Samuel A. Neymotin

This paper introduces evidence-gated plasticity (EGP) for multi-goal learning in spiking neural networks, showing improved reward and target selectivity with target-context EGP.

The paper shows how recording geometry affects the representation of cortical activity through biophysical modeling and experimental recordings.

The paper identifies the electrophysiological mechanism of intrinsic oscillations in rat geniculate interneurons through pharmacological and computational analyses.

The paper introduces a spiking neuronal network model that uses spatial representations, value-guided plasticity, and contextual modulation to enable flexible navigation under sparse reward conditions.

The paper investigates how transient high-frequency neural events contribute to sensory representation by analyzing their temporal, spectral, and phase properties across trials.

This paper evaluates the universality and reliability of a spectrolaminar motif in primate neocortex, finding it not universal and methods unreliable.

This paper introduces a neuroscience-inspired spiking neural network with feedback inhibition that enhances pattern separation and demonstrates its application in cognitive robotics.

The paper develops a biophysically detailed model of macaque auditory circuits that reproduces in vivo dynamics and physiological oscillations.

This paper trains spiking neuronal network models to play a virtual racket-ball game using reinforcement learning and evaluates different circuit architectures and learning rules.

The paper develops a multiscale model of mouse primary motor cortex to predict cell type-specific, behavior-dependent neural dynamics.

This study compares spike-timing-dependent reinforcement learning and evolutionary strategy for training spiking neuronal networks to solve a reinforcement learning control task.

This study identifies laminar dynamics of beta bursts in human motor cortex using high precision MEG and a neocortical circuit model, showing distinct excitatory inputs to deep and superficial layers.

This study investigates how thalamocortical beta events influence tactile perception using MEG and biophysical modeling, finding both suppressive and facilitatory effects depending on timing.

The paper investigates how HCN and TASK-like channels affect dendritic impedance in layer 5 pyramidal neurons, finding that their combination best matches experimental impedance profiles.

This study uses a hippocampal CA3 computer model to investigate how altering NMDAR, HCN, and GABAAR affects information flow and gamma activity, suggesting multi-target pharmacotherapy for schizophrenia.

The paper quantifies and classifies multi-cycle oscillation events in primate auditory cortex, revealing non-constant frequency and amplitude characteristics across frequency bands.

The paper introduces HNN, a software tool that interprets the cellular and network origin of human MEG/EEG data using a neocortical circuit model.

The paper develops a multiscale biophysically detailed model of mouse primary motor cortex to study information flow and dynamics across cell types and frequencies.

The paper investigates how neocortical beta events influence tactile perception using MEG and computational modeling, finding that beta events suppress sensory processing through GABAB inhibition.

NetPyNE is a tool for creating multiscale neuronal network models in NEURON using declarative specifications, enabling efficient simulation and analysis of brain circuits.