Human vision uses two neural pathways, namely the ventral and dorsal streams. The two streams are structurally segregated from the eyes to the primary visual cortex and continue onto the ventral tempo

Cortical networks have the remarkable ability to self-assemble into dynamic regimes in which excitatory positive feedback is balanced by recurrent inhibition. This inhibition-stabilized regime is incr

Neuronal assemblies — groups of neurons with strong recurrent connectivity — are thought to be the basic building blocks of perception and memory in the brain: representations of specific concepts. Ac

Planar shape, i.e., the silhouette contour of a solid body, carries rich information important for object recognition, including both local (curvature) and global shape cues. While curvature-selective

How do animals adapt in a dynamic world? Sometimes, our expectations about the world are drastically violated, such as when we accidentally bite into spoiled food. In these circumstances, we must adju

The vertebrate telencephalon is the site of complex cognitive processes such as spatial cognition. The larval zebrafish telencephalon is a compact circuit of ~10,000 neurons that nevertheless contains

Primates excel at reasoning about physical scenes comprised of objects, flexibly generalizing to novel compositions of objects. Many cognitive theories attribute this capacity to structured representa

The human brain finds solutions to complex multi-stage decision problems that are far more flexible than those learned by artificial systems. Cognitive theories attribute this flexibility to specific

Predicting the future state of the environment is a crucial task for neural systems, which must compensate for significant delays in both sensation and action. Despite this fundamental importance, we

Animals learn to reason and act successfully based on internal estimates of the state of their environment and their relationship to it. Such perceptual estimation is complicated by noise and ambiguit

Bayesian models have been successful at accounting for human and animal behavior, yet to what degree they can also explain neural activity is still an open question. While decoding approaches that lin

The proliferation of deep artificial neural network models has given rise to widespread interest in comparing such models to biological sensory systems. Model metamers – stimuli that produce the same

Neural oscillations are ubiquitously observed in many brain areas. One proposed functional role of these oscillations is that they serve as an internal clock, or ‘frame of reference’ relative to which

Information encoding in neural circuits depends on how well time-varying stimuli are encoded by neural populations. Slow neuronal timescales, noise, and network chaos can compromise reliable and rapid

An animal’s dominance status strongly affects its behavioural decision making in social contexts. While the thalamocortical circuitry involved in the establishment of male mouse social status has rece

Studies of the mouse visual system have revealed a variety of visual brain areas in a roughly hierarchical arrangement, together with a multitude of behavioral capacities, ranging from stimulus-reward

To address the question of how to compare DNN model activations to brain data, we investigate what transforms best describe similarity in neural activity in the same brain area between conspecifics. W

Voluntary movement is often prepared by motor cortex before its execution. Motor cortical neurons exhibit preparatory-activity, a neural correlate of upcoming movement [1]. Animals adopt different beh

Social memory, one's ability to recognize and remember experiences with other conspecifics, consists of several discrete psychological processes. These include the ability to rapidly detect whether an

Synchronization of neural activity is a fundamental component of information processing in the nervous system. Precise axonal conduction is required for correlated spiking activity between pairs of ne

Neuronal systems need to process temporal signals. Here, we hypothesize that temporal (co-)fluctuations – corresponding to high-order statistics beyond the average activity – are relevant for computat

Our understanding of how the brain makes decisions has benefited greatly from normative theories that have, for example, established benchmarks for how specific forms of evidence accumulation and deci

Working memory (WM) retains information during delay periods. How global neuronal activities maintain WM remains unclear. We recorded brain-wide activity via Neuropixels in mice performing a delay-var

The striatum is crucial for motor control and reinforcement learning. The striatum has only 1% fast-spiking interneurons (FSI) which constitute feedforward inhibition (FFI). Therefore, striatal neuron

Animals identify odors based on the combinatorial activation of olfactory sensory neurons having distinct chemical receptive fields. However, as the dose response of any olfactory sensory neuron is be

To navigate in a natural environment, a flying fly must estimate and contend with external forces such as wind. Estimating wind direction is difficult because the fly cannot directly sense ambient win

Both humans and monkeys are skillful at learning to form representations of latent states of their environments, those that must be inferred from observations. The neural circuits that govern this sop

Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest in modeling neural d

Precise identification of in-vivo cell types using electrophysiological signals would allow for the simultaneous monitoring of different cell populations during behavior. Current approaches typically

Learning general principles from single examples is a hallmark of high cognitive functions. This type of learning is computationally challenging for artificial agents, yet many animals such as primate

An open question in neuroscience is how the brain controls naturally generated movements. An increasing number of studies have shown that high-dimensional neural population dynamics lie within low-dim

We employed a dynamical systems perspective to bridge decision-related neural activity and decision-making behavior, a fundamentally unresolved problem. The dynamical systems approach posits that neur

We used dense (20 m site spacing) local field potential (LFP) and single unit recordings across all layers of the gRSC concurrent with CA1 LFP recordings in behaving mice to investigate circuit activ

An emerging perspective in neuroscience connects neural dynamics, or the rules that govern how neural population activity develops over time, to functional computations performed by the brain [1]. Unf

Learning relies as much on synaptic weight modifications as it does on the timing of neural signaling. For instance, variability in axonal length and diameter may compromise spike-timing-dependent pla

Accurate perception requires the integration of external (bottom-up) and internally generated (top-down) information. The main recipient of top-down projections in cortex is layer 1, which houses the

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory (WM) is biased towards the average of past observations. It is assum

In addition to long-time scale rewiring, synapses in the brain are subject to significant modulation that occurs at much shorter time scales and allows them to process short-term information. Despite

A fundamental goal of neuroscience is the functional understanding of neural circuit architecture. Previous studies revealed the importance of network depth in visual and auditory circuits, but it rem

A prominent theory of sensory perception advocates that perception in the brain is implemented via probabilistic inference. The neural sampling hypothesis (NSH) posits that neuronal responses to a sti

Modest success has been made in using resting state fMRI (rs-fMRI) to predict individualized behaviors. Here, we describe a scalable and modular system for individualized representation learning of rs

Recurrent network models of excitatory (E) and inhibitory (I) neurons with supralinear activation functions have successfully explained several cortical computations, including response normalization

A fundamental problem in systems neuroscience is understanding how the brain learns the non-linear dynamics of the complex world and identifies the environment's state. Data-driven learning in such hi

Classical studies in reinforcement learning (RL) heavily rely on normative models of behavior, models that often stress interpretability over predictive capabilities. More recently, neural network mod

The structure of neural circuitry plays a crucial role in brain function. Previous studies of brain organization generally had to trade off between coarse descriptions at a large scale and fine descri

Aggression, leading to the formation of social hierarchy, has an evolutionary role in reducing conflict and facilitating the allocation of limited resources. Previous studies in mice have identified t

Learning occurs in the context of individual prior experience. Variability in learning is, therefore, due to both differences in learning capacity as well as differences in those prior experiences. To

Cortical circuits embody remarkably reliable neural representations of sensory stimuli that are critical for perception and action. The fundamental structure of these network representations is though

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

Observers are aware of the fallibility of perception. When we experience a high degree of confidence in a perceptual interpretation, that interpretation is more likely to be correct than when we feel