Open Access. Powered by Scholars. Published by Universities.®
- Institution
Articles 1 - 5 of 5
Full-Text Articles in Engineering
Elucidating And Leveraging Dynamics-Function Relationships In Neural Circuits Through Modeling And Optimal Control, Sruti Mallik
Elucidating And Leveraging Dynamics-Function Relationships In Neural Circuits Through Modeling And Optimal Control, Sruti Mallik
McKelvey School of Engineering Theses & Dissertations
A fundamental research question in neuroscience pertains to understanding how neural networks through their activity encode and decode information. In this research, we build on methods from theoretical domains such as control theory, dynamical systems analysis and reinforcement learning to investigate such questions. Our objective is two-fold: first, to use methods from engineering to identify specific objectives that neural circuits might be optimizing through their spatiotemporal activity patterns, and second, to draw motivation from neuroscience to formulate new engineering principles such as synthesis of dynamical networks for decentralized control applications. We specifically take a top-down, optimization driven approach in our …
Mechanisms Of Sensory Adaptation In The Primate Visual System, Boris Isaac Peñaloza Rojas
Mechanisms Of Sensory Adaptation In The Primate Visual System, Boris Isaac Peñaloza Rojas
Electronic Theses and Dissertations
Under ecological conditions, the luminance impinging on the retina varies within a dynamic range of 220 dB. Stimulus contrast can also vary drastically within a scene, and eye movements leave little time for sampling luminance. In addition, the amount of information reaching our visual system far exceeds the brain’s information processing capacity. Given the limited dynamic range of its neurons and its limited capacity in processing visual information in real-time, the brain deploys both structural and functional solutions that work in tandem to adapt to the surroundings. In this work, employing visual psychophysics and computational neuroscience, we study the mechanisms …
Characterization Of A Spiking Neuron Model Via A Linear Approach, Amirhossein Jabalameli
Characterization Of A Spiking Neuron Model Via A Linear Approach, Amirhossein Jabalameli
Electronic Theses and Dissertations
In the past decade, characterizing spiking neuron models has been extensively researched as an essential issue in computational neuroscience. In this thesis, we examine the estimation problem of two different neuron models. In Chapter 2, We propose a modified Izhikevich model with an adaptive threshold. In our two-stage estimation approach, a linear least squares method and a linear model of the threshold are derived to predict the location of neuronal spikes. However, desired results are not obtained and the predicted model is unsuccessful in duplicating the spike locations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale …
Particle Swarm Optimization Using Multiple Neighborhood Connectivity And Winner Take All Activation Applied To Biophysical Models Of Inferior Colliculus Neurons, Brandon S. Coventry
Particle Swarm Optimization Using Multiple Neighborhood Connectivity And Winner Take All Activation Applied To Biophysical Models Of Inferior Colliculus Neurons, Brandon S. Coventry
Open Access Theses
Age-related hearing loss is a prevalent neurological disorder, affecting as many as 63% of adults over the age of 70. The inability to hear and understand speech is a cause of much distress in aged individuals and is becoming a major public health concern as age-related hearing loss has also been correlated with other neurological disorders such as Alzheimer's dementia. The Inferior Colliculus (IC) is a major integrative auditory center, receiving excitatory and inhibitory inputs from several brainstem nuclei. This complex balance of excitation and inhibition gives rise to complex neural responses, which are measured in terms of firing rate …
Scale Invariant Object Recognition Using Cortical Computational Models And A Robotic Platform, Danny Voils
Scale Invariant Object Recognition Using Cortical Computational Models And A Robotic Platform, Danny Voils
Dissertations and Theses
This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms are based on what is known about the visual cortex, HTM and HMAX …