A brain-wide map of neural activity during complex behaviour.

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with previous expectations to initiate movements and make decisions. It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here we report a comprehensive set of recordings from 621,733 neurons recorded with 699 Neuropixels probes across 139 mice in 12 laboratories.

Brain-wide representations of prior information in mouse decision-making.

The neural representations of prior information about the state of the world are poorly understood. Here, to investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with a prior probability alternating between 0.

Multi-timescale reinforcement learning in the brain.

To thrive in complex environments, animals and artificial agents must learn to act adaptively to maximize fitness and rewards. Such adaptive behaviour can be learned through reinforcement learning, a class of algorithms that has been successful at training artificial agents and at characterizing the firing of dopaminergic neurons in the midbrain. In classical reinforcement learning, agents discount future rewards exponentially according to a single timescale, known as the discount factor.

Reticulon-dependent ER-phagy mediates adaptation to heat stress in C. elegans.

The selective degradation of endoplasmic reticulum (ER) by autophagy, named ER-phagy, promotes the recovery of ER homeostasis after stress. Depending on the ER stress, different types of ER-phagy involve various selective autophagy receptors. In this study, we report a macroER-phagy induced by the fragmentation of tubular ER in response to acute heat stress.

Acquiring musculoskeletal skills with curriculum-based reinforcement learning.

Efficient musculoskeletal simulators and powerful learning algorithms provide computational tools to tackle the grand challenge of understanding biological motor control. Our winning solution for the inaugural NeurIPS MyoChallenge leverages an approach mirroring human skill learning. Using a novel curriculum learning approach, we trained a recurrent neural network to control a realistic model of the human hand with 39 muscles to rotate two Baoding balls in the palm of the hand.

Exploiting correlations across trials and behavioral sessions to improve neural decoding.

Traditional neural decoders model the relationship between neural activity and behavior within individual trials of a single experimental session, neglecting correlations across trials and sessions. However, animals exhibit similar neural activities when performing the same behavioral task, and their behaviors are influenced by past experiences from previous trials. To exploit these informative correlations in large datasets, we introduce two complementary models: a multi-session reduced-rank model that shares similar behaviorally-relevant statistical structure in neural activity across sessions to improve decoding, and a multi-session state-space model that shares similar behavioral statistical structure across trials and sessions.

Natural language instructions induce compositional generalization in networks of neurons.

A fundamental human cognitive feat is to interpret linguistic instructions in order to perform novel tasks without explicit task experience. Yet, the neural computations that might be used to accomplish this remain poorly understood. We use advances in natural language processing to create a neural model of generalization based on linguistic instructions.

Predicting Bordeaux red wine origins and vintages from raw gas chromatograms.

Connecting chemical properties to various wine characteristics is of great interest to the science of olfaction as well as the wine industry. We explored whether Bordeaux wine chemical identities and vintages (harvest year) can be inferred from a common and affordable chemical analysis, namely, a combination of gas chromatography (GC) and electron ionization mass spectrometry. Using 12 vintages (within the 1990-2007 range) from 7 estates of the Bordeaux region, we report that, remarkably, nonlinear dimensionality reduction techniques applied to raw gas chromatograms recover the geography of the Bordeaux region.

Multi-timescale reinforcement learning in the brain.

To thrive in complex environments, animals and artificial agents must learn to act adaptively to maximize fitness and rewards. Such adaptive behavior can be learned through reinforcement learning, a class of algorithms that has been successful at training artificial agents and at characterizing the firing of dopamine neurons in the midbrain. In classical reinforcement learning, agents discount future rewards exponentially according to a single time scale, controlled by the discount factor.

Catalyzing next-generation Artificial Intelligence through NeuroAI.

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts.

Next-generation brain observatories.

We propose centralized brain observatories for large-scale recordings of neural activity in mice and non-human primates coupled with cloud-based data analysis and sharing. Such observatories will advance reproducible systems neuroscience and democratize access to the most advanced tools and data.

Internally generated population activity in cortical networks hinders information transmission.

