Neural trajectories

…continuity appears as reproducible neural trajectories rather than isolated events. Neuronal population events form persistent trajectories whose transitions depend on prior states and other brain areas. In human EEG, microstate sequences exhibit dependencies beyond dwell time and first-order Markov predictions, and stimuli systematically reshape those dependencies. Linking ensemble and EEG dynamics may reveal classes of individual responses and inform ge

Neural systems form high-dimensional representations that underpin their computational capabilities. Consequently, methods for quantifying representational similarity in neural representations have become a popular tool for identifying computational principles that are potentially shared across neural systems. These methods generally assume that neural responses are deterministic and static. However, responses of biological systems are noisy and dynamically unfold over time and these characteristics can have substantial influence on a system’s computational capabilities. We first show that exi

…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

Prompt execution of planned motor action is context-dependent and a critical component of animal behavior. Studies from humans, non-human primates, and rodents have ubiquitously established that the primary motor cortex (M1) is the main hub for skilled motor learning, which relies on the precise coordination of multiple brain regions. Yet, how various populations of neurons separated by millimeters reliably communicate is still unclear. Here, we investigate how moment-to-moment and trial-to-trial fluctuations of population dynamics in the secondary (M2) motor cortex relate to stable population

How does the brain process and integrate information from different sensory modalities? This intriguing question has been explored in this study by recording the activity of the Ventral Premotor Cortex (VPC) in two trained macaques (Macaca mulatta) while performing a Bimodal Detection Task (BDT) previously used by Parra et al., 2022. In BDT, subjects were required to identify and report the modality, or the absence thereof, of a near-threshold stimulus that could be either tactile or acoustic. An initial approach on single cells revealed the presence of neurons that were responsive to tactile

In the brain, the collective activity of neurons form trajectories on low dimensional neural manifolds. The neural computation underlying flexible cognition and behavior relies on dynamic control of these structures. For example, different tasks or behaviors are represented on different subspaces, requiring fast timescale subspace rotation to move from one behavior to the next. For flexibility in a particular behavior, the neural trajectory must be dynamically controllable within that behaviorally determined subspace. Here we propose mechanisms for fast timescale control of the neural subspace

…giving rise to perceptual biases whose neural mechanisms remain poorly understood. This study aimed to elucidate these mechanisms by examining prefrontal cortex (PFC) neural recordings from monkeys performing a vibrotactile frequency discrimination task (Fig. 1a). Monkey’s behavior exhibited the contraction bias (Fig. 1b), a contraction of the perceived first frequency towards its mean. This bias increased with longer delay periods, suggesting a Bayesian explanation of this effect. Examination of PFC’s ne

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

Sensory systems transform inputs from the environment into internal representations that capture aspects of the information useful in downstream computation and behavior. These representations are not rigid, static reflections of the external world but are dynamically modulated by fluctuating cortical and behavioral states. Locomotion, for example, is associated with facilitative changes in stimulus representation in the mouse visual cortex (Vinck et al 2015). How does sensory processing in the visual system support downstream behavior? "Straightening" hypotheses posit that an important role o

…Here we examined whether the neural trajectories of different component subspaces would reveal the computational principles of information integration. To this end, we conducted a cue-induced pain expectation experiment with functional Magnetic Resonance Imaging in humans (N = 56) to examine the com

…space as well as the corresponding neural sequences shared the following properties across modalities: the circular dynamics of the neural trajectories and the neural sequences formed a regenerating loop

…learning likely causes changes in neural connectivity, which may shape the neural dynamics that can be produced. To understand the interaction between circuit connectivity constraints and a neural population’s ability to change its activity patterns, we modeled the neural dynamics of the motor cortex during skill learning and subsequent adaptation using a recurr

…model the computation carried out by neural populations. This perspective hypothesizes that certain dynamical features underlie the computations performed by the system, and characterizing them enables an intuitive understanding of the system. One central feature is the fixed (or slow) point: the fixed points of the system and the linearized dynamics around them can explain important aspects of the computation that is being performed. While successful in many cases, fixed-points analysis has limitations. Finding fixed points is ge

The description of neural computations currently relies on two competing views: (i) a classical single-cell view that aims to relate the activity of individual neurons to sensory or behavioural variables, and organize them into functional classes; (ii) a more recent population view that instead characterises computations in terms of collective neural trajectories, and focuses on the dimensionality of these trajectories as animals perform tasks. How the two key concepts of functional cell classes and low-dimensional trajectories interact to shape neural computations is however at present not un