147 research outputs found

    Annotated Bibliography: Anticipation

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    Pathophysiological mechanisms of absence epilepsy: a computational modelling study

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    A typical absence is a non-convulsive epileptic seizure that is a sole symptom of childhood absence epilepsy (CAE). It is characterised by a generalised hyper-synchronous activity (2.5-5 Hz) of neurons in the thalamocortical network that manifests as a spike and slow-wave discharge (SWD) in the electroencephalogram. Although CAE is not a benign form of epilepsy, its physiological basis is not well understood. In an attempt to make progress regarding the mechanism of SWDs, I built a large-scale computational model of the thalamocortical network that replicated key cellular and network electric oscillatory behaviours. Model simulation indicated that there are multiple pathological pathways leading to SWDs. They fell into three categories depending on their network-level effects. Moreover, all SWDs had the same physiological mechanism of generation irrespective of their underlying pathology. They were initiated by an increase in NRT cell bursting prior to the SWD onset. SWDs critically depended on the T-type Ca2+ current (IT) mediated firing in NRT and higher-order thalamocortical relay cells (TCHO), as well as GABAB synaptic receptor-mediated IPSPs in TCHO cells. On the other hand, first-order thalamocortical cells were inhibited during SWDs and did not actively participate in their generation. These cells, however, could promote or disrupt SWD generation if they were hyperpolarised or depolarised, respectively. Importantly, only a minority of active TC cells with a small proportion of them bursting were necessary to ensure the SWD generation. In terms of their relationship to other brain rhythms, simulated SWDs were a product of NRT sleep spindle (6.5-14 Hz) and cortical δ (1-4 Hz) pacemakers and had their oscillation frequency settle between the preferred oscillation frequencies of the two pacemakers with the actual value depending on the cortical bursting intensity. These modelling results are discussed in terms of their implications for understanding CAE and its future research and treatment

    Pathophysiological mechanisms of absence epilepsy: a computational modelling study

    Get PDF
    A typical absence is a non-convulsive epileptic seizure that is a sole symptom of childhood absence epilepsy (CAE). It is characterised by a generalised hyper-synchronous activity (2.5-5 Hz) of neurons in the thalamocortical network that manifests as a spike and slow-wave discharge (SWD) in the electroencephalogram. Although CAE is not a benign form of epilepsy, its physiological basis is not well understood. In an attempt to make progress regarding the mechanism of SWDs, I built a large-scale computational model of the thalamocortical network that replicated key cellular and network electric oscillatory behaviours. Model simulation indicated that there are multiple pathological pathways leading to SWDs. They fell into three categories depending on their network-level effects. Moreover, all SWDs had the same physiological mechanism of generation irrespective of their underlying pathology. They were initiated by an increase in NRT cell bursting prior to the SWD onset. SWDs critically depended on the T-type Ca2+ current (IT) mediated firing in NRT and higher-order thalamocortical relay cells (TCHO), as well as GABAB synaptic receptor-mediated IPSPs in TCHO cells. On the other hand, first-order thalamocortical cells were inhibited during SWDs and did not actively participate in their generation. These cells, however, could promote or disrupt SWD generation if they were hyperpolarised or depolarised, respectively. Importantly, only a minority of active TC cells with a small proportion of them bursting were necessary to ensure the SWD generation. In terms of their relationship to other brain rhythms, simulated SWDs were a product of NRT sleep spindle (6.5-14 Hz) and cortical δ (1-4 Hz) pacemakers and had their oscillation frequency settle between the preferred oscillation frequencies of the two pacemakers with the actual value depending on the cortical bursting intensity. These modelling results are discussed in terms of their implications for understanding CAE and its future research and treatment

    29th Annual Computational Neuroscience Meeting: CNS*2020

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    Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

    A model for cerebral cortical neuron group electric activity and its implications for cerebral function

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 245-265).The electroencephalogram, or EEG, is a recording of the field potential generated by the electric activity of neuronal populations of the brain. Its utility has long been recognized as a monitor which reflects the vigilance states of the brain, such as arousal, drowsiness, and sleep stages. Moreover, it is used to detect pathological conditions such as seizures, to calibrate drug action during anesthesia, and to understand cognitive task signatures in healthy and abnormal subjects. Being an aggregate measure of neural activity, understanding the neural origins of EEG oscillations has been limited. With the advent of recording techniques, however, and as an influx of experimental evidence on cellular and network properties of the neocortex has become available, a closer look into the neuronal mechanisms for EEG generation is warranted. Accordingly, we introduce an effective neuronal skeleton circuit at a neuronal group level which could reproduce basic EEG-observable slow ( 3mm). The effective circuit makes use of the dynamic properties of the layer 5 network to explain intra-cortically generated augmenting responses, restful alpha, slow wave (< 1Hz) oscillations, and disinhibition-induced seizures. Based on recent cellular evidence, we propose a hierarchical binding mechanism in tufted layer 5 cells which acts as a controlled gate between local cortical activity and inputs arriving from distant cortical areas. This gate is manifested by the switch in output firing patterns in tufted(cont.) layer 5 cells between burst firing and regular spiking, with specific implications on local functional connectivity. This hypothesized mechanism provides an explanation of different alpha band (10Hz) oscillations observed recently under cognitive states. In particular, evoked alpha rhythms, which occur transiently after an input stimulus, could account for initial reogranization of local neural activity based on (mis)match between driving inputs and modulatory feedback of higher order cortical structures, or internal expectations. Emitted alpha rhythms, on the other hand, is an example of extreme attention where dominance of higher order control inputs could drive reorganization of local cortical activity. Finally, the model makes predictions on the role of burst firing patterns in tufted layer 5 cells in redefining local cortical dynamics, based on internal representations, as a prelude to high frequency oscillations observed in various sensory systems during cognition.by Fadi Nabih Karameh.Ph.D

