Biology subjects

Burak, Y.

Publications and source records attributed to Burak, Y..

8 recordsLinked to original sources

bioRxiv2025

Stabilization of memory on neural manifolds through multiple synaptic time scales

Working memory is commonly thought to be implemented in the brain by the persistent activity of attractor neural networks. Compelling evidence exists for the implementation of such memory networks in vertebrate and invertebrate brains. Naively, retention of continuous memories in attractor networks - as observed in the head direction systems of mammals and insects and the oculomotor integrator in vertebrates - is particularly sensitive to neural stochasticity, because noise can continuously shift the activity of the network along its manifold of steady states. As these random drifts accumulate over time, the stored memory degrades. Small networks, such as the head direction system of insects, are particularly prone to this form of memory deterioration, and it is unclear how such networks can maintain their persistent state over behaviorally relevant timescales. Here we identify a neural mechanism that effectively counteracts random drifts by employing a combination of slow excitatory and fast inhibitory synaptic connections. We show that the proposed mechanism can substantially decrease diffusivity in linear and nonlinear networks. Hence, the mechanism offers a plausible explanation for how the brain can stably store memories of continuous quantities, despite the ubiquity of noise. Finally, we identify how to engineer connectivity such that variability in neural activity perpendicular to the attractor is unaffected by the stabilization mechanism. This scheme allows for tight confinement of neural activity patterns to a low dimensional manifold, as well as the rapid relaxation of transient modes of activity back into the attractor. The theory suggests that neurons in head direction cell networks that are commonly thought to be utilized for velocity integration may also aid in stabilization against noise-driven motion.

neuroscience↗

bioRxiv2024

Universal statistics of hippocampal place fields across species and dimensionalities

Hippocampal place cells form a spatial map by selectively firing at specific locations in an animals environment1. Until recently the hippocampus appeared to implement a simple coding scheme for position, in which each neuron is assigned to a single region of space in which it is active1. Recently, new experiments revealed that the tuning of hippocampal neurons to space is much less stereotyped than previously thought: in large environments, place cells are active in multiple locations and their fields vary in shape and size across locations, with distributions that differ substantially in different experiments2-7. It is unknown whether these seemingly diverse observations can be explained in a unified manner, and whether the heterogeneous statistics can reveal the mechanisms that determine the tuning of neural activity to position. Here we show that a surprisingly simple mathematical model, in which firing fields are generated by thresholding a realization of a random Gaussian process, explains the statistical properties of neural activity in quantitative detail, in bats and rodents, and in one-, two-, and three-dimensional environments of varying sizes. The model captures the statistics of field arrangements, and further yields quantitative predictions on the statistics of field shapes and topologies, which we verify. Thus, the seemingly diverse statistics arise from mathematical principles that are common to different species and behavioral conditions. The underlying Gaussian statistics are compatible with a picture in which the synaptic connections between place cells and their inputs are random and highly unstructured.

neuroscience↗

bioRxiv2022

Random Compressed Coding with Neurons

Classical models of efficient coding in neurons assume simple mean responses-- tuning curves--such as bellshaped or monotonic functions of a stimulus feature. Real neurons, however, can be more complex: grid cells, for example, exhibit periodic responses which impart the neural population code with high accuracy. But do highly accurate codes require fine tuning of the response properties? We address this question with the use of a benchmark model: a neural network with random synaptic weights which result in output cells with irregular tuning curves. Irregularity enhances the local resolution of the code but gives rise to catastrophic, global errors. For optimal smoothness of the tuning curves, when local and global errors balance out, the neural network compresses information from a high-dimensional representation to a low-dimensional one, and the resulting distributed code achieves exponential accuracy. An analysis of recordings from monkey motor cortex points to such compressed efficient coding. Efficient codes do not require a finely tuned design--they emerge robustly from irregularity or randomness.

neuroscience↗

bioRxiv2021

Grid-cell modules remain coordinated when neural activity is dissociated from external sensory cues

The representation of an animals position in the medial entorhinal cortex (MEC) is distributed across several modules of grid cells, each characterized by a distinct spatial scale. The population activity within each module is tightly coordinated and preserved across environments and behavioral states. Little is known, however, about the coordination of activity patterns across modules. We analyzed the joint activity patterns of hundreds of grid cells simultaneously recorded in animals that were foraging either in the light, when sensory cues could stabilize the representation, or in darkness, when such stabilization was disrupted. We found that the states of different grid modules are tightly coordinated, even in darkness, when the internal representation of position within the MEC deviates substantially from the true position of the animal. These findings suggest that internal brain mechanisms dynamically coordinate the representation of position in different modules, to ensure that grid cells jointly encode a coherent and smooth trajectory of the animal.

