Latent variables

…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 behaviour (e.g. the measured location of the animal) and visualise the subsequent tuning curves. However, significant mismatches between behaviour and the encoded variables may still exist---the agent may be thinking of another location, or be uncertain of its own---distorting the tuni

…NNMF layers have latent variables that are updated iteratively towards a fixed

…requires tracking not only individual latent variables but also the interactions among them. As a result, a neural circuit that optimally encodes these variables would need to be recurrently wired to relay information between them via feedback connections [2]. Given the additional cost of evolving and maintaining these feedback connections, we sought the circumstances under which t

Animals and humans rapidly adapt their behavior to dynamic environmental changes, such as predator threats or fluctuating food resources, often without immediate rewards. Existing literature posits that animals rely on internal representations of the environment, termed “beliefs”, for their decision policy. However, previous work ties belief updates to external reward signals, which does not explain adaptation in scenarios where trial-and-error approaches are inefficient or potentially perilous. In this work, we propose that the brain utilize dynamic representations that continuously infer the

…constrained to a fixed low-dimensional latent subspace. Consequently, this view does not capture how the latent subspace could evolve over learning, nor how high-dimensional neural activity could emerge over learning. Furthermore, the link between these empirically-observed changes in neural

…of behavioral and cognitive variables in a task that requires both. We use a virtual reality task in which mice navigate in a T-maze and accumulate transiently visible and stochastic left/right cues to infer the correct reward location. Neural data is recorded acutely with up to 10 Neuropixel shanks simultaneously in bilateral HPC and medial prefrontal cortex (mPFC). To compare neural activity patterns, we analyze this data

…link posterior beliefs about specific latent variables in a generative model to neural responses. On one hand, the specificity of the resulting predictions is desirable since it allows us to decide which of the infinitely many parameterizations of the task model (ideal observer) is more closely align

Network models are often designed to capture selective aspects of cortical circuits. On one end, mechanistic models such as balanced spiking networks resemble activity regimes observed in data, but are often limited to simple computations. On the other end, functional models like trained deep networks can show comparable performance and dynamical motifs, but are far removed from experimental physiology. Here, we put forth a new framework for excitatory-inhibitory spiking networks which retains key properties of both mechanistic and functional models. Based on previous studies of the geometry o

An influential theory in systems neuroscience suggests that brain function can be understood through low-dimensional dynamics [Vyas et al 2020]. However, a challenge in this framework is that a single computational task may involve a range of dynamic processes. To understand which processes are at play in the brain, it is important to use data on neural activity to constrain models. In this study, we present a method for extracting low-dimensional dynamics from data using low-rank recurrent neural networks (lrRNNs), a highly expressive and understandable type of model [Mastrogiuseppe & Ostojic

The spike-threshold nonlinearity is a fundamental, yet enigmatic, component of biological computation — despite its role in many theories, it has evaded definitive characterisation. Indeed, much classic work has attempted to limit the focus on spiking by smoothing over the spike threshold or by approximating spiking dynamics with firing-rate dynamics. Here, we take a novel perspective that captures the full potential of spike-based computation. Based on previous studies of the geometry of efficient spike-coding networks, we consider a population of neurons with low-rank connectivity, allowing

…behavior, and then regress the latent variables of those models onto fMRI data. Though widely used, this approach is restricted to mapping computational variables that are in some way expressed in behavior. Here we introduce a computational parametric mapping (CPM), a framework which builds on and generalizes the Bayesian population receptive field framework. CPM offers three main adva

Effectively modeling and quantifying behavior is essential for our understanding of the brain. Modeling behavior in naturalistic settings in social and multi-subject tasks in a unified manner remains a significant challenge. Modeling the behavior of different subjects performing the same task requires partitioning the behavioral data into features that are common across subjects, and others that are distinct to each subject. Modeling social interactions between multiple individuals in a freely-moving setting requires disentangling effects due to the individual as compared to social investigati

…tools allow us to uncover latent variables that define different decision making approaches, even in animals with similar correct performance. Here, we quantify sex differences in mice in the latent variables underlying

Technological advances have increased the availability of recordings from large populations of neurons across multiple brain areas. Coupling these recordings with dimensionality reduction techniques, recent work has led to new proposals for how populations of neurons can send and receive signals selectively and flexibly. Advancement of these proposals depends, however, on untangling the bidirectional, parallel communication between neuronal populations. Because our current data analytic tools struggle to achieve this task, we have recently validated and presented a novel dimensionality reducti