Biology subjects

Balasubramanian, V.

Publications and source records attributed to Balasubramanian, V..

At least 19 recordsLinked to original sources

bioRxiv2026

An Information Compass for Probabilistic Reasoning in the Brain

Probabilistic reasoning in the brain has been studied primarily using simple two-choice tasks that are experimentally and theoretically tractable but have unclear relevance to more complex problems. Here we generalize principles from these studies using information geometry, which treats probability distributions (``beliefs'') as points on statistical manifolds whose geometry captures relationships between those beliefs. We introduce an ``information compass,'' a context-dependent, one-dimensional axis along which an arbitrarily complex belief is reinforced or repudiated. Projecting high-dimensional belief updates onto this axis casts even complex decisions into familiar forms of deliberation and commitment used for simpler decisions. Our framework provides a unified account of how biological and artificial systems can use geometric forms of probabilistic reasoning to make decisions across a range of tasks and domains.

neuroscience↗

bioRxiv2026

Suboptimal human inference reflects an efficient and flexible information bottleneck

Human inference is often suboptimal in ways that vary across individuals and tasks. We propose that at least some of this variability reflects information-processing limits. To test this idea, we developed a task-general application of the information-bottleneck framework to quantify how much information individuals use (information capacity) and how effectively they use it (information efficiency). We applied this framework to human choice behavior on two distinct inference tasks and found that individual differences in performance were driven largely by variation in information capacity, while information efficiency remained consistently high. This pattern of variable capacity and high efficiency also applied to a subset of participants who appeared inefficient overall, but were efficient when evaluated relative to a simplified (heuristic) encoding of the observations. Regardless of whether participants based their strategies on full or simplified encodings, they tended to adjust their performance to different task demands by changing their information capacity while maintaining efficiency. We interpret these findings in terms of new analytical results linking optimal inference under information-capacity constraints to evidence-based choice noise, suggesting that a key driver of human behavioral variability is rational adaptation to flexible information-processing limits.

neuroscience↗

bioRxiv2026

A simple model of the co-emergence of grid and place fields

Grid cells in the medial entorhinal cortex and place cells in the hippocampus together support spatial navigation. The two regions are reciprocally connected, and there is a chicken-and-egg problem for how both arise and reinforce each other during development. Current computational accounts either derive one type from the other or use network dynamics to model the emergence of one type in isolation. We introduce a unified recurrent network model that instantiates Dales Law (every neuron is either excitatory or inhibitory), and is trained to predict the next sensory observation from masked previous sensory observations and egocentric motion. To our knowledge, this is the first single-objective model in which grid and place cells co-emerge without supervision of either type, or reliance on pre-existing spatial-cell representations. The two kinds of spatial codes coexist across 1,000 different training configurations, with their balance set by the amount of sensory noise and masking. Without retraining, the network qualitatively reproduces experimentally observed grid fragmentation in hairpin mazes, grid merging after wall removal, lattice alignment across connected rooms, locally ordered 3D fields observed in freely flying bats, as well as the developmental order in which place cells precede grid cells. We interpret these results in terms of two complementary encoding pressures within a single sensory-prediction objective: (1) correcting errors or reconstructing missing components of sensory observations, and (2) prediction of the next sensory state during navigation. Our results suggest a circuit-level account of the co-emergence of grid and place cells, and experimentally testable predictions for the two kinds of spatial codes. Codes for experiments are available at 1,2.

neuroscience↗

bioRxiv2026

Spatial remapping in the subicular complex and entorhinal cortex follows 1 low-dimensional geometric principles

Neurons in the hippocampal formation are part of the brains cognitive map, representing the spatial structure of the environment through coordinated activity across place cells, grid cells, border cells, head-direction cells and others1-5. Although remapping between environments has been extensively documented6-9, it remains unknown whether transitions between maps reflect unconstrained reorganization or obey a systematic transformation principle. To address this question, we recorded large neuronal populations from the subicular complex and entorhinal cortex in awake, behaving mice navigating environments spanning a wide range of geometries. We asked whether spatial representations across rooms could be related through a shared class of coordinate transformations. Despite pronounced heterogeneity and apparent randomness in single-cell remapping, population-level decoding across environments demonstrated a consistent low-dimensional affine transformation of coordinates, comprising rotation, scaling, shear, reflection, and translation. Thus, what appears as complex remapping at the level of individual neurons reduces to a compact geometric rule at the level of neural assemblies. These results indicate that the hippocampal formation maintains a structured internal coordinate template that is flexibly tailored to environmental geometry. This may serve as the organisms internal model of space.

neuroscience↗

bioRxiv2026

When and Where: A Model Hippocampal Network Unifies Formation of Time Cells and Place Cells

