Neural population activity

…existing approaches to measure brain activity, connectivity, genomics, or other aspects across the age spectrum of neurodevelopment. The overarching goal is to extend our understanding of brain development and aging, including studies of the neurodevelopmental origins of later health and disease, by improving repeated measures across longer epochs of the lifespan to better predict outcomes at later ages

Complex brain functions are realized through the coordinated activity of populations of neurons. To manipulate brain function, it is crucial for brain stimulation techniques to achieve targeted control at the level of neural populations. Electrical microstimulation (uStim) has the potential to create targeted activity states because different combinations of stimulation parameters (e.g., location of the stimulated electrode(s), current amplitude, and frequency) produce diverse neural population activity states. However, its use for this purpose has been hindered by the challenge of identifying

…significant interest in structures of neural population activity such as dimensionality. A key question is how these dynamic features arise mechanistically and their relationship to circuit connectivity. It was previously proposed to use the covariance eigenvalue distribution, or spectrum, which can be analytically derived in random recurrent networks, as a robust measure for describe the shape of neural population activity beyond the dimension (Hu \& Sompolinsky 2022). Applications of the theoretical spectrum have bro

…brain region for these maps, exhibits neural population activity that often evolves along nonlinear, low-dimensional manifolds. While cognitive maps have been characterized at the level of cell tunings to spatial location and decorrelations over time, a precise, unsupervised, and quantitative model of how this map forms and morphs at the population level remains elusive due to the large dimensionality and nonlinearity of neural activity. Current dimensionality reduction t

…Can neural population dynamics simultaneously encode multiple timescales? Could modeling such temporal multiplexing help us understand how the brain integrates temporal information to guide appetitive behavior? To examine these questions, we recorded neural populati

The genome governs the developmental trajectory of neural circuits [1, 2], guiding processes like neurogenesis and synaptogenesis to ultimately shape the structure of the brain. Here, we study the impact of genetic variation during development on the dynamics and geometry of neural population activity in the motor cortex (MOp) of adult mice during natural feeding behavior. We focus on autism spectrum disorder (ASD) models, many cases of which are characterized by stereotypical motor impairments [3]. Specifically, we compare neural activity during a dexterous cued self-feeding task in two trans

…The associated neural population activity is often characterized by low-dimensional manifolds, orthogonal null and potent spaces, and rotations in neural trajectories. These features are conceptualized as “factors” or “latent dynamics” underlying movement preparation and execution, yet their origin and role in movement flexibility remain unclear. Current research attributes these factors to neural networks trained on specific tasks. However, unlike humans, these networks l

Recent studies have proposed that neural circuits generate a task manifold: i.e., a subset of the neural state-space to which neural activity is confined as an animal performs a task. Thus, discovering and characterising these manifolds from experimental data can shed light on the neural computations unfolding within the brain during behavioural tasks. In parallel, neuroscience has seen a recent surge of interest into fitting dynamical systems to experimental data with the goal of reverse-engineering neural circuits. Yet, common neural manifolds frameworks often do not take into account that n

…used to characterize the dynamics of neural population activity, they assume a stable mapping from the latent states to neural activity. This limits their ability to capture non-stationary processes---such as representational drif

…the dynamics of high-dimensional neural population activity. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, and thus to capture the effects of cell-specific optogenetic perturbations on neural activity and behavior. To address this gap, we introduce the "cell-type dynamical systems" (CTDS) model, which extends latent linear dynamical systems to contain distinct latent variables for each cell

…Yet, how neural populations distributed across the brain keep track of time remains unclear. Here, we set out to investigate large-scale neural population activity associated with timing behaviour. We developed a self-paced behavioural paradigm in which head-fixed mice learn to initiate reaching movements towards a spout dispensing drops of water reward. Mice start each trial themselves by holding onto a bar; the reward is then only dispensed if a cert

Understanding the neural mechanisms governing different states of cortical processing is pivotal for unraveling the cognitive processes underpinning perception. Observing freely-behaving animals offers a naturalistic environment that elicits a manifold of dynamically changing cognitive and behavioral states, and thus reflects real-world scenarios more accurately than under anesthesia or head-fixed conditions. In this study, we explore the interplay between evoked and spontaneous population activity and its impact on auditory processing within the primary auditory cortex of freely-moving gerbil