How neuronal variability affects sensory coding is a central question in systems neuroscience, often with complex and model-dependent answers. Many studies explore population models with a parametric structure for response tuning and variability, preventing an analysis of how synaptic circuitry establishes neural codes. We study stimulus coding in networks of spiking neuron models with spatially ordered excitatory and inhibitory connectivity.

Mice alternate between discrete strategies during perceptual decision-making.

Classical models of perceptual decision-making assume that subjects use a single, consistent strategy to form decisions, or that decision-making strategies evolve slowly over time. Here we present new analyses suggesting that this common view is incorrect. We analyzed data from mouse and human decision-making experiments and found that choice behavior relies on an interplay among multiple interleaved strategies.

Synaptic plasticity as Bayesian inference.

Learning, especially rapid learning, is critical for survival. However, learning is hard; a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability distributions over weights is the optimal strategy.

Investigating the representation of uncertainty in neuronal circuits.

Skilled behavior often displays signatures of Bayesian inference. In order for the brain to implement the required computations, neuronal activity must carry accurate information about the uncertainty of sensory inputs. Two major approaches have been proposed to study neuronal representations of uncertainty.

The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs.

In standard models of perceptual decision-making, noisy sensory evidence is considered to be the primary source of choice errors and the accumulation of evidence needed to overcome this noise gives rise to speed-accuracy tradeoffs. Here, we investigated how the history of recent choices and their outcomes interact with these processes using a combination of theory and experiment. We found that the speed and accuracy of performance of rats on olfactory decision tasks could be best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence.

Learning optimal decisions with confidence.

Diffusion decision models (DDMs) are immensely successful models for decision making under uncertainty and time pressure. In the context of perceptual decision making, these models typically start with two input units, organized in a neuron-antineuron pair. In contrast, in the brain, sensory inputs are encoded through the activity of large neuronal populations.

Neural Correlates of Optimal Multisensory Decision Making under Time-Varying Reliabilities with an Invariant Linear Probabilistic Population Code.

Perceptual decisions are often based on multiple sensory inputs whose reliabilities rapidly vary over time, yet little is known about how the brain integrates these inputs to optimize behavior. The optimal solution requires that neurons simply add their sensory inputs across time and modalities, as long as these inputs are encoded with an invariant linear probabilistic population code (ilPPC). While this theoretical possibility has been raised before, it has never been tested experimentally.

Optimal policy for multi-alternative decisions.

Everyday decisions frequently require choosing among multiple alternatives. Yet the optimal policy for such decisions is unknown. Here we derive the normative policy for general multi-alternative decisions.

Inferring decoding strategies for multiple correlated neural populations.

Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations.

A method to estimate the number of neurons supporting visual orientation discrimination in primates.

In this method article, we show how to estimate of the number of retinal ganglion cells (RGC), and the number of lateral genicular nucleus (LGN) and primary visual cortex (V1) neurons involved in visual orientation discrimination tasks. We reported the results of this calculation in Kanitscheider . (2015), where we were interested in comparing the number of neurons in the visual periphery versus visual cortex for a specific experiment.

Robust information propagation through noisy neural circuits.

Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures.

Context- and Output Layer-Dependent Long-Term Ensemble Plasticity in a Sensory Circuit.

Sensory information is translated into ensemble representations by various populations of projection neurons in brain circuits. The dynamics of ensemble representations formed by distinct channels of output neurons in diverse behavioral contexts remains largely unknown. We studied the two output neuron layers in the olfactory bulb (OB), mitral and tufted cells, using chronic two-photon calcium imaging in awake mice.

A probabilistic approach to demixing odors.

The olfactory system faces a hard problem: on the basis of noisy information from olfactory receptor neurons (the neurons that transduce chemicals to neural activity), it must figure out which odors are present in the world. Odors almost never occur in isolation, and different odors excite overlapping populations of olfactory receptor neurons, so the central challenge of the olfactory system is to demix its input. Because of noise and the large number of possible odors, demixing is fundamentally a probabilistic inference task.