Biology subjects

Solla, S. A.

Publications and source records attributed to Solla, S. A..

12 recordsLinked to original sources

bioRxiv2026

Hippocampal trace coding dominates and disrupts place coding

The hippocampus is widely viewed as a spatial map because many CA1 neurons exhibit spatially selective activity during exploration. However, the hippocampus is also involved in nonspatial learning, including conditioning tasks. Most prior reports of nonspatial hippocampal activity were obtained in immobile animals, leading to the proposal that the hippocampus encodes spatial variables during locomotion and nonspatial variables during immobility. The question of how the hippocampus encodes both spatial and nonspatial variables during locomotion and spatial exploration has remained unanswered. To address this issue directly, we used calcium imaging to record the activity of thousands of CA1 neurons from rats tested on a conditioning task while freely exploring an open field. Across more than 6,000 neurons from five rats, the rate of calcium events increased during conditioning task periods relative to non-task periods, an effect that persisted after controlling for locomotor speed. At the single cell level, conditioning task modulation was widespread and strongly biased toward increased activity; neurons whose activity was enhanced during the conditioning task outnumbered spatially selective neurons by more than threefold. In addition, many neurons showed significantly increased activity during specific segments of the conditioning task. At the population level, conditioning coding remained invariant to changing spatial location; in contrast, spatial coding was reduced during task periods. These findings show that during active behavior, engagement in the conditioning task reorganizes hippocampal activity into a temporally structured pattern of population activity that can dominate and disrupt place coding. Together, these results challenge the view that the hippocampus functions primarily as a stable spatial map.

neuroscience↗

bioRxiv2024

A universal hippocampal memory code across animals and environments

Learned behaviors often generalize across contexts even though neural representations can vary substantially between environments. How the brain preserves task-relevant information across these changing neural representations remains unclear. This problem is particularly evident in the hippocampus because hippocampal spatial representations reorganize across environments even when learned behaviors generalize successfully. Here, we recorded hippocampal neuronal activity using calcium imaging while rats performed a conditioning task in two distinct environments. Despite remapping of spatial representations, the low-dimensional organization of task related population activity was conserved across contexts, and the temporal relationships among neural population states during task execution were preserved. Strikingly, this relational organization was conserved not only across contexts for individual animals, but also across animals, despite differences in the underlying neural populations and individual experiences. Moreover, this conserved organization was sufficient to support transfer of task decoding across animals, demonstrating that task information could generalize between independently learned neural representations without shared neuron identities. These findings identify a conserved population level neural organization through which the hippocampus can support behavioral generalization despite contextual remapping and suggest that shared organizational neural mechanisms and structures may underlie behavioral generalization across individuals.

neuroscience↗

bioRxiv2023

Low-dimensional neural manifolds for the control of constrained and unconstrained movements

Across many brain areas, neural population activity appears to be constrained to a low-dimensional manifold within a neural state space of considerably higher dimension. Recent studies of the primary motor cortex (M1) suggest that the activity within the low-dimensional manifold, rather than the activity of individual neurons, underlies the computations required for planning and executing movements. To date, these studies have been limited to data obtained in constrained laboratory settings where monkeys executed repeated, stereotyped tasks. An open question is whether the observed low dimensionality of the neural manifolds is due to these constraints; the dimensionality of M1 activity during the execution of more natural and unconstrained movements, like walking and picking food, remains unknown. We have now found similarly low-dimensional manifolds associated with various unconstrained natural behaviors, with dimensionality only slightly higher than those associated with constrained laboratory behaviors. To quantify the extent to which these low-dimensional manifolds carry task-relevant information, we built task-specific linear decoders that predicted EMG activity from M1 manifold activity. In both settings, decoding performance based on activity within the estimated low-dimensional manifold was the same as decoding performance based on the activity of all recorded neurons. These results establish functional links between task-specific manifolds and motor behaviors, and highlight that both constrained and unconstrained behaviors are associated with low-dimensional M1 manifolds.

neuroscience↗

bioRxiv2022

Monkey-to-human transfer of brain computer interface decoders

Intracortical brain-computer interfaces (iBCIs) enable paralyzed persons to generate movement, but current methods require large amounts of both neural and movement-related data to be collected from the iBCI user for supervised decoder training. We hypothesized that the low-dimensional latent neural representations of motor behavior, known to be preserved across time, might also be preserved across individuals, and allow us to circumvent this problem. We trained a decoder to predict the electromyographic (EMG) activity for a "source" monkey from the latent signals of motor cortex. We then used Canonical Correlation Analysis to align the latent signals of a "target" monkey to those of the source. These decoders were as accurate across monkeys as they were across sessions for a given monkey. Remarkably, the same process with latent signals from a human participant with tetraplegia was within 90% of the with-monkey decoding across session accuracy. Our findings suggest that consistent representations of motor activity exist across animals and even species. Discovering this common representation is a crucial first step in designing iBCI decoders that perform well without large amounts of data and supervised subject-specific tuning.