…However, the geometry of population activity in the SUB remains to be determined. This study investigated neural population activity recorded extracellularly from the CA1 and SUB of rats performing T-maze and open-field tasks. The population activity in both areas was confined to low-dimensional neural manifolds that were homeomorphic to the external space. The neural manifolds conveyed information about the rats' position, speed, and fut

…information is expressed in the activity of neural populations in the lateral entorhinal cortex (LEC). However, whether or how this code for episodic time is integrated with the spatial codes in the hippocampus remains to be determined. To answer this question, we implanted high-density silicon probes in the hippocampal CA1 region and LEC in mice. Activity in the two regions was simultaneously recorded while mice were either freely foraging in an open field arena or head-fixed runn

…field potentials to underlying neural sources (e.g. an “epileptogenic focus”). Yet still, how neural population activity relates to epileptic field potentials and ultimately clinical phenomenology, remains far from being understood. After a brief overview on this topic, this seminar will focus on unpublished work, with an emphasis on seizure-related focal spreading depression. The presented results will include hippocampal and neocortical chronic in vivo two-photon population imaging and local field potential recordings

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 dimensional data, they are typically only designed for a single type of data, making it difficult to identify structure shared across different experimental data modalities. Here, we address this shortcoming by proposing an LVM which extracts shared and independent latents for distinct, simultaneously recorded experimental modalities. We do this by combining Gaussian Process Factor Analysis (GPFA), an int

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 neural dynamics (X) are parameterized by a state equation (dX/dt=F(X)+U) with varying initial conditions (x0) and that evolves in time by combining at each time step, recurrent activity (F) and inputs (U). We considered different dynamical systems that make predictions about whether 1) initial conditions substantially predict subsequent dynamics and reaction time (RT) and/or choice, and 2) whether i

Neural activity in visual cortex is well described by models composed of a deterministic tuning function, a stochastic gain, and a point process. Recent work has shown that in the responses of neurons in the visual cortex of nonhuman primates, variability in the gain is tuned to the statistics of the sensory input. This suggests that gain variability may reflect signal rather than noise. Some theories of neural coding suggest that perceptual uncertainty may be encoded in neural response variability, and that, specifically, gain variability appears well suited to inform downstream uncertainty e

Distributed neural population activity in the macaque inferior temporal (IT) cortex, which lies at the apex of the visual ventral stream hierarchy, is critical in supporting an array of object recognition behavior. Previous research, however, has been agnostic to the relevance of specific cell types, inhibitory vs. excitatory, in the formation of “behaviorally sufficient” IT population codes that can accurately predict primate object confusion patterns. Therefore, here, we first compared the strength of behavioral predictions of neural decoding (“readout”) models constructed from specific (put

…Here we study neural population activity in prefrontal cortex of macaque monkeys trained to flexibly report categorical judgments of ambiguous visual stimuli. The subjects judged whether a visual stimulus presented near the central visual field was orient

…This is important for understanding neural control of movement, but dauting for the tangling of high-dimensional sensory and motor information. To address this question, we recorded neural population activity from the motor cortex while the monkeys were performing a manual interception task. Interestingly, on the neural states of single trials, we observed a low-dimensional ring-like neural geometry. This geometry, featured with ordered reach-direction clusters and tilted target-speed rings, also emerges in a three-layer RN

Calcium imaging (CI) has emerged as a prominent method for recording neural population activity. Therefore, CI recordings are prime candidates for the application of latent variable models designed to identify latent neural states and dynamics. However, in CI experiments spiking events are measured only indirectly via calcium ions that rapidly enter a cell during action potentials and then slowly seep back out. To analyze CI data, per-neuron fluorescence time-traces are typically either de-convolved to approximate spiking events or analyzed directly. The former approach, although enabling many

…learning (RL), where phasic dopamine activity encodes reward prediction errors to drive the learning of reward associations. In contrast to classical RL, however, where relevant state information is always provided to the agent, animals must find optimal behaviors using only incomplete state information. One theoretical observation is that animals (or agents) can solve these partially observable tasks by forming "beliefs," or optimal Bayesian posterior estimates of the state of the task. However, it is unknown whether animal

…estimates of object identity from neural population activity and the object-specific behavioral deficits observed after cortical perturbation, previous work has identified the primate ventral visual cortex as a critical brain circuit for core object recognition. In addition, our recent work has demonstrated that the ventrolateral prefrontal cortex (vlPFC; recurrently connected to the ventral stream) is also critical for robust object recognition. In this st