Michael London

The paper demonstrates that detailed models of rat pyramidal neurons can perform complex computational tasks using a novel algorithm, highlighting dendritic nonlinearities as key to high-order feature binding.

The paper uses all-optical electrophysiology to show how behavior affects excitation and inhibition in hippocampal cells, revealing state-dependent dynamics and gain modulation.

This study shows that activating astrocytes in an Alzheimer's disease mouse model fully corrects memory deficits and even improves memory beyond wild-type levels.

The paper introduces the Functional Complexity Index (FCI) to quantify neuronal functional complexity and finds human cortical pyramidal neurons are more complex than rat neurons due to morpho-electrical differences.

This study shows that the anterior cingulate cortex modulates USV production in male mice during courtship through increased neural activity and Ca2+ dynamics.

The paper develops a 'Filter-and-Fire' neuron model that shows increased memory capacity and ability to recognize spatio-temporal patterns through multiple synaptic contacts on dendrites.

The paper identifies hub neurons in a rodent neocortical microcircuit and shows that their removal disrupts network topology and dynamics more than random neuron removal.

The paper trains deep neural networks to model the input-output behavior of cortical pyramidal neurons, revealing their functional complexity as multi-layered processing units.

The paper develops a morphometric reconstruction pipeline to analyze cortical VIP/ChAT interneurons and finds insult-driven plasticity in their morphology and gene expression.

This study shows that astrocytes modulate hippocampal-cortical communication during learning, specifically impairing remote memory recall when manipulated.

The paper shows that neuron geometry in cortical microcircuits explains universal network motifs and predicts spatial cell alignment, validated through in vitro recordings.

The paper investigates how multimodal sensory stimuli affect NE axonal activity and local dendritic responses in sensory cortices, finding nonlinear relationships and behaviorally meaningful variations.

The paper analyzes mouse courtship vocalizations to uncover temporal structure in their sequences and introduces a new algorithm to improve classification based on sequence statistics.