in monkeys and much shorter in rats, because they depend on conduction delays (Hillyard et al., 1998). For practical purposes, the stimuli used in this study will be phoneme-like brief complex sounds, lasting no longer than 250 ms.

Part of the proposed work requires technical skills in programming computer software. The software has to use drivers of sound cards and sound processing hardware. The software has to contain several components: 1) reader of the “WAV” and “MP3” formats, 2) At least two channels for the interaction of stereo sound, 3) Cochlear mechanisms, conversion of sound into spike trains, 4) The “leaky integrator” neuronal module 5) Implementation of conspicuous sound selection algorithm. 6) Sound source localization algorithm 7) Sound sequences recognition and classification.

The dissertation should contain two parts: in the theoretical part the aim is to study encoding of complex sounds, in the implementation part, the neuronal algorithms should be employed to process sounds on a digital sound processing hardware.

Literature

  1. Koch C, Ullman S. (1985) Shifts in selective visual attention: towards the underlying neural circuitry. Hum Neurobiol. 4(4):219–27.

  2. Itti L. and Koch C. (2000) A saliency-based search mechanism for overt and covert shifts of visual attention.Vision Res. 40(10–12):1489–506.

  3. Villa, A., Hyland, B., Tetko, I., and Najem, A. (1998) Dynamical cell assemblies in the rat auditory cortex in a reaction-time task. Biosystems, 48(1–3):269–77.

  4. Maršálek P., C. Koch C. and J. Maunsell J. (1997) On the relationship between synaptic input and spike output jitter in individual neurons, Proc. Natl. Acad. Sci. USA, 94: 735–740

  5. Hillyard SA, Teder-Salejarvi WA and Munte TF.(1998) Temporal dynamics of early perceptual processing. Curr Opin Neurobiol. 8(2):202–10.

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PhD thesis topic - No. 9: Neuronal circuits for flight control reflexes in the fruit fly (Drosophila melanogaster)

Advisor: Petr Marsalek, MD, PhD

Michael Dickinson (2000) and his group at the California Institute of Technology studies the flight behavior of both free flying and tethered fruit fly (Drosophila melanogaster). Based on their data, questions arise: How can be the fast fly maneuvers executed by neuronal circuits? We constructed a model in the Matlab software package, which reproduces some observations on flight reflexes and predicts the outcome of some experiments yet to be done. The aim of the thesis is to improve the maneuvers of the fly produced as output from the model under different conditions and parameter values. This output has to be compared to experimental recordings This project should follow the project „Neuronal Control of Flight in Drosophila,“ originally pursued at the Max-Planck Institute for the Physics of Complex Systems in Dresden in recent years.

Literature

  1. Dickinson MH, Farley CT, Full RJ, Koehl MA, Kram R and Lehman S., (2000) How animals move: an integrative view. Science. 288(5463):100–6.

  2. Yu Sun; Potasek, D.P.; Bell, D.J.; Fry, S.N. and Nelson, B.J.; (2004) Drosophila flight force measurements using a MEMS micro force sensor, Engineering in Medicine and Biology Society, 2004. EMBC 2004. Conference Proceedings. 26th Annual International Conference of the IEEE CNF, pp. 2014–2017 Vol.3

  3. Frye MA. and Gray JR., (2005) Mechanosensory integration for flight control in insects, in Methods in insect sensory neuroscience, Christensen TA. (ed.), Boca Raton: CRC Press. 107—128.

  4. Maršálek P., C. Koch C. and J. Maunsell J. (1997) On the relationship between synaptic input and spike output jitter in individual neurons, Proc. Natl. Acad. Sci. USA, 94: 735–740

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PhD thesis topic - No. 10: Recognition of complex sounds by a neuronal synchronization

Advisor: Petr Marsalek, MD, PhD

In two publications (the first one presented as a contest to solve a problem for a price, and the second one with the problem solution) Hopfield and Brody (2000 and 2001) proposed a mechanism for detecting groups of neurons receiving similar levels of sensory input. The task was to describe the architecture of an artificial neuronal network, similar to biological network. The description had to be based on „recordings“ from the network, which have many features common with real neuro-physiological recordings. The network, dubbed „mus silicium,“ (silicon mouse in Latin) does the job with the use of synchronization of action potentials. Krchak (2006) in his master’s thesis presented a generalized solution to the problem of Hopfield and Brody. This solution also contains a limited search through the parametr space for optimal parameter values.

The first task for the graduate student is to produce a next version of source code to this neural network model (either based on some solution by the people participating in the contest in 2000, or based on the solution of Krchak, or find a new, own solution). The second task is to perform analysis of the parameter space in order to replace the heuristic approach of Krchak by systematic exploration of parameters with respect to neuronal dynamics (this task is, however, quite demanding and requiring higher mathematics).

Literature

  1. P. Marsalek, C. Koch and J. Maunsell: On the relationship between synaptic input and spike output jitter in individual neurons, Proc. Natl. Acad. Sci. USA, Vol. 94, pp. 735–740, 1997

  2. J.J.Hopfield and C.D. Brody: What is a moment? ‘‘Cortical’’ sensory integration over a brief interval, Proc. Natl. Acad. Sci. USA, Vol. 97 , pp. 13919–24, 2000

  3. J.J.Hopfield and C.D. Brody: What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration, Proc. Natl. Acad. Sci. USA, Vol. 98, pp. 1282–87, 2001

  4. Krchak, J. A neuronal network recognizing complex sounds, Master’s thesis, Faculty of mathematics and physics, Charles University, 2006, in Czech, advisor: P. Marsalek

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PhD thesis topic - No. 11: Models of sound localization in mammals

Advisor: Petr Marsalek, MD, PhD


This project proposes to study the mechanisms by which the human brain extracts spatial information from the acoustic environment. This information is used to create an internal representation of the auditory scene and to comprehend speech. The graduate student will be involved in theoretical studies, design of mathematical models, algorithm development, their implementation in a programming language, hardware emulation and as a last mandatory step psychophysical behavioral experiments on human volunteers. We expect results in: 1) improved understanding of how normal hearing listeners and hearing impaired listeners localize sounds and process speech in complex environments, considering both bottom-up and top-down factors; 2) computational models of various aspects of spatial auditory processing in complex environments.


Literature

  1. Marsalek P., Koch C. and Maunsell J. (1997) On the relationship between synaptic input and spike output jitter in individual neurons, Proc. Natl. Acad. Sci. USA, 94: 735–740

  2. Marsalek P. and Lansky P. Proposed mechanisms for coincidence detection in the auditory brainstem. Biol. Cybern., 92(6): 445–51, 2005.

  3. Patterson R.D, Robinson K., Holdsworth J, McKeown D, Zhang C., and Allerhand M.H., (1992) Complex sounds and auditory images, In Auditory Physiology and Perception, (Eds.) Y Cazals, L. Demany, K.Horner, Pergamon, Oxford, pp. 429–446.

  4. Marsalek P. and Drapal M., Mechanisms for Coincidence Detection in the Auditory Brainstem: Examples, In: Mathematical Modeling of Biological Systems, Vol. II., A. Deutsch, R. Bravo de la Parra, R. de Boer, O. Diekmann, P. Jagers, E. Kisdi, M. Kretzschmar, P. Lansky and H. Metz (eds). Birkhaeuser, Boston, 255-264, 2008