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As a computational neuroscientist, Mihalas has worked on models of both molecular and systems neuroscience including nervous system development, synaptic plasticity, minimalistic spiking neuron models, self-organized criticality, visual attention and figure ground segregation. His current research interests are aimed at building models to elucidate how large networks of interacting neurons produce cognitive behaviors. At the Allen Institute, Mihalas integrates anatomical and physiological connectivity data to generate models of visual perception in the mouse. To this end, he works to build a series of models of increasing complexity for both individual components, i.e., neurons, synapses, and microcircuits, as well as for large portions of the entire system. This series of models will be compared to the simplified theoretical predictions from statistical physics, information theory and computer vision.
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Neural networks : the official journal of the International Neural Network Society (2023): 615-630
NETWORK NEUROSCIENCEno. 4 (2023): 1497-1512
bioRxiv : the preprint server for biology (2023)
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Nature Neuroscienceno. 1 (2023): 1-8
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