neuroscience↗

bioRxiv2022

Statistical characterization of tactile scenes in three-dimensional environments reveals filter properties of somatosensory cortical neurons

Natural scenes statistics have been studied extensively using collections of natural images and sound recordings. These studies have yielded important insights about how the brain might exploit regularities and redundancies in visual and auditory stimuli. In contrast, natural scenes for somatosensation have remained largely unexplored. Here we use three-dimensional scans of natural and human-made objects to quantify natural scene statistics at the scale of the human fingertip. Using measurements of distance, slope, and curvature from the object surfaces, we show that the first order statistics follow similar trends as have been observed for images of natural and human-made environments. In addition, independent component analysis of curvature measurements reveals Gabor-like basis vectors similar to those found in natural images. A simple neural model using these filters showed responses that accurately capture the statistics of responses in primate primary somatosensory cortex.

neuroscience↗

bioRxiv2021

A novel approach to the empirical characterization of learning in biological systems

Learning to execute precise, yet complex, motor actions through practice is a trait shared by most organisms. Here we develop a novel experimental approach for the comprehensive investigation and characterization of the learning dynamics of practiced motion. Following the dynamical systems framework, we consider a high-dimensional behavioral space in which a trial-by-trial sequence of motor outputs defines a trajectory that converges to a fixed point - the desired motor output. In this scenario, details of the internal dynamics and the trial-by-trial learning mechanism cannot be disentangled from behavioral noise for nonlinear systems or even well estimated for linear systems with many parameters. To overcome this problem, we introduce a novel approach: the sporadic application of systematic target perturbations that span the behavioral space and allow us to estimate the linearized dynamics in the vicinity of the fixed point. The steady-state Lyapunov equation then allows us to identify the noise covariance. We illustrate the method by analyzing sequence-generating neural networks with either intrinsic or extrinsic noise, at time resolutions that span from spike timing to spiking rates. We demonstrate the utility of our approach in experimentally plausible and realizable settings and show that this method can fully characterize the linearized between-trials learning dynamics as well as extract meaningful internal properties of the unknown mechanism that drives the motor output within each trial. We then illustrate how the approach can be extended to nonlinear learning dynamics through a flexible choice of the basis and magnitude of perturbations. Significance statementMovement control ties brain activity to measurable external actions in real time, providing a useful tool for both neuroscientists interested in the emergence of stable behavior and biomedical engineers interested in the design of neural prosthesis and brain-machine interfaces. We approach the question of motor skill learning by introducing artificial errors through a novel perturbative scheme amenable to analytic examination in the linearized regime close to the desired behavior. Numerical simulations then demonstrate how to probe the learning dynamics in both linear and nonlinear systems. These findings stress the usefulness of analyzing responses to deliberately induced errors and the importance of properly designing such perturbation experiments. Our approach provides a novel generic tool for monitoring the acquisition of motor skills.

neuroscience↗

bioRxiv2020

Estimating the dimensionality of the manifold underlying multi-electrode neural recordings

It is generally accepted that the number of neurons in a given brain area far exceeds the information that area encodes. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding from the low-dimensional manifold to the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated dimensionality. We thus developed a denoising algorithm based on deep learning, the "Joint Autoencoder", which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data. Author SummaryThe number of neurons that we can record from has increased exponentially for decades; today we can simultaneously record from thousands of neurons. However, the individual firing rates are highly redundant. One approach to identifying important features from redundant data is to estimate the dimensionality of the neural recordings, which represents the number of degrees of freedom required to describe the data without significant information loss. Better understanding of dimensionality may also uncover the mechanisms of computation within a neural circuit. Circuits carrying out complex computations might be higher-dimensional than those carrying out simpler computations. Typically, studies have quantified neural dimensionality using one of several available methods despite a lack of consensus on which method would be most appropriate for neural data. In this work, we used several methods to investigate the accuracy of simulated neural data with properties mimicking those of actual neural recordings. Based on these results, we devised an analysis pipeline to estimate the dimensionality of neural recordings. Our work will allow scientists to extract informative features from a large number of highly redundant neurons, as well as quantify the complexity of information encoded by these neurons.

neuroscience↗

bioRxiv2019

Continuous, multidimensional coding of 3D complex tactile stimuli by primary sensory neurons of the vibrissal system

To reveal the full representational capabilities of sensory neurons, it is essential to observe their responses to complex stimuli. In the rodent vibrissal system, mechanical information at the whisker base drives responses of primary sensory neurons in the trigeminal ganglion (Vg). Studies of how Vg neurons encode stimulus properties are typically limited to 2D analyses and restricted stimulus sets. Here we record from Vg neurons during 3D stimulation while quantifying the complete 3D whisker shape and mechanics. Results show that individual Vg neurons simultaneously represent multiple mechanical features of the stimulus, do not preferentially encode principal components of the stimuli, and represent continuous and tiled variations of all available mechanical information. As a population, the neurons span a continuum of rapid and slow adaptation properties; a binary distinction between these adaptation classes is oversimplified. These results contrast with proposed codes in which Vg neurons segregate into functional classes.

neuroscience↗

bioRxiv2019

The dynamics of motor learning through the formation of internal models

A medical student learning to perform a laparoscopic procedure as well as a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for the external device. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of learning dynamics and the performance observed in a group of subjects demonstrate first-order exponential convergence of the learning process toward a particular state that depends only on the initial inverse and forward models and on the supplied sequence of targets. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes.\n\nAuthor summarySeveral studies have suggested that as we learn a new skill our brain forms representations, or \"internal models\", of the skill and of the environment in which we operate. Theories of motor learning postulate that the brain builds forward models that predict the sensory consequences of motor commands, and inverse models that generate successful commands from planned movements. We test this hypothesis taking advantage of a special interface that generates a novel relation between the subjects actions and the position of a cursor on a computer monitor, thus allowing subjects to control an external device by movements of their body. We recorded the motions of the body and of the cursor, and obtained estimates of both forward and inverse models. We followed how these estimates evolved in time as subjects practiced and acquired a new skill. We found that the description of learning as a simple deterministic process driven by the sequence of targets is sufficient to capture the observed convergence to a single solution of the inverse model among an infinite variety of alternative possibilities. This work is relevant to the study of fundamental learning mechanisms as well as to the design of intelligent interfaces for people with paralysis.

neuroscience↗

bioRxiv2018

A stable, long-term cortical signature underlying consistent behavior

Animals readily execute learned motor behaviors in a consistent manner over long periods of time, yet similarly stable neural correlates remained elusive up to now. How does the cortex achieve this stable control? Using the sensorimotor system as a model of cortical processing, we investigated the hypothesis that the dynamics of neural latent activity, which capture the dominant co-variation patterns within the neural population, are preserved across time. We recorded from populations of neurons in premotor, primary motor, and somatosensory cortices for up to two years as monkeys performed a reaching task. Intriguingly, despite steady turnover in the recorded neurons, the low-dimensional latent dynamics remained stable. Such stability allowed reliable decoding of behavioral features for the entire timespan, while fixed decoders based on the recorded neural activity degraded substantially. We posit that latent cortical dynamics within the manifold are the fundamental and stable building blocks underlying consistent behavioral execution.

neuroscience↗

bioRxiv2017

Use of a Neural Circuit Probe to Validate in silico Predictions of Inhibitory Connections

Understanding how neuronal signals propagate in local network is an important step in understanding information processing. As a result, spike trains recorded with Multi-electrode Arrays (MEAs) have been widely used to study behaviors of neural connections. Studying the dynamics of neuronal networks requires the identification of both excitatory and inhibitory connections. The detection of excitatory relationships can robustly be inferred by characterizing the statistical relationships of neural spike trains. However, the identification of inhibitory relationships is more difficult: distinguishing endogenous low firing rates from active inhibition is not obvious. In this paper, we propose an in silico interventional procedure that makes predictions about the effect of stimulating or inhibiting single neurons on other neurons, and thereby gives the ability to accurately identify inhibitory causal relationships. To experimentally test these predictions, we have developed a Neural Circuit Probe (NCP) that delivers drugs transiently and reversibly on individually identified neurons to assess their contributions to the neural circuit behavior. With the help of NCP, three inhibitory connections identified by our in silico modeling were validated through real interventional experiments. Together, these methods provide a basis for mapping complete neural circuits.

neuroscience↗

bioRxiv2017

Multiple tasks viewed from the neural manifold: Stable control of varied behavior

How do populations of cortical neurons have the flexibility to perform different functions? We investigated this question in primary motor cortex (M1), where populations of neurons are able to generate a rich repertoire of motor behaviors. We recorded neural activity while monkeys performed a variety of wrist and reach-to-grasp motor tasks, each requiring a different pattern of neural activity. We characterized the flexibility of M1 movement control by comparing the \"neural modes\" that capture covariation across neurons, believed to arise from network connectivity. We found large similarities in the structure of the neural modes across tasks, as well as striking similarities in their temporal activation dynamics. These similarities were only apparent at the population level. Moreover, a subset of these well-preserved modes captured a task-independent mapping onto muscle commands. We hypothesize that this system of flexibly combined, stable neural modes gives M1 the flexibility to generate our wide-ranging behavioral repertoire.

neuroscience↗