Jason N. MacLean

The paper shows that SST neurons become selectively coordinated with behaviorally relevant excitatory populations during motor skill learning.

The paper infers motor cortex inputs during movement tasks and finds similar encoding for initial and corrective movements despite nonidentifiability challenges.

This study shows that sensorimotor cortex maintains canonical dynamics during natural prey capture but flexibly reconfigures them for online control.

The paper shows that learning refines mouse motor cortex activity into a distinct representation of active grasp maintenance during reach-grasp-carry tasks.

The paper investigates divisive normalization in mouse visual cortex by analyzing population calcium imaging data and computational models, revealing how subcortical inputs and heterogeneity in suppression strength influence neural responses.

The paper examines how movement variability is encoded in the motor cortex of mice over multiple days, finding that most neurons maintain stable encoding despite small declines in similarity over time.

The paper examines normalization in mouse visual cortex using calcium imaging and electrophysiology, finding similar normalization strength distributions to cats and macaques but slightly weaker on average.

This study trains spiking neural networks to perform a state change detection task and finds that cross-stimulus-modulated inhibition emerges as an optimal solution under Dales law constraints.

The paper shows that functional networks of motor cortical neurons change dynamically during movement tasks and carry behaviorally relevant information.

The paper identifies stiff and sloppy parameters in synaptic architectures of murine neocortex using simulations and experimental data, showing that specific excitatory-inhibitory combinations are critical while other parameters are flexible.

The paper identifies a feed-forward circuit in Drosophila larval nerve cord formed by sequential addition of neuronal stem cell temporal cohorts, challenging the early-to-early connectivity model.

The paper identifies a sparse set of spikes with reliable correlations that are more informative and accurately decoded than the full spike train for visual stimuli.

This paper develops a method for chronic wireless neural recordings in marmosets across multiple behaviors and brain states, validated through sensorimotor cortex activity tracking.

The paper constructs spiking neural network models to show that cyclic transitions between triangle motifs are necessary for sustained activity in sparse, asynchronous networks.

This paper introduces a semi-automated platform for training common marmosets in-home for behavioral neuroscience studies, enabling naturalistic behavior analysis with minimal experimenter intervention.

The paper models trial-to-trial variability in cortical neurons by incorporating neuronal correlations, finding that functional groups improve prediction and decoding of sensory information.

The paper shows that functional triplet motifs in V1 neuron populations predict single-trial responses and explain both trial-averaged tuning and variability.