Anthony Neville Burkitt

This study uses TMS-EEG and a new toolbox to analyze cortical recovery dynamics after perturbation, finding that extended post-stimulation analysis reveals LICI-related differences in resilience and temporal memory.

The paper builds a learning model to show how diverse spatial properties in the postrhinal cortex emerge from visual input processing through the superior colliculus pathway.

This study shows that a biologically grounded spiking neural network model of V1 learning exhibits sparse coding, balance, and decorrelation as emergent properties.

This paper presents a spiking network model of Layer 2/3 in V1 that computes sign-specific prediction errors through compartmentalized neurons without synaptic weight updates.

This paper develops a spiking neural network model of V1 layer 4 to study predictive coding, showing it can reproduce in vivo features and maintain E-I balance under varying input conditions.

This paper connects sparse coding, predictive coding, and divisive normalization within a unified learning framework to explain neural response nonlinearities and brain computations.

This study uses a neural mass model to predict seizure duration from neural connectivity patterns in a rat epilepsy model, finding that preictal connectivity parameters can forecast seizure length with high accuracy.

This study identifies dynamic multiday seizure cycles in a Tetanus Toxin rat model, showing evolving rhythms that may inform seizure prediction and treatment.

This study identifies cyclical patterns in seizure duration in the Tetanus Toxin rat model, showing predictable rhythms linked to EEG power, which could improve epilepsy management.

This study uses a neural mass model to analyze iEEG data from a Tetanus Toxin rat epilepsy model, finding correlations between connectivity weights and seizure frequency, and showing longer recovery times before seizures.

This study uses a neural mass model to show reduced synaptic heterogeneity in a Tetanus Toxin rat epilepsy model, linked to increased network synchrony.

This paper introduces a predictive coding model to study motion detection, showing how prior knowledge and sensory data balance in schizophrenia, with longer convergence times in higher noise.

This study uses a sparse coding model to show that spatio-temporal properties of V1 cells emerge from learning diverse LGN inputs through synaptic plasticity.

The paper shows that hippocampal neural networks approach criticality during a cognitive task but move away from criticality with memory impairment induced by scopolamine.

The paper develops a neural mass model of a cortical column to study inhibitory stabilization across layers, finding that layers 4 and 5 exhibit ISN behavior with paradoxical responses, while layer 2/3 does not.

This paper shows that egocentric boundary cells can be generated with a simple synaptic learning rule that creates sparse representations of visual input, matching observed neural activity in retrosplenial cortex.

The paper shows that in vitro neuronal networks exhibit critical dynamics when receiving structured sensory input during a task, with better performance linked to proximity to critical states.

This paper demonstrates a computational model showing how orientation selectivity in V1 develops through retinal activity and Hebbian plasticity without external input.

This paper presents a learning model that explains how hippocampal place cells develop both spatial and temporal response properties from entorhinal cortex input.

This paper proposes a model using non-negative sparse coding to learn hippocampal place maps from entorhinal inputs, showing that grid cell diversity and sparse coding enable efficient spatial tiling.

This paper shows that a hierarchical neural network with spike-timing-dependent plasticity spontaneously develops motion extrapolation capabilities without supervision, compensating for neural transmission delays.

This paper proposes a biologically plausible model for learning complex cells in V1 using BCM rule-based plasticity, showing it can replicate experimental diversity in receptive field properties.

This study analyzes long-term intracranial EEG recordings to examine variability and circadian patterns of high-frequency activity and epileptiform spikes in patients with drug-resistant epilepsy.

The paper was withdrawn due to a duplicate posting and directs readers to the current version on bioRxiv.

This study identifies critical slowing as a biomarker for seizure susceptibility using long-term iEEG recordings from epilepsy patients.

The paper extends predictive coding models by incorporating neural transmission delays, showing how real-time temporal alignment can explain motion illusions and solve the temporal binding problem.