M.S. / Computer Science & Engineering / The Oregon Graduate Center's Cognitive Architecture Project (CAP) is developing a flexible architecture to evaluate and implement several types of neural networks. Wafer-scale integrated silicon is the targeted technology, allowing higher density and larger networks to be implemented more cheaply than with discrete components. The large size of networks implemented in wafer-scale technology makes it difficult to assess the effects of manufacturing faults on network behavior. Since neural networks degrade gracefully in the presence of faults, and since in larger networks faults tend to interact with each other, it is difficult to determine these effects analytically. This paper discusses a program, FltSim, that simulates wafer manufacturing faults. By building an abstract model of the CAP architecture, the effects of these manufacturing faults can be determined long before proceeding to implementation. In addition, the effects of architectural design trade-offs can be studied during the design process.

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Thesis (Ph. D.)--University of Rhode Island, 2003. / Typescript. Includes bibliographical references (leaves 191-207).

Thesis (Ph.D.)--Ohio University, June, 2005. / Title from PDF t.p. Includes bibliographical references (p. 130-137)

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006. / Michaels, Thomas E., Committee Member ; LaPlaca, Michelle, Committee Member ; Frazier, A. Bruno, Committee Member ; DeWeerth, Stephen P., Committee Member ; Allen, Mark G., Committee Chair.

This dissertation presents two lines of research that are superficially at opposite ends of the computational neuroscience spectrum. While models of adaptive motion detection in fruit flies and simulations inspired by monkeys that learn to control brain machine interfaces might seem like they have little in common, these projects both attempt to address the broad question of how real neural circuits flexibly compute. Sensory systems flexibly adapt their processing properties across a wide range of environmental and behavioral conditions. Such variable processing complicates attempts to extract mechanistic understanding of sensory computations. This is evident in the highly constrained, canonical Drosophila motion detection circuit, where the core computation underlying direction selectivity is still debated despite extensive studies. The first part of this dissertation analyzes the filtering properties of four neural inputs to the OFF motion-detecting T5 cell in Drosophila. These four neurons, Tm1, Tm2, Tm4 and Tm9, exhibit state- and stimulus-dependent changes in the shape of their temporal responses, which become more biphasic under specific conditions. Summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that these shapes are sufficient to explain T5 responses to various motion stimuli. Thus, the stimulus- and state-dependent measurements reconcile motion computation with the anatomy of the circuit. These findings provide a clear example of how a basic circuit supports flexible sensory computation. The most flexible neural circuits are circuits that can learn. Despite extensive theoretical work on biologically plausible learning rules, however, it has been difficult to obtain clear evidence about whether and how such rules are implemented in the brain. In the second part of this dissertation, I consider biologically plausible supervised- and reinforcement-learning rules and ask whether biased changes in network activity during learning can be used to determine which learning rule is being used. Supervised learning requires a credit-assignment model estimating the mapping from neural activity to behavior, and, in a biological organism, this model will inevitably be an imperfect approximation of the ideal mapping, leading to a bias in the direction of the weight updates relative to the true gradient. Reinforcement learning, on the other hand, requires no credit-assignment model and tends to make weight updates following the true gradient direction. I derive a metric to distinguish between learning rules by observing biased changes in the network activity during learning, given that the mapping from brain to behavior is known by the experimenter. Because brain-machine interface (BMI) experiments allow for perfect knowledge of this mapping, I focus on modeling a cursor-control BMI task using recurrent neural networks, and show that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to.

