Neural variability

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding in

…I will elucidate the behavioral and neural mechanisms linking arousal and performance under the Yerkes-Dodson law in a mouse model. During decision-making tasks, mice express an array of discrete strategies, whereby the optimal strategy occurs at intermediate arousal, measured

The onset of a stimulus, whether sensory or non-sensory, quenches trial-to-trial neural variability. The universality of this phenomenon across brain areas and species implies that it is a fundamental strategy employed by the brain to boost the fidelity of stimulus representation. Multiple network models have attempted to explain this phenomenon via circuit interactions, but a recent alternative theory proposes a much simpler cell-intrinsic mechanism: stimulus onset increases membrane conductance which itself is enough to quench output variability. Although this model can explain all of the ph

…itself in both behavior and neural responses. While CSP in neural responses is typically modeled using divisive normalization or in purely descriptive terms, none of them can account for the bewildering range of effects documented for CSP of motion alone. Furthermore, a normative model that links these neural effects to function and behavior is still

…decline may be caused by increased neural variability, reducing the signal-to-noise ratio of information processing in the central nervous system. To explicitly test this hypothesis, we investigate behavior and brain activity in young and old mice performing a visual decision-making task. Old animals show relatively worse performance (a shallower psychometric curve and slower reaction times). We then used extracellular neural recordings to quantify single-neuron variabi

…It is difficult to record neural activity in freely moving animals, and complex statistics and lack of stimulus repeats preclude traditional analyses. While a popular approach is to first identify reduced, data-driven behavioral variables prior to seeking neural correlates, we propose a complementary approach: comparing a suite of mechanistic models, directly fit to behavior, that embody competing hypotheses about single-cell or population neura

…brain produces variable patterns of neural activity. This neural variability stems from many sources, one of which is fluctuations in internal states (e.g., arousal, attention, and motivation). Neural variability may reflect changes in these internal cognitive factors and can influence our perceptual and decision-making abilities. For instance, when tasked with shooting consecutive free throws on a basketball court, we may experience changes in our arousal, and our minds might wander. Such trial-to-trial variability can

Neural responses are variable: even under identical experimental conditions, single neuron and population responses typically differ from trial to trial and across time. Recent work demonstrated that this variability can be modulated by specific stimulus features, most prominently quenched by stimulus onset. However, it is unknown how ubiquitous this effect is across stimulus features, and indeed whether it exists in brain areas to which classical neural coding approaches cannot be applied due to the lack of trials with repeatable experimental stimuli. To address these questions, we develop a

Neural responses are variable: even under identical experimental conditions, single neuron and population responses typically differ from trial to trial and across time. Recent work has demonstrated that this variability has predictable structure, can be modulated by sensory input and behaviour, and bears critical signatures of the underlying network dynamics and computations. However, current methods for characterising neural variability are primarily geared towards sensory coding in the laboratory: they require trials with repeatable experimental stimuli and behavioural covariates. In additi

Large-scale neural recordings contain high-dimensional structure that cannot be easily captured by existing data visualization methods. We therefore developed an embedding algorithm called Rastermap, which captures highly nonlinear relationships between neurons, and provides useful visualizations by assigning each neuron to a location in the embedding space. Compared to standard algorithms such as t-SNE and UMAP, Rastermap finds finer and higher dimensional patterns of neural variability, as measured by quantitative benchmarks. We applied Rastermap to a variety of datasets, including spontaneo

…It is not known if the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher-order visual areas and measured stimulus discrimination thresholds of 0.35 degrees and 0.37 degrees respectively in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported