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Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural models. In the present thesis, we introduce several recurrent network models of threshold units that combine spike timing dependent plasticity with homeostatic plasticity mechanisms like intrinsic plasticity or synaptic normalization. We investigate how these different forms of plasticity shape the dynamics and computational properties of recurrent networks. The networks receive input sequences composed of different symbols and learn the structure embedded in these sequences in an unsupervised manner. Information is encoded in the form of trajectories through a high-dimensional state space reminiscent of recent biological findings on cortical coding. We find that these self-organizing plastic networks are able to represent and "understand" the spatio-temporal patterns in their inputs while maintaining their dynamics in a healthy regime suitable for learning. The emergent properties are not easily predictable on the basis of the individual plasticity mechanisms at work. Our results underscore the importance of studying the interaction of different forms of plasticity on network behavior.
A framework for the analysis and visualization of multielectrode spike trains / von Ovidiu F. Jurjut
(2009)
The brain is a highly distributed system of constantly interacting neurons. Understanding how it gives rise to our subjective experiences and perceptions depends largely on understanding the neuronal mechanisms of information processing. These mechanisms are still poorly understood and a matter of ongoing debate remains the timescale on which the coding process evolves. Recently, multielectrode recordings of neuronal activity have begun to contribute substantially to elucidating how information coding is implemented in brain circuits. Unfortunately, analysis and interpretation of multielectrode data is often difficult because of their complexity and large volume. Here we propose a framework that enables the efficient analysis and visualization of multielectrode spiking data. First, using self-organizing maps, we identified reoccurring multi-neuronal spike patterns that evolve on various timescales. Second, we developed a color-based visualization technique for these patterns. They were mapped onto a three-dimensional color space based on their reciprocal similarities, i.e., similar patterns were assigned similar colors. This innovative representation enables a quick and comprehensive inspection of spiking data and provides a qualitative description of pattern distribution across entire datasets. Third, we quantified the observed pattern expression motifs and we investigated their contribution to the encoding of stimulus-related information. An emphasis was on the timescale on which patterns evolve, covering the temporal scales from synchrony up to mean firing rate. Using our multi-neuronal analysis framework, we investigated data recorded from the primary visual cortex of anesthetized cats. We found that cortical responses to dynamic stimuli are best described as successions of multi-neuronal activation patterns, i.e., trajectories in a multidimensional pattern space. Patterns that encode stimulus-specific information are not confined to a single timescale but can span a broad range of timescales, which are tightly related to the temporal dynamics of the stimuli. Therefore, the strict separation between synchrony and mean firing rate is somewhat artificial as these two represent only extreme cases of a continuum of timescales that are expressed in cortical dynamics. Results also indicate that timescales consistent with the time constants of neuronal membranes and fast synaptic transmission (~10-20 ms) appear to play a particularly salient role in coding, as patterns evolving on these timescales seem to be involved in the representation of stimuli with both slow and fast temporal dynamics.
Algorithms and data structures constitute the theoretical foundations of computer science and are an integral part of any classical computer science curriculum. Due to their high level of abstraction, the understanding of algorithms is of crucial concern to the vast majority of novice students. To facilitate the understanding and teaching of algorithms, a new research field termed "algorithm visualisation" evolved in the early 1980's. This field is concerned with innovating techniques and concepts for the development of effective algorithm visualisations for teaching, study, and research purposes. Due to the large number of requirements that high-quality algorithm visualisations need to meet, developing and deploying effective algorithm visualisations from scratch is often deemed to be an arduous, time-consuming task, which necessitates high-level skills in didactics, design, programming and evaluation. A substantial part of this thesis is devoted to the problems and solutions related to the automation of three-dimensional visual simulation of algorithms. The scientific contribution of the research presented in this work lies in addressing three concerns: - Identifying and investigating the issues related to the full automation of visual simulations. - Developing an automation-based approach to minimising the effort required for creating effective visual simulations. - Designing and implementing a rich environment for the visualisation of arbitrary algorithms and data structures in 3D. The presented research in this thesis is of considerable interest to (1) researchers anxious to facilitate the development process of algorithm visualisations, (2) educators concerned with adopting algorithm visualisations as a teaching aid and (3) students interested in developing their own algorithm animations.