Neural code

A combinatorial neural code for long-term motor memory

Breaking the neural code requires the systematic identification and characterization of neuronal ensembles related to specific brain functions. Recent research suggests that neuronal ensembles underlie brain functions such as perception, movement, and memory processes. However, the standardization of different mathematical algorithms that allow the identification and characterization of the properties of neuronal ensembles is still lacking in the systems neuroscience field. Our work addresses this gap by introducing an open-source Graphic User Interface (GUI) designed for researchers intereste

…Hence, the nature of the neural codes of macaque V1 neurons remains controversial. To resolve this question, we performed 2-photon calcium imaging on three macaque monkeys to obtain 1689 V1 neur

Recent studies on representational drift in the visual cortex have produced an intriguing puzzle, demonstrating that neuronal responses to the same external stimuli change over time while population-level activity remains stable (Deitch et al., 2021; Marks & Gourd, 2021). The purpose of drift, and how the brain either compensates for or takes advantage of this phenomenon to maintain robust perception, remains largely unknown. A recent study found that the geometric properties of drift in the primary visual cortex resemble that of continual learning in the presence of a large amount of drop

…discuss the implications of seeing the neural code as having three syllables: silences, spikes and bursts. In particular, I will describe recent theoretical

…for probabilistic inference in neural circuits, but it remains unknown how one can implement fast sampling algorithms in biologically-plausible spiking networks. Here, we propose to leverage the population geometry, controlled by the neural code and the neural dynamics, to implement fast samplers in spiking neural networks. We first show that two classes of spiking samplers---efficient balanced spi

Efficient coding has served as a guiding principle in understanding the neural code. To date, however, it has been explored mainly in the context of peripheral sensory cells with simple tuning curves. By contrast, ‘deeper’ neurons such as grid cells come with more complex tuning properties which imply a different, yet highly efficient, strategy for representing information. I will show that a highly efficient code is not specific to a population of neurons with finely tuned response properties: it emerges robustly in a shallow network with random synapses. Here, the geometry of population resp

How the brain stores a sequence in memory remains largely unknown. We investigated the neural code underlying sequence working memory using two-photon calcium imaging to record thousands of neurons in the prefrontal cortex of macaque monkeys memorizing and then reproducing a sequence of locations after a delay. We discovered a regular geometrical organization: The high-dimensional neural state space during the delay could be decomposed into a sum of low-dimensional subspaces, each storing the spatial location at a given ordinal rank, which could be generalized to novel sequences and explain mo

Understanding the neural code depends on capturing latent computational features within the highly complex dynamics of neural activity. While many features of neural dynamics will be relevant for a given computation, other features will not. In contrast with existing methods that describe how latent dynamics depend on experimental variables, we present a model designed to factor out variables that are irrelevant to the latent dynamics. We propose a fluctuating hidden Markov model (FHMM) that simultaneously learns an HMM that captures latent dynamics, and a baseline, dynamic firing rate that ca

Understanding the neural code for vision remains a major challenge for neuroscience, with recent efforts influenced by the success of convolutional neural networks. However, spiking neurons make biological vision fundamentally different from the firing rate model implicit in these artificial neural networks. In region V1 of the primate visual cortex, a flashed image is processed in tens of milliseconds, allowing time for only a few action potentials, typically across a small subset of neurons. Moreover, persistent eye motion (ocular drift) changes the location of retinal responses, and thus

Neural circuits must perform probabilistic computations to efficiently process noisy sensory information. Sampling codes propose that single neuron variability is a signature of probabilistic computation, and corresponds to sampling the space of possible solutions in proportion to their posterior probability. Under this hypothesis, the neural code defines how variables inferred by the network are represented in single neurons. Compared to other proposed probabilistic codes, the capacity of sampling codes scales with the number of neurons, but their convergence speed scales poorly with dimensio

…of the control representation's neural code: its representational dimensionality. Dimensionality of a neural representation balances a basic separability/generalizability trade-off in neural computation. This tradeoff has important implications for cognitive control. In this talk, I will present ini

…such methods to extract and analyze neural activity from 20,000 neurons recorded simultaneously in awake, behaving mice. The neural activity was not low-dimensional as commonly thought, but instead was high-dimensional and obeyed a power-law scaling across its eigenvalues. We developed a theory that proposes that neural responses to external stimuli maximize information capacity while maintaining a smooth neural code. We then observed power-law eigenvalue scaling in many real-world datasets,

…to exploit our knowledge of neural circuitry and function to restore or even enhance vision. However, although existing devices demonstrate proof of principle in treating incurable blindness, they produce limited visual function. Some of the reasons for this can be understood based on the precise and specific neural circuitry that mediates visual signaling in the retina. Consideration of this circuitry suggests that future devices may need to operate at single-cell, sing