Bayesian Statistics ePosters

Hippocampal remapping describes the phenomenon of substantial reshuffling of the spatial preferences of hippocampal place cells in response to environmental changes. Disparate neural representations in different environments could reduce interference between competing memories, hence functionally supporting robust…

Human perception is significantly influenced by sensory context. A classic demonstration is the tilt illusion, in which the perceived orientation of a center stimulus is altered by the orientation of a surround (Gibson \& Radner 1937). Previous research on the tilt illusion has primarily focused on how surround…

To survive, organisms must make rapid inferences about their surroundings. For example, to successfully escape approaching predators, prey must estimate the direction of approach from noisy stimuli [1]. Such rapid inferences are particularly challenging because there is little time to gather sensory information, yet…

Social interactions in nonhuman primates involve complex communication, outcome evaluation, and behavioral adjustments. Social gaze is a vital mechanism for gathering information during such interactions. We investigated the role of social gaze in cooperative behavior among common marmosets (Callithrix jacchus) using…

Perception balances what we sense with what we expect. To maintain accuracy, our brains must continuously update sensory expectations when predictions don't match reality. To investigate this, we trained mice on an auditory detection task with hidden switches in stimulus probability across trials. Mice dynamically…

The neural control of movement is a fundamental aspect of understanding how the nervous system coordinate and regulate motor behavior. Procedural safety is a critical concern for movement control, especially for online tasks in high-dimensional control spaces. Current safe exploration algorithms exhibit inefficiency…

The ability to learn and adapt to cognitive demands is vital for survival, and mice serve as valuable models in neuroscience for studying these processes. In this study, we introduce a novel maze framework to investigate learning and adaptability in mice, specifically focusing on humanized App knock-in models of…

Recent evidence suggests the brain performs sampling-based Bayesian inference to solve various cognitive tasks [1–6]. Neural sampling requires neural circuits to exhibit statistically stable variability to draw posterior samples. While ad-hoc frameworks simply inject randomness into spike emission, it remains unknown…

Bayesian models have been successful in explaining sensorimotor behavior in humans and animals, yet it remains unclear whether they can also explain neural activity. Testing them using neural data is complicated by the fact that linking them to neural activity requires two key moving parts: (1) a hypothesis about…

Neural circuits display both input-driven activity that is necessary for the real-time control of behavior and internally generated activity that is necessary for memory, planning, and other cognitive processes. A key mediator between these intrinsic and evoked dynamics is arousal, an internal state variable that…

Relational invariance---a network’s ability to capture the geometric relationships between input patterns abstracted away from explicit external variables---has the potential to enable robustness and generalizability in neural networks. Previous work from our group has shown that a combinatorial neuronal code…

Cognition arises from the coordinated interaction of multiple circuits with individual computational roles. While our ability to extract the dynamics underlying circuit computation from population activity recorded in individual areas, understanding how multiple areas jointly support distributed computation remains a…

Understanding how cortical circuits encode sensory information remains a key challenge. Given a stimulus, the central question from the prespecitive of probablistic theories is how posterior probablities are neurally encoded. In recent years, standard approaches like sampling-based and variational approximations have…

Neuroscientific data exhibits complex, nonlinear dynamics, varying in time and over trials. A growing body of work has focused on capturing nonlinear dynamics, doing so in increasingly complex ways for modeling and inference. Meanwhile, capturing the dependency on experimental conditions surrounding the neural…

Uncertainty is a fundamental aspect of the natural environment. Computing with uncertain information is important not only for encoding and decoding stimuli in early sensory areas, but also for more complex downstream inference and integration. The head-direction (HD) system is a prominent example of this, where…

Center-surround processing (CSP) is a ubiquitous feature of the brain, starting from early sensory processing (e.g., context-dependent retinal encoding) to representing abstract concepts (e.g., context-dependent object representation), manifesting itself in both behavior and neural responses. While CSP in neural…

Exploring the origins of animal and human behavior within cortical circuits remains a puzzle awaiting resolution. It is established that when animals and humans compare two stimuli (d1 and d2) separated by a delay interval (Fig. 1a), their perception is influenced by a perceptual bias, characterized by a contraction…

In the awake human brain, alpha-band activity (8–13 Hz) is the most prominent electrophysiological signature. Several models exist for alpha-rhythm generation using neural mass models, including the Jansen-Rit, Moran-David-Friston, Robinson-Rennie-Wright, and Liley-Wright models [1]. Fitting these models traditionally…

The generation of appropriate parameter configurations for high-dimensional hierarchical spiking neural network models is currently one of the slowest and most frustrating tasks in the computational neuroscience workflow. Moreover, many of the currently used methods for multidimensional parametrization only provide…

Advancing therapies for neurological disorders relies on developing efficient, personalized whole-brain models that can capture mechanistic insights and pathological features. Current challenges in creating such computational models arise from managing a considerable number of adjustable parameters. The conventional…

Systematic biases in perception can be either attractive or repulsive with respect to the immediate spatial or temporal context. There is an extensive amount of literature [1,2,3] on how to explain these differences in biases with a variety of mechanisms, ranging from sensory adaptation over efficient coding to…

The visual system processes vast amounts of information by internally weighting stimulus inputs based on their local features and behavioral relevance [1]. Although evidence shows that behavioral relevance influences stimulus saliency, it is not well understood how integrating stimulus and social information impacts…

A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should ideally be both interpretable and fit the observed data well. Low-rank recurrent neural networks (RNNs) exhibit such interpretability by having…

Motor and premotor cortical activities reflect motor planning and execution. For stereotyped movements, the internal cortical state should be predictive of subsequent motor commands. We developed a statistical method to infer a nonlinear state-space model that extracts an effective systems-level description of neural…