The clustered protocadherins (Pcdh α, β & γ) provide individual neurons with cell surface diversity. However, the importance of Pcdh mediated diversity in neural circuit assembly and how it may promote neuronal connectivity remains largely unknown. Moreover, to date, Pcdh in vivo function has been studied at the level of individual gene clusters; whole cluster-wide function has not been addressed. Here I examine the role of all three Pcdh gene clusters in olfactory sensory neurons (OSNs); a neuronal type that expressed all three types of Pcdhs and in addition I address the role of Pcdh mediate diversity in their wiring. When OSNs share a dominant single Pcdh identity (α, β & γ) their axons fail to form distinct glomeruli, suggestive of inappropriate self-recognition of neighboring axons (loss of non-self-discrimination). By contrast, deletion of the entire α, β,γ Pcdh gene cluster, but not of each individual cluster alone, leads to loss of self-recognition and self-avoidance thus, OSN axons fail to properly arborize. I conclude that Pcdh-expression is necessary for self-recognition in OSNs, whereas its diversity allows distinction between self and non-self. Both of these functions are required for OSNs to connect and assembly into functional circuits in the olfactory bulb. My results, also reveal neuron-type specific differences in the requirement of specific Pcdh gene clusters and demonstrate significant redundancy between Pcdh isoforms in the olfactory system.

The response of a neuron encoding information about a sensory stimulus is influenced by the context in which that information is presented. In the primary visual cortex (area V1), neurons respond selectively to stimuli presented to a relatively constrained region of visual space known as the classical receptive field (CRF). These responses are influenced by stimuli in a much larger region of visual space known as the extra-classical receptive field (eCRF). In that they cannot directly evoke a response from the neuron, surround stimuli in the eCRF provide the context for the input to the CRF. Though the past few decades of research have revealed many details of the complex and nuanced interactions between the CRF and eCRF, the circuit mechanisms underlying these interactions are still unknown. In this thesis, we present a simple, novel cortical circuit model that can account for a surprisingly diverse array of eCRF properties. This model relies on extensive recurrent interactions between excitatory and inhibitory neurons, connectivity that is strongest between neurons with similar stimu- lus preferences, and an expansive input-output neuronal nonlinearity. There is substantial evidence for all of these features in V1. Through analytical and computational modeling techniques, we demonstrate how and why this circuit is able to account for such a comprehensive array of contextual modulations. In a linear network model, we demonstrate how surround suppression of both excitatory and inhibitory neurons is achieved through the selective amplification of spatially-periodic pat- terns of activity. This amplification relies on the network operating as an inhibition-stabilized network, a dynamic regime previously shown to account for the paradoxical decrease in in- hibition during surround suppression (Ozeki et al., 2009). With the addition of nonlinearity, effective connectivity strength scales with firing rate, and the network can transition be- tween different dynamic regimes as a function of input strength. By moving into and out of the inhibition-stabilized state, the model can reproduce a number of contrast-dependent changes in the eCRF without requiring any asymmetry in the intrinsic contrast-response properties of the cells. This same model also provides a biologically plausible mechanism for cortical normalization, an operation that has been shown to be ubiquitous in V1. Through a winner-take-all population response, we demonstrate how this network undergoes a strong reduction in trial-to-trial variability at stimulus onset. We also propose a novel mechanism for attentional modulation in visual cortex. We then go on to test several of the critical pre- dictions of the model using single unit electrophysiology. From these experiments, we find ample evidence for the spatially-periodic patterns of activity predicted by the model. Lastly, we show how this same circuit motif may underlie behavior in a higher cortical region, the lateral intraparietal area.

Perception is the result of more than just the unbiased processing of sensory stimuli. At each moment in time, sensory inputs enter a circuit already impacted by signals of arousal, attention, and memory. This thesis aims to understand the impact of such internal states on the processing of sensory stimuli. To do so, computational models meant to replicate known biological circuitry and activity were built and analyzed. Part one aims to replicate the neural activity changes observed in auditory cortex when an animal is passively versus actively listening. In part two, the impact of selective visual attention on performance is probed in two models: a large-scale abstract model of the visual system and a smaller, more biologically-realistic one. Finally in part three, a simplified model of Hebbian learning is used to explore how task context comes to impact prefrontal cortical activity. While the models used in this thesis range in scale and represent diverse brain areas, they are all designed to capture the physical processes by which internal brain states come to impact sensory processing.