Hippocampal place and time cells encode spatial and temporal aspects of experience. Both have the same neural substrate, but have been modeled as having different functions and mechanistic origins, place cells as continuous attractors, and time cells as leaky integrators. Here, we show that both types emerge from two dynamical regimes of a single recurrent network (RNN) modeling hippocampal CA3 as a predictive autoencoder. The network receives simulated, partially occluded "experience vectors" containing spatial patterns (location-specific activity sampled during environmental traversal) and/or temporal patterns (correlated activity pairs separated by "void" intervals), and is trained to reconstruct missing input. During spatial navigation, the network generates stable attractor-like place fields. But trained on temporally structured inputs, the network produces sequentially broad-ened fields, recapitulating time cells. By varying spatio-temporal input patterning, we observe hidden units transition smoothly between time cell-like and place cell-like representations. These results suggest a shared origin, but task-driven difference, between place and time cells.

neuroscience↗

bioRxiv2025

Patterns of Ocular Dominance are in the Eye of the Beholder

Ocular Dominance Columns (ODCs), structures in the early visual cortex that demarcate inputs from each eye, have been observed in a wide range of mammals. The extent and arrangement of these columnar structures can vary drastically from species to species, and even along the visual cortex of a single individual. While previous studies show that these structures can form by competitive Hebbian learning, the relationship between system parameters and the resulting organizational patterns remains unclear. Here, we use a mesoscopic model of V1 development to explain how the different observed patterns of ocular dominance (columns, islands, and monocular) can arise through local Hebbian competition, as a function of the density of projections from each eye and cortical interaction strengths. We argue that our results explain the broad cortical organizational differences between species which have forward-facing versus steeply angled eyes. We additionally show that spatially varying retinal input projections, which can arise from a combination of retinotopy and varying cell density, can lead to coexistence of multiple ocular dominance patterns along the cortical sheet, consistent with experimental observations.

biophysics↗

bioRxiv2025

REMI: Reconstructing Episodic Memory During Intrinsic Path Planning

Grid cells in the medial entorhinal cortex (MEC) and place cells in the hippocampus (HC) both form spatial representations. Grid cells fire in triangular grid patterns, while place cells fire at specific locations and respond to contextual cues. How do these interacting systems support not only spatial encoding but also internally driven path planning, such as navigating to locations recalled from cues? Here, we propose a system-level theory of MEC-HC wiring that explains how grid and place cell patterns could be connected to enable cue-triggered goal retrieval, path planning, and reconstruction of sensory experience along planned routes. We suggest that place cells autoassociate sensory inputs with grid cell patterns, allowing sensory cues to trigger recall of goal-location grid patterns. We show analytically that grid-based planning permits shortcuts through unvisited locations and generalizes local transitions to long-range paths. During planning, intermediate grid states trigger place cell pattern completion, reconstructing sensory experiences along the route. Using a single-layer RNN modeling the HC-MEC loop with a planning subnetwork, we demonstrate these effects in both biologically grounded navigation simulations using RatatouGym and visually realistic navigation tasks using Habitat Sim. Codes for experiments, simulations, and vision encoder are available at 1,2,3.

neuroscience↗

bioRxiv2025

Predisposed and learned preferences for multipoint visual statistics in visually naïve newly hatched chicks

Significance statementWe show that visually naive chicks spontaneously prefer specific multipoint correlation patterns, mirroring preferences seen in humans and rats, and reflecting the most informative structures in natural scenes. This provides evidence that efficient coding mechanisms may be innately driven by evolutionary predispositions. Notably, early visual experience through imprinting can alter these preferences, highlighting a role for learning in shaping visual processing. Recent studies have revealed that human and non-human animals (rats) can detect luminance distribution and correlations between pixels in an image (ranging from 2-point to 4-point). This sensitivity is believed to stem from optimization processes in the visual system that operate through efficient coding mechanisms to extract the most informative image statistics from the environment. However, it is yet to be determined whether this optimization is evolutionarily given by inborn mechanisms or shaped by visual experience. Here we report that newly-hatched visually naive domestic chicks spontaneously prefer to approach luminance, 2-point and 4-point correlation patterns (respectively, horizontal lines and rectangular patterns), while showing no preference for 3-point correlation over white noise controls. This parallels the ranking observed in adult humans and rats, thus suggesting that evolutionarily given biological predispositions largely drive efficient coding of natural images. We also found that learning by exposure to visual stimuli, as occurs naturally during visual imprinting, induced a preference for white noise over point correlation patterns in chicks exposed to 3- and 4-point patterns. We hypothesize that this behavior could reflect chicks preference for stimuli of lower statistical complexity.

