Adrien Wohrer

Virtual Retina: A biological retina model and simulator, with contrast gain control

We propose a new retina simulation software, called Virtual Retina, which transforms a video into spike trains. Our goal is twofold: Allow large scale simulations (up to 100,000 neurons) in reasonable processing times and keep a strong biological plausibility, taking into account implementation constraints. The underlying model includes a linear model of filtering in the Outer Plexiform Layer, a shunting feedback at the level of bipolar cells accounting for rapid contrast gain control, and a spike generation process modeling ganglion cells. We prove the pertinence of our software by reproducing several experimental measurements from single ganglion cells such as cat X and Y cells. This software will be an evolutionary tool for neuroscientists that need realistic large-scale input spike trains in subsequent treatments, and for educational purposes.

Population-wide distributions of neural activity during perceptual decision-making.

Cortical activity involves large populations of neurons, even when it is limited to functionally coherent areas. Electrophysiological recordings, on the other hand, involve comparatively small neural ensembles, even when modern-day techniques are used. Here we review results which have started to fill the gap between these two scales of inquiry, by shedding light on the statistical distributions of activity in large populations of cells. We put our main focus on data recorded in awake animals that perform simple decision-making tasks and consider statistical distributions of activity throughout cortex, across sensory, associative, and motor areas. We transversally review the complexity of these distributions, from distributions of firing rates and metrics of spike-train structure, through distributions of tuning to stimuli or actions and of choice signals, and finally the dynamical evolution of neural population activity and the distributions of (pairwise) neural interactions. This approach reveals shared patterns of statistical organization across cortex, including: (i) long-tailed distributions of activity, where quasi-silence seems to be the rule for a majority of neurons; that are barely distinguishable between spontaneous and active states; (ii) distributions of tuning parameters for sensory (and motor) variables, which show an extensive extrapolation and fragmentation of their representations in the periphery; and (iii) population-wide dynamics that reveal rotations of internal representations over time, whose traces can be found both in stimulus-driven and internally generated activity. We discuss how these insights are leading us away from the notion of discrete classes of cells, and are acting as powerful constraints on theories and models of cortical organization and population coding.

From Light to Spikes: a Large-Scale Retina Simulator

The purpose of this article is to provide potential neuroscientists and computer scientists with an artificial retina model, delivering spikes to higher-level visual tasks simulations. The architecture of our retina model is based on recent physiological studies, so that each feature is related to real retina characteristics. The model includes a linear filtering process, followed by a static non-linearity, and then a spike generation process. Two series of tests were performed: firstly on single cells for which ground truth is available, secondly on realistic visual scenes. This retina model can be further extended and we describe some possible additional features. It is our conviction that building such a bio-inspired model will help in better understanding how the retina performs and also in relating observations to biological mechanisms.

Biological Motion Recognition Using a MT-like Model

We propose a bio-inspired system for biological motion recognition in image sequences. Our system has two main contributions. We propose a bio-inspired spiking VI model that transforms a video sequence into spikes train according to local motion detectors. The motion detectors are directionally spatial-temporal filters properly tuned for a certain range of velocity. At the same time we propose a method to obtain a histogram map representation for the velocity distribution of VI output. This histogram map acts as a MT-like model containing the spatial-temporal information of an event. We also propose a distance between histogram maps to realize motion categorization. In order to evaluate the performance of our approach, we ran our system in Giese database which contains 40 sequences and two actions, walk and march. The results reveal that motion categorization can be reliably estimated from the analysis of spike trains together with a coarse estimation of their spatial position

On the Number of Neurons and Time Scale of Integration Underlying the Formation of Percepts in the Brain

All of our perceptual experiences arise from the activity of neural populations. Here we study the formation of such percepts under the assumption that they emerge from a linear readout, i.e., a weighted sum of the neurons’ firing rates. We show that this assumption constrains the trial-to-trial covariance structure of neural activities and animal behavior. The predicted covariance structure depends on the readout parameters, and in particular on the temporal integration window w and typical number of neurons K used in the formation of the percept. Using these predictions, we show how to infer the readout parameters from joint measurements of a subject’s behavior and neural activities. We consider three such scenarios: (1) recordings from the complete neural population, (2) recordings of neuronal sub-ensembles whose size exceeds K, and (3) recordings of neuronal sub-ensembles that are smaller than K. Using theoretical arguments and artificially generated data, we show that the first two scenarios allow us to recover the typical spatial and temporal scales of the readout. In the third scenario, we show that the readout parameters can only be recovered by making additional assumptions about the structure of the full population activity. Our work provides the first thorough interpretation of (feed-forward) percept formation from a population of sensory neurons. We discuss applications to experimental recordings in classic sensory decision-making tasks, which will hopefully provide new insights into the nature of perceptual integration.

Percept and the single neuron

Work reported in this issue has derived the long-sought analytical link between neural readout weights and choice signals in the standard model of perceptual decision making. This fresh perspective opens the door to experimental assessments of percept formation from the activity of sensory neurons.