James Gonzalo King

The paper creates a detailed computational model of rat somatosensory cortex anatomy, including 4.2 million neurons and 14.2 billion synapses, and identifies limitations in connectivity prediction from morphology.

The paper presents a large-scale biophysically detailed model of somatosensory cortex to simulate and validate cortical dynamics and experimental results.

The paper uses simulations to study how synaptic plasticity interacts with network structure and dendritic organization to shape cortical microcircuit function and learning.

This study develops a multiscale electro-metabolic model of a rat neocortex to show that middle cortical layers are more vulnerable to energy impairment with age.

The paper presents a full-scale in silico model of rat hippocampal CA1 integrating diverse experimental data and validates it through simulations of theta rhythm and other experiments.

The paper develops a computational model of mouse thalamoreticular microcircuitry to study sensory enhancement and sleep spindle properties, revealing mechanisms of network-level activity across brain states.

STEPS 4.0 improves scalability, performance, and memory efficiency for stochastic reaction-diffusion simulations of neurons.

The paper modernizes the NEURON simulator to improve sustainability, portability, and performance through new tools and optimizations.

The paper proposes a calcium-based plasticity model that predicts LTP and LTD dynamics across multiple pyramidal connections in the neocortex.

The paper uses a biologically-detailed in-silico model of a neocortical microcircuit to study stimulus-specific adaptation in the auditory cortex and identifies key mechanisms underlying the response to surprising sensory inputs.

The paper introduces the SONATA data format to efficiently describe large-scale network models in neuroscience, enabling model construction, simulation, and sharing.

The paper quantifies intrinsic variability in cortical circuits and shows that recurrent connectivity enables millisecond spike-time reliability despite noise and chaos.