animal behavior and cognition↗

bioRxiv2024

Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes

Many animals learn cognitive maps of their environment - a simultaneous representation of context, experience, and position. Place cells in the hippocampus, named for their explicit encoding of position, are believed to be a neural substrate of these maps, with place cell "remapping" explaining how this system can represent different contexts. Briefly, place cells alter their firing properties, or "remap", in response to changes in experiential or sensory cues. Substantial sensory changes, produced, e.g., by moving between environments, cause large subpopulations of place cells to change their tuning entirely. While many studies have looked at the physiological basis of remapping, we lack explicit calculations of how the contextual capacity of the place cell system changes as a function of place field firing properties. Here, we propose a geometric approach to understanding population level activity of place cells. Using known firing field statistics, we investigate how changes to place cell firing properties affect the distances between representations of different environments within firing rate space. Using this approach, we find that the number of contexts storable by the hippocampus grows exponentially with the number of place cells, and calculate this exponent for environments of different sizes. We identify a fundamental trade-off between high resolution encoding of position and the number of storable contexts. This trade-off is tuned by place cell width, which might explain the change in firing field scale along the dorsal-ventral axis of the hippocampus. We demonstrate that clustering of place cells near likely points of confusion, such as boundaries, increases the contextual capacity of the place system within our framework and conclude by discussing how our geometric approach could be extended to include other cell types and abstract spaces.

neuroscience↗

bioRxiv2024

Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences

The vertebrate hippocampus is believed to use recurrent connectivity in area CA3 to support episodic memory recall from partial cues. This brain area also contains place cells, whose location-selective firing fields implement maps supporting spatial memory. Here we show that place cells emerge in networks trained to remember temporally continuous sensory episodes. We model CA3 as a recurrent autoencoder that recalls and reconstructs sensory experiences from noisy and partially occluded observations by agents traversing simulated arenas. The agents move in realistic trajectories modeled from rodents and environments are modeled as continuously varying, high-dimensional, sensory experience maps (spatially smoothed Gaussian random fields). Training our autoencoder to accurately pattern-complete and reconstruct sensory experiences with a constraint on total activity causes spatially localized firing fields, i.e., place cells, to emerge in the encoding layer. The emergent place fields reproduce key aspects of hippocampal phenomenology: a) remapping (maintenance of and reversion to distinct learned maps in different environments), implemented via repositioning of experience manifolds in the networks hidden layer, b) orthogonality of spatial representations in different arenas, c) robust place field emergence in differently shaped rooms, with single units showing multiple place fields in large or complex spaces, and d) slow representational drift of place fields. We argue that these results arise because continuous traversal of space makes sensory experience temporally continuous. We make testable predictions: a) rapidly changing sensory context will disrupt place fields, b) place fields will form even if recurrent connections are blocked, but reversion to previously learned representations upon remapping will be abolished, c) the dimension of temporally smooth experience sets the dimensionality of place fields, including during virtual navigation of abstract spaces. Code for our experiments is available at1.

neuroscience↗

bioRxiv2024

Slow and steady: auditory features for discriminating animal vocalizations

We propose that listeners can use temporal regularities - spectro-temporal correlations that change smoothly over time - to discriminate animal vocalizations within and between species. To test this idea, we used Slow Feature Analysis (SFA) to find the most temporally regular components of vocalizations from birds (blue jay, house finch, American yellow warbler, and great blue heron), humans (English speakers), and rhesus macaques. We projected vocalizations into the learned feature space and tested intra-class (same speaker/species) and inter-class (different speakers/species) auditory discrimination by a trained classifier. We found that: 1) Vocalization discrimination was excellent (> 95%) in all cases; 2) Performance depended primarily on the [~]10 most temporally regular features; 3) Most vocalizations are dominated by [~]10 features with high temporal regularity; and 4) These regular features are highly correlated with the most predictable components of animal sounds.

neuroscience↗

bioRxiv2023

The Physical Effects of Learning

Interacting many-body physical systems ranging from neural networks in the brain to folding proteins to self-modifying electrical circuits can learn to perform specific tasks. This learning, both in nature and in engineered systems, can occur through evolutionary selection or through dynamical rules that drive active learning from experience. Here, we show that learning leaves architectural imprints on the Hessian of a physical system. Compared to a generic organization of the system components, (a) the effective physical dimension of the response to inputs (the participation ratio of low-eigenvalue modes) decreases, (b) the response of physical degrees of freedom to random perturbations (or system "susceptibility") increases, and (c) the low-eigenvalue eigenvectors of the Hessian align with the task. Overall, these effects suggest a method for discovering the task that a physical network may have been trained for.

