Fundamental Mechanisms of NeuroInformation Processing: Inverse Problems and Spike Processing
Technical Report,01 Mar 2012,30 Apr 2016
Columbia University New York United States
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During the research period we i devised path breaking algorithms for the functional identification and evaluation of non-linear dendritic processing, and ii released a groundbreaking open source platform for the isolated and integrated emulation of the fruit fly brain on multiple GPUs Neurokernel. We established that identifying a single dendritic stimulus processor DSP is mathematically dual to decoding of stimuli encoded by a population of neurons with a bank of DSPs. Building on this key duality property, we i demonstrated that the evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space, and ii characterized the effect of noise parameters on the precision of the functional identification of feed forward, feedback and cross-feedback neural circuits with DSPbiological spike generator neuron models. We demonstrated the power of Neurokernels model integration by combining independently developed models of the retina and lamina neuropils in the flys visual system and by demonstrating their neuroinformation processing capability.