Noise induced processes in neural systems

Real neurons, and their networks, are far too complex to be described exactly by simple deterministic equations. Any description of their dynamics must therefore incorporate noise to some degree. It is my thesis that the nervous system is organized in such a way that its performance is optimal, subject to this constraint. I further contend that neuronal dynamics may even be enhanced by noise, when compared with their deterministic counter-parts. To support my thesis I will present and analyze three case studies. I will show how noise might (i) extend the dynamic range of mammalian cold-receptors and other cells that exhibit a temperature-dependent discharge; (ii) feature in the perception of ambiguous figures such as the Necker cube; (iii) alter the discharge pattern of single cells

Action selection in the rhythmic brain: The role of the basal ganglia and tremor.

Low-frequency oscillatory activity has been the target of extensive research both in cortical structures and in the basal ganglia (BG), due to numerous reports of associations with brain disorders and the normal functioning of the brain. Additionally, a plethora of evidence and theoretical work indicates that the BG might be the locus where conflicts between prospective actions are being resolved. Whereas a number of computational models of the BG investigate these phenomena, these models tend to focus on intrinsic oscillatory mechanisms, neglecting evidence that points to the cortex as the origin of this oscillatory behaviour. In this thesis, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. To do so, we build a complete suite of computational tools for the design, optimization and simulation of spiking neural networks. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG, and it was used to make a number of biologically-plausible predictions. First, we investigate the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. Our results indicate the existence of a multimodal gating mechanism at the level of the BG that can be entirely controlled by cortical oscillations, and provide evidence for the hypothesis of cortically-entrained but locally-generated subthalamic beta activity. Next, we explore the relationship of wave properties of entrained cortical inputs, dopamine and the transient effectiveness of the BG, when viewed as an action selection device. We found that cortical frequency, phase, dopamine and the examined time scale, all have a very important impact on the ability of our model to select. Our simulations resulted in a canonical profile of selectivity, which we termed selectivity portraits. Taking together, our results suggest that the cortex is the structure that determines whether action selection will be performed and what strategy will be utilized while the role of the BG is to perform this selection. Some frequency ranges promote the exploitation of actions of whom the outcome is known, others promote the exploration of new actions with high uncertainty while the remaining frequencies simply deactivate selection. Based on this behaviour, we propose a metaphor according to which, the basal ganglia can be viewed as the ''gearbox" of the cortex. Coalitions of rhythmic cortical areas are able to switch between a repertoire of available BG modes which, in turn, change the course of information flow back to and within the cortex. In the same context, dopamine can be likened to the ''control pedals" of action selection that either stop or initiate a decision. Finally, the frequency of active cortical areas that project to the BG acts as a gear lever, that instead of controlling the type and direction of thrust that the throttle provides to an automobile, it dictates the extent to which dopamine can trigger a decision, as well as what type of decision this will be. Finally, we identify a selection cycle with a period of around 200 ms, which was used to assess the biological plausibility of the most popular architectures in cognitive science. Using extensions of the BG model, we further propose novel mechanisms that provide explanations for (1) the two distinctive dynamical behaviours of neurons in globus pallidus external, and (2) the generation of resting tremor in Parkinson's disease. Our findings agree well with experimental observations, suggest new insights into the pathophysiology of specific BG disorders, provide new justifications for oscillatory phenomena related to decision making and reaffirm the role of the BG as the selection centre of the brain.Open Acces

Community-Derived Core Concepts for Neuroscience Higher Education

Core concepts provide a framework for organizing facts and understanding in neuroscience higher education curricula. Core concepts are overarching principles that identify patterns in neuroscience processes and phenomena and can be used as a foundational scaffold for neuroscience knowledge. The need for community-derived core concepts is pressing, because both the pace of research and number of neuroscience programs are rapidly expanding. While general biology and many subdisciplines within biology have identified core concepts, neuroscience has yet to establish a community-derived set of core concepts for neuroscience higher education. We used an empirical approach involving more than 100 neuroscience educators to identify a list of core concepts. The process of identifying neuroscience core concepts was modeled after the process used to develop physiology core concepts and involved a nationwide survey and a working session of 103 neuroscience educators. The iterative process identified eight core concepts and accompanying explanatory paragraphs. The eight core concepts are abbreviated as communication modalities, emergence, evolution, gene–environment interactions, information processing, nervous system functions, plasticity, and structure–function. Here, we describe the pedagogical research process used to establish core concepts for the neuroscience field and provide examples on how the core concepts can be embedded in neuroscience education

Variability in Singing and in Song in the Zebra Finch

Variability is a defining feature of the oscine song learning process, reflected in song and in the neural pathways involved in song learning. For the zebra finch, juveniles learning to sing typically exhibit a high degree of vocal variability, and this variability appears to be driven by a key brain nucleus. It has been suggested that this variability is a necessary part of a trial-­â€and-­â€error learning process in which the bird must search for possible improvements to its song. Our work examines the role this variability plays in learning in two ways: through behavioral experiments with juvenile zebra finches, and through a computational model of parts of the oscine brain. Previous studies have shown that some finches exhibit less variability during the learning process than others by producing repetitive vocalizations. A constantly changing song model was played to juvenile zebra finches to determine whether auditory stimuli can affect this behavior. This stimulus was shown to cause an overall increase in repetitiveness; furthermore, there was a correlation between repetitiveness at an early stage in the learning process and the length of time a bird is repetitive overall, and birds that were repetitive tended to repeat the same thing over an extended period of time. The role of a key brain nucleus involved in song learning was examined through computational modeling. Previous studies have shown that this nucleus produces variability in song, but can also bias the song of a bird in such a way as to reduce errors while singing. Activity within this nucleus during singing is predominantly uncorrelated with the timing of the song, however a portion of this activity is correlated in such a manner. The modeling experiments consider the possibility that this persistent signal is part of a trial-­â€and-­â€error search and contrast this with the possibility that the persistent signal is the product of some mechanism to directly improve song. Simulation results show that a mixture of timing-­â€dependent and timing-­â€independent activity in this nucleus produces optimal learning results for the case where the persistent signal is a key component of a trial-­â€and-­â€error search, but not in the case where this signal will directly improve song. Although a mixture of timing-­â€locked and timing-­â€independent activity produces optimal results, the ratio found to be optimal within the model differs from what has been observed in vivo. Finally, novel methods for the analysis of birdsong, motivated by the high variability of juvenile song, are presented. These methods are designed to work with sets of song samples rather than through pairwise comparison. The utility of these methods is demonstrated, as well as results illustrating how such methods can be used as the basis for aggregate measures of song such as repertoire complexity