Computational Statistics ePosters

Constructing well-motivated methodologies for quantifying similarity between high-dimensional neural representations is an active research in computational neuroscience. Such similarity measures have implications for understanding variability in neural computations across individuals and species [1], as well as for…

A fundamental property of natural intelligence is the capacity to recognise and leverage statistical patterns in the environment to promote adaptive behaviour. This capability, often termed statistical learning (SL), enables us to construct accurate models of the world that aid in perception and decision-making, when…

Introduction: Spontaneous activity in visual cortex is high-dimensional and state-dependent, yet its organizing principles are unclear (Stringer et al. Science 2019; Berkes et al. Science 2011). We combine large-scale imaging, modelling, and theory to ask: 1. Does spontaneous activity share the structure of evoked…

Recent findings have shown that the brain’s internal representation of the external world shifts over days, a phenomenon termed representational drift [Driscoll et al., 2017, Rule et al., 2020]. Thus, it becomes crucial to account for such non-stationarities when investigating the roles of circuits in the brain. While…

High-dimensional neural activity in the brain is known to encode low-dimensional, time-evolving, behaviour-related latent variables. A fundamental goal of neural data analysis consists of identifying such variables and their mapping to neural activity. The canonical approach is to assume the latent variables are…

Multimodal data fusion can facilitate a more complete understanding of neural dynamics. In neuroscience, modalities such as neuronal spiking activity, local field potentials (LFPs), and behavioral signals capture various aspects of brain processes. By leveraging the complementary strengths of different modalities,…

To date, most studies of neural representation have focused on how a single stimulus is encoded in the spiking activity of neurons. Consequently, little is known about how sensory neurons can preserve information from multiple stimuli – a problem given that many sensory neurons have relatively broad receptive fields.…

Latent variable models are widely used in neuroscience to extract dynamical structure from high-dimensional neural activity, and provide valuable insights into neural computation. However, these models are typically inferred from a single recording session and exhibit limited generalization capabilities. This is…

Hippocampal studies provide a rich window into the mechanisms of memory via the paradigm of spatial navigation. To interpret neural responses, it is common practice to first build encoding models using position during locomotion as the label, and then decode the position using this model. Of particular interest is the…

Studying and understanding the code of large neural populations hinges on learning accurate models of population activity. While statistical models based on pairwise interactions are highly accurate in describing the code of tens of neurons, accurate models of >100 neurons require higher order dependencies.…

Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While latent variable models can successfully extract low-dimensional embeddings from such recordings, using them to generate realistic spiking data,…

Although reinforcement learning has been highly successful in training artificial agents to perform a wide variety of complex tasks, the principles governing how animals actually learn to perform new tasks remain poorly understood. While normative approaches have provided profound insights into the design of optimal…

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…

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…

Disentangling independent latent components from neural data is a desirable goal in neuroscience. Independent component analysis (ICA) has been used to identify disentangled latent neural trajectories and their corresponding sub-brain maps from recorded imaging sequences. However, enforcing full independence between…

Generative models provide a powerful interpretive framework in neural reinforcement learning. However, given the complexity of behavior, models are often proposed in the absence of structurally different alternatives, poten-tially weakening their explanatory force. We employ a data-driven methodology (choice-wide…

A pivotal challenge in computational neuroscience is identifying sub-populations of neurons that encode behavior, perception, and memory, across varying contexts such as task difficulty or temporal progression. Therefore, analysis tools that provide model interpretability, beyond prediction performance, are essential…

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…

In the past decades, functional MRI research has investigated task processing in largely static fashion based on evoked responses during blocked and event-related designs. Despite some progress in naturalistic designs, our understanding of threat processing remains largely limited to those obtained with standard…

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…

Long-range temporal dependencies (LRTD) are an important signature of computation in the brain. They were shown to be decreased by Alzheimer’s and increased by epilepsy. An established way of capturing the LRTDs is to use Hurst exponents, for which estimation various methodologies were proposed in different fields of…

Psychedelics (5-HT2AR agonists) show great promise in treating mental disorders but their effects on neuronal population activity are not clear. Latent dimensions identified by Principal Component Analysis (PCA) capture the dynamics of this interrelated neuronal population activity. We asked whether latent dimensions…

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…