Code - Data

On a wall surface, any continuous stretch of wall, using a hard pencil, place fifty points at random. The points should be evenly distributed over the area of the wall. All of the points should be connected by straight lines.

Sol LeWitt, Wall Drawing #118 (1971)

Human perceptual segmentation data and code. (PLoS Computational Biology 2023)

A proof of concept dataset collected using the method introduced in Vacher et al 2023 PLoS CB and a python package to reconstruct probabilistic and deterministic segmentation maps.

Data, code and associated paper.

SENS - Statistical tools for Estimation of divisive Normalization Signals

Pairwise Ratio of Gaussians (RoG, aka stochastic divisive normalization). (PLoS Computational Biology 2023)

Matlab functions for training and inference of the pairwise RoG model. Example files illustrate how to generate synthetic data, estimate model parameters, and perform inference of the latent normalization signals. Includes scripts to analyze Ca2+ imaging data from mouse V1 (Adesnik lab).

Code and associated paper and data

Ratio of Gaussians (RoG). (Journal of Neuroscience 2019)

Matlab functions for training and inference of the RoG model. An example file is provided to illustrate how to generate synthetic RoG data, estimate model parameters, and perform inference of the latent normalization signal.

Code and data, and associated paper.

Naturalistic texture synthesis and interpolation. (NeurIPS 2020)

Python tools to synthesize naturalistic textures based on the statistics of CNN activations, and to interpolate between textures with the Optimal Transport metric.

Code and associated paper.

Bias-corrected estimator of linear Fisher Information. (PLoS Computational Biology 2015)

MatLab tools for estimating linear Fisher information from population data. Includes a synthetic dataset and spike counts recorded in macaque primary visual cortex in response to grating images with different orientations and white noise.

Code and data, and associated paper.

Neural-mixtures: Modelling spike-count responses with conditional mixtures of Poisson distributions. (eLife 2021)

Neural-mixtures is a library for working with conditional mixture models and applying them to neural data. The code page includes two data files with spike counts recorded in macaque primary visual cortex in response to grating images with different orientations. A comprehensive tutorial on installing the neural-mixtures CLI and applying it to new datasets is available here.

Code and tutorial and associated paper.

Mixture of Gaussian Scale Mixtures (MGSM). (PLoS Computational Biology 2012)

This package contains MatLab tools for building MGSM models, and perform inference and learning. MGSM models, and their application to the statistics of natural images and contextual phenomena in the visual cortex, are described in the associated paper.

Code and data, and associated paper.

Multi-electrode recordings of anesthetized macaque V1 responses to static natural images and gratings. (Nature Neuroscience 2015)

This data consist of multi-electrode recordings from V1 in anesthetized macaque monkeys, while natural images and gratings were flashed on the screen. Recordings were performed using the “Utah” electrode array. Natural images were presented at two sizes, 3-6.7 degrees and windowed to 1 degree, to quantify surround modulation. Experimental procedures and stimuli are fully described in the associated paper.

Code and data, and associated paper.

Flexible contextual modulation of naturalistic texture perception in peripheral vision (Journal of Vision 2021)

The anonymized raw data of the experiments, together with the analysis code, and the code for running the experiments, are available in the Open Science Framework. All participants gave informed written consent for their anonymized data to be publicly shared.

Code and data and associated paper.

Investigating the representation of uncertainty in neuronal circuits. (PLoS Computational Biology 2021)

Matlab code to reproduce the figures of the paper, including sound-computable, ideal Observer models of Interaural Time Difference processing.

Code and associated paper.

Tutorial on Computational Neuroscience. (2017)

Here are the slides for a 1-hour tutorial on Computational Neuroscience presented at the 2017 Cognitive Computational Neuroscience conference.

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