Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks

Recurrently connected neural networks, in which synaptic connections between neurons can form directed cycles, have been used extensively in the literature to describe various neurophysiological phenomena, such as coordinate transformations during sensorimotor integration. Due to the directed cycles that can exist in recurrent networks, there is no well-known way to a priori specify synaptic weights to elicit neuron spiking responses to stimuli based on available neurophysiology. Using a common mean field assumption, that synaptic inputs are uncorrelated for sufficiently large populations of neurons, we show that the connection topology and a neuron\u27s response characteristics can be decoupled. This assumption allows specification of neuron steady-state responses independent of the connection topology. Specification of neuron responses necessitates the creation of a novel simulator (computational framework) which allows modeling of large populations of connected spiking neurons. We describe the implementation of a spike-based computational framework, designed to take advantage of high performance computing architectures when available. We show that performance of the computational framework is improved using multiple message passing processes for large populations of neurons, resulting in a worst-case linear relationship between the number of neurons and the time required to complete a simulation. Using the computational framework and the ability to specify neuron response characteristics independent of synaptic weights, we systematically investigate the effects of Hebbian learning on the hemodynamic response. Changes in the magnitude of the hemodynamic responses of neural populations are assessed using a forward model that relates population synaptic currents to the blood oxygen dependant (BOLD) response via local field potentials. We show that the magnitude of the hemodynamic response is not a accurate indicator of underlying spiking activity for all network topologies. Instead, we note that large changes in the aggregate response of the population (\u3e50%) can results in a decrease in the overall magnitude of the BOLD signal. We hypothesize that the hemodynamic response magnitude changed due to fluctuations in the balance of excitatory and inhibitory inputs in neural subpopulations. These results have important implications for mean-field models, suggesting that the underlying excitatory/inhibitory neural dynamics within a population may need to be taken into account to accurately predict hemodynamic responses

The Role of Error-sensitivity in Motor Adaptation

When we experience an error in a motor task, we adapt our next movement to partially compensate. The process of adaptation can be modeled as u(n+1)=αu(n)+η(n)e(n) where u(n) is the motor command on trial n, α is a decay factor, e(n) is error, and η(n) represents the subjects’ sensitivity to the experienced error. Here, we explore the rules that govern the value of η(n) as well as the brain-regions that are responsible for its evaluation. In Chapter 2, we begin with a puzzle: in motor learning tasks, humans are able to modulate how much they learn from a given error. In some conditions, they learn a large amount, but in other conditions they learn only a small amount. That is, the brain selects how much it is willing to learn from error. We suggest that ‘error-sensitivity’ is modulated by the history of previous errors. What brain region is responsible for determining the amount subjects are willing to learn from an error? Adaptation is critically dependent on the cerebellum, as demonstrated by patient and lesion studies. In Chapter 3 we use transcranial direct current stimulation (tDCS) to alter the function of the cerebellum, and observe its effects on error-sensitivity. We find that increasing the excitability of the cerebellum via anodal tDCS increases the rate of learning, while decreasing iicerebellum excitability via cathodal tDCS decreases error-sensitivity. That is, we suggest the cerebellum is responsible for determining how much subjects are willing to learn from a motor error. How does the cerebellum accomplish the task of adaptation? It is has been proposed that the firing rates of the principal cells of the cerebellum, Purkinje (P-)cells, should encode movement kinematics. Yet, this has remained a long standing puzzle, as no clear encoding of movement kinematics has been found. How the cerebellum learns has been difficult to approach because the problem of encoding remains unresolved. In Chapter 4 we approach this problem from a new direction: we propose that the cerebellum is composed of micro-clusters of P-cells, organized based on their preference for error. When the cells are organized in this manner, a clear encoding of kinematics emerges

Ethnography Without Tears

Anthropolog

Using the MicroASAR on the NASA SIERRA UAS in the Characterization of Arctic Sea Ice Experiment

The MicroASAR is a flexible, robust SAR system built on the successful legacy of the BYU microSAR. It is a compact LFM-CW SAR system designed for low-power operation on small, manned aircraft or UAS. The NASA SIERRA UAS was designed to test new instruments and support flight experiments. NASA used the MicroASAR on the SIERRA during a science field campaign in 2009 to study sea ice roughness and break-up in the Arctic and high northern latitudes. This mission is known as CASIE-09 (Characterization of Arctic Sea Ice Experiment 2009). This paper describes the MicroASAR and its role flying on the SIERRA UAS platform as part of CASIE-09

On De-Pathologizing Resistance

This introductory essay draws attention to two processes, the pathologization and exoticization of resistance. Working independently or in parallel, these two processes silence resistance by depoliticizing it as illogical or idealizing it in out-worldly terms. In both cases, resistance is caricatured as abnormal or exotic and distanced from current political priorities. I argue that analytical de-pathologization and de-exoticization of resistance can (a) provide valuable insights on the silencing of resistance and (b) help us understand the relationship between hegemony and resistance in terms that stretch beyond the moderately pathologizing view of political inaction as apathy or “false consciousness”. In my analysis, I also engage with James Scott's seminal view of resistance, which, despite its de-pathologizing orientation, fails to capture the dialectical relationship of resistance and hegemony. I suggest that attention to the pathologizing and exoticizing workings of power may reveal the complexity and compromising ambivalence of resistance and contribute to the broader field of resistance studies, conceived as renewed interest in insurrectionary movements, rebellion, and protest