Code base for the SENSORIUM competition.

NeurIPS | 1st place solution for Sensorium 2023 Competition

Spiking Neural Network modelling the Visual Cortex (V1) Layer 5

Simulation of cat V1 simple cell and receptive field.

Edge co-occurrences can account for rapid categorization of natural versus animal images

Analysis and visualization of data recorded from V4 of macaque monkeys trained in a shape detection task

[fMRI] Multisensory integration of metaphorically related audiovisual inputs in visual cortex

A lightweight, ready-to-run demo environment for GEM-pRF, featuring automatic GPU setup, path configuration, and multiple example pipelines.

Foundations of Neuroscience Course Project

Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates

Two-photon calcium imaging of cross-orientation suppression across cortical depth in macaque V1. 4,785 ROIs across 28 depths (140-518um) reveal laminar shift from facilitation to suppression, paralleled by gradients in spatial frequency preference, tuning width, and orientation selectivity. Mixed-effects models and bootstrap CIs included.

Probabilistic segmentation uncertainty for V1 neural dynamics

The Role of Chromatic Stimuli in Modulating Perceptual Inpainting within the Visual Cortex

This project was developed as part of the Biologically Inspired Artificial Intelligence course at the Silesian University of Technology. The goal is to automatically classify eye diseases (diabetic retinopathy, cataract, glaucoma, normal) from retinal images using a convolutional neural network (CNN) inspired by the human visual cortex.

Congenitally blind AV scenes fMRI analysis (visual cortex / live-wire). Source: OpenNeuro ds002715 AVScenes_Blind.

Does recurrent processing in V1 correct an early cardinal bias in orientation coding over time, specifically in resilient neurons? Investigating this question on Ladret et al. (2023) in vivo recordings in cats and Motion Clouds, under the supervision of Laurent Perrinet, Research Director, and Alexandre Lainé, Doctoral Student.

Interactive Bressloff V1 form constants lab

Bio-inspired Convolutional Neural Network architectures modeled on biological visual neural network structures (retina → LGN → V1/V2 hierarchy, orientation selectivity, hierarchical processing). Research implementation and analysis.

Code for my BSc thesis: decoding orientation preference maps from spontaneous activity in human V1. Compares sleep against wakefulness and finds sleep-derived maps markedly more stable — a stimulus-free route to functional cortical mapping for visual prostheses.