Animals can switch rapidly between multiple well-learned tasks without being explicitly instructed on which task to perform, a cognitive function termed un-cued task switching. This function relies on

The view of the world through the lens of our senses is noisy and incomplete. Confronted with this uncertainty, the brain relies on knowledge about the natural world and the current task context to pe

Finding a low-dimensional manifold of neural signals is crucial for understanding neural computation and developing a robust brain-computer interface (BCI). Latent variable models have been proposed t

The entorhinal cortex (EC) is central to the brain’s navigation system. Its subregions are conventionally thought to compute dichotomous representations for spatial processing: medial entorhinal corte

Information from rich visual signals and ongoing behavioral variables simultaneously drive the same neurons in the cortex without corrupting each other (Niell et al. 2010, Stringer et al. 2019, Shimao

Animals move their head and eyes as they explore and sample the visual environment. Previous studies have demonstrated neural correlates of head and eye movements in rodent primary visual cortex (V1),

Biological neural networks use an abundance of “recurrent” connections, yet state-of-the-art deep neural network based computer vision models are predominantly feedforward. Why does biological vision

We show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals during the early stages of learning. Interf

A neuron’s input-output function is a central component of network dynamics and is commonly understood in terms of two fundamental operating regimes: 1) the mean-driven regime where the mean input dri

Cortical neurons store information across different timescales, from seconds to years. Although the stability of cortical representations is variable across regions, it can vary within a region as wel

Motor behaviors arise from dynamic interactions of interconnected neural populations across distributed brain areas. The underlying principles of information flow remain largely unknown. Here, we inve

Dorsolateral prefrontal cortex (DLPFC) and dorsal premotor cortex (PMd) are two association brain areas implicated in decision making. However, whether these brain areas have similar or distinct decis

Summary: Dopamine has long been known to critically contribute to learning. The canonical view is that midbrain dopamine neurons broadcast errors in reward prediction. These learning signals are thoug

Perceptual decision-making involves combining sensory evidence with available choices to arrive at an appropriate action to report the choice. When sensory evidence and potential actions to report cho

Interactions between frontal cortex and hippocampus (HPC) play a key role in decision-making behaviors. Here, we test how these brain areas coordinate their representations of behavioral and cognitive

Although distinct cortical regions specialize in different functions, they also benefit from receiving global reinforcement feedback to tune local processing. When and how those reinforcement signals

Across the primate neocortex, neurons dedicated to similar functions are likely to be found physically nearby. In the high-level visual cortex, this principle gives rise to cortical patches with disti

The classical sparse coding (SC) model represents visual stimuli as a convex combination of a handful of learned basis functions that are Gabor-like when trained on natural image data. However, the Ga

Characterizing the relationship between neural population activity and behavioral data is a central goal of neuroscience. While latent variable models (LVMs) are successful in describing high dimensio

Brain-computer interfaces (BCIs) offer a unique method to study learning dynamics by defining a causal mapping between neural activity and movement. Examples include studying the acquisition of an arb

Neural decoding is essential for understanding the association between neural activity and behavior. A prerequisite for most decoding methods is spike sorting, the assignment of action potentials (or

Oscillations in neural activity are often studied in signals that reflect electrical currents aggregated over neuronal populations (e.g., local field potentials). Ideally, we could straightforwardly a

We study randomly connected neural networks to understand the effects of network architectures on network dynamics. Specifically, we examine the impact of sparse connectivity combined with Dale’s law

Assemblies are subsets of neurons whose coordinated excitation could represent the subject's thinking of an object, idea, episode, or word, and so they provide a promising basis for a theory of how ne

In reversal learning tasks, large-scale circuits in multiple brain areas are involved in encoding multiple decision variables, such as trial outcomes, action values and action switching. It is unknown

Variations in brain states and behavioral profiles clearly influence neuronal activity in the visual cortex[1,2]. However, the mechanisms behind these influences and their consequent effect on sensory

There remains significant debate about the role of cortical inhibition for visual selectivity and perception [1,2]. A long-standing view states feedforward excitation dictates neural selectivity for v

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to

Neural circuits adapt their computations to perform cognitive functions within and across tasks. These adaptations are vital for decision making, which often requires the integration of past informati

Locomotion in complex environments depends on the precise timing and active control of single paw movements in order to adapt steps to surface structure and coordinate between paws. Control of motor t

Value-based decision-making in a context-changing environment is known to be guided by the two distinctive reinforcement learning (RL) strategies: goal-directed and habitual learning. Despite decades-

One of the most essential language skills that humans possess is the ability to correctly execute actions based on linguistic instructions. Here we use the latest advances in Natural Language Processi

Neurons with recurrent connectivity can store memory patterns as attractor states in their dynamics, forming a plausible basis for associative memory in the brain. Classical theoretical results concer

Sensory neuron responses vary across repeated presentations of the same stimuli, but whether this trial-to-trial variability represents noise versus unidentified signals remains unresolved (1–3). Some

Artificial neural networks (ANN) have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far e

Recent findings from awake, behaving animals lead us to hypothesize the existence of an underlying dynamical process whose manifestations are observed across diverse neural, physiological and behavior

Recent population imaging studies in mouse primary visual cortex (V1) have revisited and questioned the traditional view of 'salt-and-pepper' organization of orientation tuning preference. A controver

Categorical perception of sensory inputs, including human speech, enables adaptive behavior and is thought to emerge in the sensory cortex. There would be significant computational advantages, however

The eyes are a window into the mind. Eye-tracking studies in the psychology literature report large individual differences in how people deploy attention when scanning photographs of real-world enviro

Working memory (WM) enables retaining and manipulating information for brief periods. Attractor models have been developed for explaining diverse phenomena linked to WM, including error-correcting dyn

Variations in cellular and network parameters shape neural dynamics and computation. Mechanistic models, such as spiking neural networks (SNNs), are instrumental for linking recordings of neural popul

Sensory systems in the mammalian brain exhibit rich representations that ultimately support complex behaviours. Microcircuits across neocortical layers are believed to underlie the development of thes

Adaptive behavior is guided by dynamic evaluations of the reward environment, which can be influenced by factors like reward frequency and internal motivational state. We implemented a behavioral task

A common approach to analyzing spike train data in stimulus-response experiments is to estimate spike rates relative to the stimulus onset. These experiments typically involve collecting measurements

The sensory cortex is widely considered to be specialized for perception by interpreting complex sensory patterns while also exhibiting structured forms of representational plasticity of behaviorally-

In natural environments, animals must decide when to commit to one option, such as searching for food, or switching to another option, such as escaping a predator. How the nervous system mediates this

Rhythms stretching across multiple interacting frequencies and spatial scales are ubiquitous in brain activity during complex cognitive tasks. Yet their functional significance is hotly debated betwee

Due to the size and opacity of vertebrate brains, it has until now been impossible to simultaneously image neuronal circuits at cellular resolution across the entire adult brain. Thus, any recording i

An animal’s ability to evaluate environmental threats and mount an appropriate behavioral response is critical to survival. Based on previous literature in rodents and humans, we developed a novel rod

In networks of neurons, fine-scale interactions build upon one another to produce large-scale patterns of activity. But inferring these interactions from state-of-the-art experiments poses a fundament