neuroscience↗

bioRxiv2021

Toroidal topology of population activity in grid cells

The medial entorhinal cortex (MEC) is part of a neural system for mapping a subjects position within a physical environment1,2. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations3, and are organized in modules4,5 which collectively form a population code for the animals allocentric position1,6-8. The invariance of the correlation structure of this population code across environments9,10 and behavioural states11,12, independently of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern1,2,13-16. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained elusive due to the small samples of cells obtained to date. Here we show, with simultaneous recordings from many hundreds of grid cells, and subsequent topological data analysis, that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to the moving animals position in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained, with minimal distortion, between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feed-forward models where grid patterns are created from external inputs by Hebbian plasticity17-22. This demonstration of network dynamics on a toroidal manifold provides the first population-level visualization of CAN dynamics in grid cells.

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bioRxiv2021

Fixational drift is driven by diffusive dynamics in central neural circuitry

During fixation and between saccades, our eyes undergo diffusive random motion called fixational drift [1]. The role of fixational drift in visual coding and inference has been debated in the past few decades, but the mechanisms that underlie this motion remained unknown. In particular, it has been unclear whether fixational drift arises from peripheral sources, or from central sources within the brain. Here we show that fixational drift is correlated with neural activity, and identify its origin in central neural circuitry within the oculomotor system. We analyzed a large data set of ocular motoneuron (OMN) recordings in the rhesus monkey, alongside precise measurements of eye position [2, 3], and found that most of the variance of fixational eye drifts must arise upstream of the OMNs. The diffusive statistics of the motion points to the oculomotor integrator, a memory circuit responsible for holding the eyes still between saccades, as a likely source of the motion. Theoretical modeling, constrained by the parameters of the primate oculomotor system, supports this hypothesis by accounting for the amplitude as well as the statistics of the motion. Thus, we propose that fixational ocular drift provides a direct observation of diffusive dynamics in a neural circuit responsible for storage of continuous parameter memory in persistent neural activity. The identification of a mechanistic origin for fixational drift is likely to advance the understanding of its role in visual processing and inference.

neuroscience↗

bioRxiv2020

A theory of joint attractor dynamics in the hippocampus and the entorhinal cortex accounts for artificial hippocampal remapping and individual grid cell field-to-field variability

The representation of position in the brain is distributed across multiple neural populations. Grid cell modules in the medial entorhinal cortex (MEC) express activity patterns that span a low-dimensional manifold which remains stable across different environments. In contrast, the activity patterns of hippocampal place cells span distinct low-dimensional manifolds in different environments. It is unknown how these multiple representations of position are coordinated. Here we develop a theory of joint attractor dynamics in the hippocampus and the MEC. We show that the system exhibits a coordinated, joint representation of position across multiple environments, consistent with global remapping in place cells and grid cells. We then show that our model accounts for recent experimental observations that lack a mechanistic explanation: variability in the firing rate of single grid cells across firing fields, and artificial remapping of place cells under depolarization, but not under hyperpolarization, of layer II stellate cells of the MEC.

neuroscience↗

bioRxiv2019

Velocity coupling of grid cell modules: stable embedding of a low dimensional variable in a high dimensional neural attractor

Grid cells in the medial entorhinal cortex (mEC) encode position using a distributed representation across multiple neural populations (modules), each possessing a distinct spatial scale. The modular structure of the representation confers the grid cell neural code with large capacity. Yet, the modularity poses significant challenges for the neural circuitry that maintains the representation, and updates it based on self motion. Small incompatible drifts in different modules, driven by noise, can rapidly lead to large, abrupt shifts in the represented position, resulting in catastrophic readout errors. Here we propose a theoretical model of coupled modules. The coupling suppresses incompatible drifts, allowing for a stable embedding of a two dimensional variable (position) in a higher dimensional neural attractor, while preserving the large capacity. We propose that coupling of this type may be implemented by recurrent synaptic connectivity within the mEC with a relatively simple and biologically plausible structure.

neuroscience↗