    Unified developmental model of maps, complex cells and surround modulation in the primary visual cortex

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    For human and animal vision, the perception of local visual features can depend on the spatial arrangement of the surrounding visual stimuli. In the earliest stages of visual processing this phenomenon is called surround modulation, where the response of visually selective neurons is influenced by the response of neighboring neurons. Surround modulation has been implicated in numerous important perceptual phenomena, such as contour integration and figure-ground segregation. In cats, one of the major potential neural substrates for surround modulation are lateral connections between cortical neurons in layer 2/3, which typically contains ”complex” cells that appear to combine responses from ”simple” cells in layer 4C. Interestingly, these lateral connections have also been implicated in the development of functional maps in primary visual cortex, such as smooth, well-organized maps for the preference of oriented lines. Together, this evidence suggests a common underlying substrate the lateral interactions in layer 2/3—as the driving force behind development of orientation maps for both simple and complex cells, and at the same time expression of surround modulation in adult animals. However, previously these phenomena have been studied largely in isolation, and we are not aware of a computational model that can account for all of them simultaneously and show how they are related. In this thesis we resolve this problem by building a single, unified computational model that can explain the development of orientation maps, the development of simple and complex cells, and surround modulation. First we build a simple, single-layer model of orientation map development based on ALISSOM, which has more realistic single cell properties (such as contrast gain control and contrast invariant orientation tuning) than its predecessor. Then we extend this model by adding layer 2/3, and show how the model can explain development of orientation maps of both simple and complex cells. As the last step towards a developmental model of surround modulation, we replace Mexican-hat-like lateral connectivity in layer 2/3 of the model with a more realistic configuration based on long-range excitation and short-range inhibitory cells, extending a simpler model by Judith Law. The resulting unified model of V1 explains how orientation maps of simple and complex cells can develop, while individual neurons in the developed model express realistic orientation tuning and various surround modulation properties. In doing so, we not only offer a consistent explanation behind all these phenomena, but also create a very rich model of V1 in which the interactions between various V1 properties can be studied. The model allows us to formulate several novel predictions that relate the variation of single cell properties to their location in the orientation preference maps in V1, and we show how these predictions can be tested experimentally. Overall, this model represents a synthesis of a wide body of experimental evidence, forming a compact hypothesis for much of the development and behavior of neurons in the visual cortex

    Development and encoding of visual statistics in the primary visual cortex

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    How do circuits in the mammalian cerebral cortex encode properties of the sensory environment in a way that can drive adaptive behavior? This question is fundamental to neuroscience, but it has been very difficult to approach directly. Various computational and theoretical models can explain a wide range of phenomena observed in the primary visual cortex (V1), including the anatomical organization of its circuits, the development of functional properties like orientation tuning, and behavioral effects like surround modulation. However, so far no model has been able to bridge these levels of description to explain how the machinery that develops directly affects behavior. Bridging these levels is important, because phenomena at any one specific level can have many possible explanations, but there are far fewer possibilities to consider once all of the available evidence is taken into account. In this thesis we integrate the information gleaned about cortical development, circuit and cell-type specific interactions, and anatomical, behavioral and electrophysiological measurements, to develop a computational model of V1 that is constrained enough to make predictions across multiple levels of description. Through a series of models incorporating increasing levels of biophysical detail and becoming increasingly better constrained, we are able to make detailed predictions for the types of mechanistic interactions required for robust development of cortical maps that have a realistic anatomical organization, and thereby gain insight into the computations performed by the primary visual cortex. The initial models focus on how existing anatomical and electrophysiological knowledge can be integrated into previously abstract models to give a well-grounded and highly constrained account of the emergence of pattern-specific tuning in the primary visual cortex. More detailed models then address the interactions between specific excitatory and inhibitory cell classes in V1, and what role each cell type may play during development and function. Finally, we demonstrate how these cell classes come together to form a circuit that gives rise not only to robust development but also the development of realistic lateral connectivity patterns. Crucially, these patterns reflect the statistics of the visual environment to which the model was exposed during development. This property allows us to explore how the model is able to capture higher-order information about the environment and use that information to optimize neural coding and aid the processing of complex visual tasks. Using this model we can make a number of very specific predictions about the mechanistic workings of the brain. Specifically, the model predicts a crucial role of parvalbumin-expressing interneurons in robust development and divisive normalization, while it implicates somatostatin immunoreactive neurons in mediating longer range and feature-selective suppression. The model also makes predictions about the role of these cell classes in efficient neural coding and under what conditions the model fails to organize. In particular, we show that a tight coupling of activity between the principal excitatory population and the parvalbumin population is central to robust and stable responses and organization, which may have implications for a variety of diseases where parvalbumin interneuron function is impaired, such as schizophrenia and autism. Further the model explains the switch from facilitatory to suppressive surround modulation effects as a simple by-product of the facilitating response function of long-range excitatory connections targeting a specialized class of inhibitory interneurons. Finally, the model allows us to make predictions about the statistics that are encoded in the extensive network of long-range intra-areal connectivity in V1, suggesting that even V1 can capture high-level statistical dependencies in the visual environment. The final model represents a comprehensive and well constrained model of the primary visual cortex, which for the first time can relate the physiological properties of individual cell classes to their role in development, learning and function. While the model is specifically tuned for V1, all mechanisms introduced are completely general, and can be used as a general cortical model, useful for studying phenomena across the visual cortex and even the cortex as a whole. This work is also highly relevant for clinical neuroscience, as the cell types studied here have been implicated in neurological disorders as wide ranging as autism, schizophrenia and Parkinson’s disease
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