Cognitive Learning and Memory Systems Using Spiking Neural Networks

Ph.DDOCTOR OF PHILOSOPH

Towards Brains in the Cloud: A Biophysically Realistic Computational Model of Olfactory Bulb

abstract: The increasing availability of experimental data and computational power have resulted in increasingly detailed and sophisticated models of brain structures. Biophysically realistic models allow detailed investigations of the mechanisms that operate within those structures. In this work, published mouse experimental data were synthesized to develop an extensible, open-source platform for modeling the mouse main olfactory bulb and other brain regions. A “virtual slice” model of a main olfactory bulb glomerular column that includes detailed models of tufted, mitral, and granule cells was created to investigate the underlying mechanisms of a gamma frequency oscillation pattern (“gamma fingerprint”) often observed in rodent bulbar local field potential recordings. The gamma fingerprint was reproduced by the model and a mechanistic hypothesis to explain aspects of the fingerprint was developed. A series of computational experiments tested the hypothesis. The results demonstrate the importance of interactions between electrical synapses, principal cell synaptic input strength differences, and granule cell inhibition in the formation of the gamma fingerprint. The model, data, results, and reproduction materials are accessible at https://github.com/justasb/olfactorybulb. The discussion includes a detailed description of mechanisms underlying the gamma fingerprint and how the model predictions can be tested experimentally. In summary, the modeling platform can be extended to include other types of cells, mechanisms and brain regions and can be used to investigate a wide range of experimentally testable hypotheses.Dissertation/ThesisDoctoral Dissertation Neuroscience 201

Hippocampal GABAergic inhibitory interneurons

In the hippocampus GABAergic local circuit inhibitory interneurons represent only ~10–15% of the total neuronal population; however, their remarkable anatomical and physiological diversity allows them to regulate virtually all aspects of cellular and circuit function. Here we provide an overview of the current state of the field of interneuron research, focusing largely on the hippocampus. We discuss recent advances related to the various cell types, including their development and maturation, expression of subtype-specific voltage- and ligand-gated channels, and their roles in network oscillations. We also discuss recent technological advances and approaches that have permitted high-resolution, subtype-specific examination of their roles in numerous neural circuit disorders and the emerging therapeutic strategies to ameliorate such pathophysiological conditions. The ultimate goal of this review is not only to provide a touchstone for the current state of the field, but to help pave the way for future research by highlighting where gaps in our knowledge exist and how a complete appreciation of their roles will aid in future therapeutic strategies

Hippocampal gabaergic inhibitory interneurons

This is the author accepted manuscript. The final version is available from American Physiological Society via the DOI in this record In the hippocampus GABAergic local circuit inhibitory interneurons represent only ~10–15% of the total neuronal population; however, their remarkable anatomical and physiological diversity allows them to regulate virtually all aspects of cellular and circuit function. Here we provide an overview of the current state of the field of interneuron research, focusing largely on the hippocampus. We discuss recent advances related to the various cell types, including their development and maturation, expression of subtype-specific voltage-and ligand-gated channels, and their roles in network oscillations. We also discuss recent technological advances and approaches that have permitted high-resolution, subtype-specific examination of their roles in numerous neural circuit disorders and the emerging therapeutic strategies to ameliorate such pathophysiological conditions. The ultimate goal of this review is not only to provide a touchstone for the current state of the field, but to help pave the way for future research by highlighting where gaps in our knowledge exist and how a complete appreciation of their roles will aid in future therapeutic strategies.National Institute of Child Health and Human Developmen

Detecting cells and cellular activity from two-photon calcium imaging data

To understand how networks of neurons process information, it is essential to monitor their activity in living tissue. Information is transmitted between neurons by electrochemical impulses called action potentials or spikes. Calcium-sensitive fluorescent probes, which emit a characteristic pulse of fluorescence in response to a spike, are used to visualise spiking activity. Combined with two-photon microscopy, they enable the spiking activity of thousands of neurons to be monitored simultaneously at single-cell and single-spike resolution. In this thesis, we develop signal processing tools for detecting cells and cellular activity from two-photon calcium imaging data. Firstly, we present a method to detect the locations of cells within a video. In our framework, an active contour evolves guided by a model-based cost function to identify a cell boundary. We demonstrate that this method, which includes no assumptions about typical cell shape or temporal activity, is able to detect cells with varied properties from real imaging data. Once the location of a cell has been identified, its spiking activity must be inferred from the fluorescence signal. We present a metric that quantifies the similarity between inferred spikes and the ground truth. The proposed metric assesses the similarity of pulse trains obtained from convolution of the spike trains with a smoothing pulse, whose width is derived from the statistics of the data. We demonstrate that the proposed metric is more sensitive than existing metrics to the temporal and rate precision of inferred spike trains. Finally, we extend an existing framework for spike inference to accommodate a wider class of fluorescence signals. Our method, which is based on finite rate of innovation theory, exploits the known parametric structure of the signal to infer the unknown spike times. On in vitro imaging data, we demonstrate that the updated algorithm outperforms a state of the art approach.Open Acces

Information Processing by Neuron Populations in the Central Nervous System: Mathematical Structure of Data and Operations

In the intricate architecture of the mammalian central nervous system, neurons form populations. Axonal bundles communicate between these clusters using spike trains as their medium. However, these neuron populations' precise encoding and operations have yet to be discovered. In our analysis, the starting point is a state-of-the-art mechanistic model of a generic neuron endowed with plasticity. From this simple framework emerges a profound mathematical construct: The representation and manipulation of information can be precisely characterized by an algebra of finite convex cones. Furthermore, these neuron populations are not merely passive transmitters. They act as operators within this algebraic structure, mirroring the functionality of a low-level programming language. When these populations interconnect, they embody succinct yet potent algebraic expressions. These networks allow them to implement many operations, such as specialization, generalization, novelty detection, dimensionality reduction, inverse modeling, prediction, and associative memory. In broader terms, this work illuminates the potential of matrix embeddings in advancing our understanding in fields like cognitive science and AI. These embeddings enhance the capacity for concept processing and hierarchical description over their vector counterparts.Comment: 34 pages, 12 figure