Terrence J. Sejnowski A B

The paper investigates how presynaptic vesicle density changes during LTP and links it to axonal bouton enlargement.

The paper presents two EEG datasets for speech decoding, focusing on phoneme and word classification and articulatory properties, with conditions involving transcranial magnetic stimulation.

The paper models synaptic spine head morphology using graph grammars to simulate actin dynamics and their effects on membrane shape.

This study shows that adding random noise to RNNs improves training and enables stable working memory by increasing synaptic decay time constants in inhibitory units.

The paper shows that synaptic vesicle clusters become less dense and more dispersed during long-term potentiation, with increased mobility and a transition from compact to loose organization.

The paper develops a predictive autoencoder model of the hippocampus to simulate sequence learning, showing CA3's predictive role and CA1's error signaling in mice.

The paper develops a spatial model of CaMKII autophosphorylation in synaptic spines to show that CaM-trapping prolongs phospho-CaMKII lifetime by hindering dephosphorylation.

This study shows that long-term potentiation (LTP) increases synaptic information storage capacity in adult rat hippocampus by expanding the range of synapse sizes and improving precision.

The paper examines glia organization across mammalian species and finds that glia volume density is conserved within brain regions but varies between regions and scales with brain size.

The paper introduces a method to measure synaptic information storage capacity (SISC) using 3DEM data from rat hippocampus and finds regional and LTP-dependent variations in SISC.

This paper introduces DDC, a method for detecting directional network interactions in neural systems, validated on simulations and applied to fMRI data.

This study shows that Dnmt3a knockout in mouse excitatory neurons disrupts synapse maturation and working memory, with partial compensation by H3K27me3.

The paper develops a mesoscale model of presynaptic release dynamics by characterizing molecular mechanisms and applying model reduction to create an efficient phenomenological framework.

This paper introduces differential covariance for functional connectivity analysis in fMRI, showing it better captures structural connectivity and correlates with behavioral reaction times.

The paper abstracts a detailed 3D reaction-diffusion model of a chemical synapse into a compact state space for neuromorphic hardware emulation with preserved input-output dynamics.

This paper explores methods to improve EEG speech decoding by combining transcranial magnetic stimulation with delay differential analysis.

This paper trains a spiking RNN model on cognitive tasks to uncover task-specific synaptic and membrane parameters, revealing fast membrane properties and slow synaptic dynamics are critical for stimulus encoding and working memory maintenance.

The paper analyzes mitochondrial morphology in mouse cerebellum neurons using 3D TEM data to quantify structural features that influence metabolic output.

This study uses graph-theoretical analysis to identify network dynamics predicting seizure propagation patterns and post-onset dynamics in epilepsy.

This paper shows how neuromodulators alter neural network behavior by shifting activity flows through hyperchannels, enhancing computational flexibility in a recurrent neural network model.

The paper investigates the role of presynaptic endoplasmic reticulum in modulating short-term plasticity in hippocampal synapses using computational modeling.

The paper shows that strong inhibitory signaling in spiking neural networks supports stable temporal dynamics and working memory by analyzing a model and primate prefrontal cortex data.

This paper introduces a neural network framework called DynaMoE to model how the prefrontal cortex encodes, stores, and switches between schemas, demonstrating transfer learning and memory savings.

The paper identifies spontaneous traveling cortical waves in awake primates and shows they predict perceptual sensitivity and sensory processing.

This paper introduces differential covariance analysis to estimate functional connectivity in fMRI, showing improved performance in complex network simulations.

This paper uses echo-state networks to predict rs-fMRI signal fluctuations from vascular dynamics in rodent and human brains, revealing brain states with global synchrony and decoupled internal correlations.

This paper uses rule-based modeling to show that neurogranin modulates CaMKII phosphorylation dynamics, acting as a leaky integrator of calcium signals through interactions with calmodulin and phosphatase.

The paper develops a rule-based computational model of CaMKII holoenzyme to explore how Ca2+/CaM binding stabilizes autophosphorylation and regulates synaptic plasticity.

This study uses spiking recurrent neural networks to model neuronal timescales in working memory and shows they replicate experimental findings.

The paper presents a framework to construct biologically realistic spiking RNNs by transferring dynamics from continuous rate models, achieving similar performance with minimal additional parameters.