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Huntington disease is a genetic neurodegenerative disorder that produces motor, neuropsychiatric, and cognitive deficits and is caused by an abnormal expansion of the CAG tract in the huntingtin (htt) gene. In humans, mutated htt induces a preferential loss of medium spiny neurons in the striatum and, to a lesser extent, a loss of cortical neurons as the disease progresses. The mechanisms causing these degenerative changes remain unclear, but they may involve synaptic dysregulation. We examined the activity of the corticostriatal pathway using a combination of electrophysiological and optical imaging approaches in brain slices and acutely dissociated neurons from the YAC128 mouse model of Huntington disease. The results demonstrated biphasic age-dependent changes in corticostriatal function. At 1 month, before the behavioral phenotype develops, synaptic currents and glutamate release were increased. At 7 and 12 months, after the development of the behavioral phenotype, evoked synaptic currents were reduced. Glutamate release was decreased by 7 months and was markedly reduced by 12 months. These age-dependent alterations in corticostriatal activity were paralleled by a decrease in dopamine D 2 receptor modulation of the presynaptic terminal. Together, these findings point to dynamic alterations at the corticostriatal pathway and emphasize that therapies directed toward preventing or alleviating symptoms need to be specifically designed depending on the stage of disease progression.

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Deep artificial neural networks apply principles of the brain's information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by the form and function of biological neural circuits, so they can process new knowledge, adapt, behave, and learn in real time at low power levels. Despite several decades of research, until recently, very few published results have shown that today's neuromorphic chips can demonstrate quantitative computational value. This is now changing with the advent of Intel's Loihi, a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to deliver competitive results compared to state-of-the-art contemporary computing architectures. This survey reviews results that are obtained to date with Loihi across the major algorithmic domains under study, including deep learning approaches and novel approaches that aim to more directly harness the key features of spike-based neuromorphic hardware. While conventional feedforward deep neural networks show modest if any benefit on Loihi, more brain-inspired networks using recurrence, precise spike-timing relationships, synaptic plasticity, stochasticity, and sparsity perform certain computation with orders of magnitude lower latency and energy compared to state-of-the-art conventional approaches. These compelling neuromorphic networks solve a diverse range of problems representative of brain-like computation, such as event-based