Mark H. Histed

The paper shows recurrent cortical networks in mouse visual cortex encode natural sensory statistics by filtering input sequences rather than generating them.

The paper investigates how optogenetic stimulation reshuffles firing rates in mouse and monkey V1 by analyzing neuronal responses and developing theoretical models.

The paper shows optogenetic input to mouse V1 excitatory neurons creates salt-and-pepper patterns of excitation and suppression, with responses not strongly predicted by direct input.

The paper measures neural activation functions in awake mice using optogenetics, finding neurons exhibit a supralinear-to-linear response that allows filtering of inputs during suppression.

The paper shows that mice learning to use novel optogenetic activity patterns in V1 experience amplified neural responses, suggesting cortical plasticity improves behavioral performance.

The paper introduces a bicistronic AAV vector that co-expresses GCaMP8s and stChrimsonR in mice, enabling stable and robust two-photon holographic stimulation and imaging of cortical neurons.

The paper shows that networks of strongly coupled conductance-based neurons exhibit asynchronous irregular activity and broad firing rate distributions when synaptic efficacy is of order 1/log(K), maintained by a drift-diffusion balance rather than current fluctuations.

The paper shows that secondary visual areas in mice are necessary for a simple visual task, even when primary visual cortex is intact.

The paper investigates nonlinear responses in spiking neuron networks, showing nonlinearities exist at finite coupling and can be captured by rate models.

The paper tests inhibition-stabilized network (ISN) models in mouse cortex and finds evidence for ISN operation across multiple areas and states.

The paper shows that different inhibitory interneurons in the visual cortex of mice have distinct effects on contrast perception, with PV and SST impairing and VIP improving detection.

The paper shows feedforward inhibition allows cortical networks to adjust input summation from linear to sublinear through changes in recurrent connectivity.