Martin A. Vinck

This paper introduces TWINDEX, a method for detecting and analyzing neural traveling waves in 3D spatiotemporal data, and applies it to ECoG and EEG data to identify alpha/low-beta waves with directional propagation.

The paper introduces DIFF, a method to map directed information flow in cortical networks, and shows it reveals distinct trajectories from traveling waves in simulations and electrophysiological recordings.

This study investigates how attention modulates prediction-error (PE) encoding in thalamic and cortical circuits during learning, revealing distinct informational dynamics and reorganization of PE representations.

This paper develops a mean-field model of E-PV-SOM circuits to show that gamma oscillations arise from specific connectivity patterns between cell types.

The paper investigates the roles of broadband and narrowband gamma oscillations in visual information encoding using ECoG and LFP data from humans and macaques, finding broadband signals to be synergistic and narrowband gamma to be redundant.

This study shows that inhibitory feedback combined with predictive learning allows neural networks to efficiently anticipate and classify multiple spike sequences.

The paper shows that representational drift in visual cortex and neural networks is driven by sensory input rather than elapsed time, with same-class acceleration observed during stimulus presentation.

This paper introduces a biomimetic agent that autonomously regulates internal needs through a neural mass model, outperforming control agents in efficiency and stability.

This paper introduces MoHA, a model that integrates motivational and visual inputs to generate cognitive maps, enabling adaptive navigation in reinforcement learning agents.

The paper identifies distinct temporal roles of PV and Sst interneurons in visually-induced gamma oscillations in awake mice.

The paper analyzes neural traveling waves and their relationship to causal information flow, showing that phase ordering does not always reflect directionality and introducing a new measure called DIFF.

The paper introduces a computational model of cortical microcircuits, CAMINOS, showing how different inhibitory neuron types contribute to network oscillations and stability.

The paper shows random-weight CNNs with sufficient complexity can predict early visual cortex activity as well as trained models, but not higher visual areas.

The paper investigates surround-induced responses in mouse V1 neurons and finds they occur for large gray patches regardless of RF stimulus mismatch, suggesting they represent surface representation rather than prediction.

The paper investigates how attention modulates neural responses in V1 and V4 by analyzing distinct feedforward and feedback pathways in macaque and mouse visual cortex.

The paper investigates whether cortical interactions during prediction error processing are synergistic or redundant, finding that both ERPs and broadband signals show high synergy across the cortical hierarchy.

The paper investigates hippocampal place cell sequences during a visual discrimination task and finds they recapitulate paths near the chosen reward site, independent of perirhinal cortex activity.

SpikeShip identifies high-dimensional neural spiking patterns using optimal transport theory, distinguishing natural images and behavioral states in Neuropixel recordings.

The paper compares spike-rate and spike-timing codes in visual cortex during natural movie viewing, finding that temporal spiking sequences carry more information and are more stable than spike-rate vectors.

This study investigates how visual flicker-induced synchronization propagates across cortical regions and affects different cell types, finding frequency-dependent differences in phase locking.

This study shows that loss of ErbB4 in PV interneurons during embryonic development disrupts cortical circuit dynamics and neuronal connectivity.

This study uses optogenetics to show that mouse V1 neurons encode high-frequency inputs with phase-locking and low-frequency inputs with more globally reliable responses.

The paper shows that in macaque V1, repeated visual stimuli lead to reduced firing rates and increased gamma-band synchronization, specific to the stimulus and its location, and generalize to natural images.

This paper introduces a predictive learning rule that explains sequence anticipation and STDP mechanisms in neurons.

The paper investigates how anatomical connection strength relates to rhythmic synchronization in macaque cortex, finding that strength predicts synchronization and vice versa, with frequency and direction specificity.

The paper uses statistical modeling and anatomical analysis to establish a cortical hierarchy based on laminar profiles and functional connectivity, highlighting the role of top-down generative networks.

The paper uses self-supervised deep neural networks to study how V1 activity predicts natural images, finding distinct roles for synchronization and firing rates in predictive coding.

The paper develops a mathematical model explaining interareal coherence in brain signals as a result of connectivity and oscillatory synchronization, rather than spiking entrainment.

The paper analyzes gamma oscillations in macaque V1, showing they can be modeled by a linear harmonic oscillator driven by noise, capturing amplitude-duration correlations and other features.