biophysics↗

bioRxiv2023

Playing it safe: information constrains collective betting strategies

Every interaction of a living organism with its environment involves the placement of a bet. Armed with partial knowledge about a stochastic world, the organism must decide its next step or near-term strategy, an act that implicitly or explicitly involves the assumption of a model of the world. Better information about environmental statistics can improve the bet quality, but in practice resources for information gathering are always limited. We argue that theories of optimal inference dictate that "complex" models are harder to infer with bounded information and lead to larger prediction errors. Thus, we propose a principle of playing it safe where, given finite information gathering capacity, biological systems should be biased towards simpler models of the world, and thereby to less risky betting strategies. In the framework of Bayesian inference, we show that there is an optimally safe adaptation strategy determined by the Bayesian prior. We then demonstrate that, in the context of stochastic phenotypic switching by bacteria, implementation of our principle of "playing it safe" increases fitness (population growth rate) of the bacterial collective. We suggest that the principle applies broadly to problems of adaptation, learning and evolution, and illuminates the types of environments in which organisms are able to thrive.

biophysics↗

bioRxiv2023

How Occam's razor guides human decision-making

Occam's razor is the principle that, all else being equal, simpler explanations should be preferred over more complex ones. This principle is thought to guide human decision-making, but the nature of this guidance is not known. Here we used preregistered behavioral experiments to show that people tend to prefer the simpler of two alternative explanations for uncertain data. These preferences align quantitatively with predictions of formal theories of model selection that penalize excessive flexibility. These penalties emerge when considering not just the best explanation but the integral over all possible, relevant explanations. We further show that these simplicity preferences persist in humans, but not in certain artificial neural networks, even when they are maladaptive. Our results imply that principled notions of statistical model selection, including integrating over possible, latent causes to avoid overfitting to noisy data, may play a central role in human decision-making.

neuroscience↗

bioRxiv2022

Time as a supervisor: temporal regularity and auditory object learning

Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or "objects", that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learning features of a stimulus that are temporally regular. We will show that this procedure generates a feature space sufficient to support fundamental computations of auditory perception. In detail, we consider the problem of discriminating between instances of a prototypical class of natural auditory objects, i.e., rhesus macaque vocalizations. We test discrimination in two ethologically relevant tasks: discrimination in a cluttered acoustic background and generalization to discriminate between novel exemplars. We show that an algorithm that learns these temporally regular features affords better or equivalent discrimination and generalization than conventional feature-selection algorithms, i.e., principal component analysis and independent component analysis. Our findings suggest that the slow temporal features of auditory stimuli may be sufficient for parsing auditory scenes and that the auditory brain could utilize these slowly changing temporal features.

neuroscience↗

bioRxiv2021

Connectivity and dynamics in the olfactory bulb

Dendrodendritic interactions between excitatory mitral cells and inhibitory granule cells in the olfactory bulb create a dense interaction network, reorganizing sensory representations of odors and, consequently, perception. Large-scale computational models are needed for revealing how the collective behavior of this network emerges from its global architecture. We propose an approach where we summarize anatomical information through dendritic geometry and density distributions which we use to calculate the probability of synapse between mitral and granule cells, while capturing activity patterns of each cell type in the neural dynamical systems theory of Izhikevich. In this way, we generate an efficient, anatomically and physiologically realistic large-scale model of the olfactory bulb network. Our model reproduces known connectivity between sister vs. non-sister mitral cells; measured patterns of lateral inhibition; and theta, beta, and gamma oscillations. It in turn predicts testable relations between network structure, lateral inhibition, and odor pattern decorrelation; between the density of granule cell activity and LFP oscillation frequency; how cortical feedback to granule cells affects mitral cell activity; and how cortical feedback to mitral cells is modulated by the network embedding. Additionally, the methodology we describe here provides a tractable tool for other researchers. Author summaryThe function of the olfactory bulb (OB) critically depends on connectivity patterns between its excitatory and inhibitory cells. Here, we develop an anatomically grounded algorithm for efficiently determining the probability of synapses between mitral cells and granule cells in the OB. We use this algorithm to generate a large-scale network model of the OB with characteristic connectivity distributions between cell types, as well as between sister mitral cells. We simulate the network using the dynamical systems approach of Izhikevich for describing neurons, and show how network structure affects GC-mediated processes, including LFP oscillation frequency, lateral inhibition, odor decorrelation, and cortical feedback. Our results suggest how alterations to the OB network through processes like neurogenesis, or via injury or disease, can have significant effects on function.

neuroscience↗

bioRxiv2021

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al. 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from 2- to 4-point correlations and a further decrease from 4- to 3-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

neuroscience↗

bioRxiv2021

A scaling law in CRISPR repertoire sizes arises from avoidance of autoimmunity

Some prokaryotes possess CRISPR-Cas systems that provide adaptive immunity to viruses guided by DNA segments called spacers acquired from invading phage. However, the patchy incidence and limited memory breadth of CRISPR-Cas systems suggest that their fitness benefits are offset by costs. Here, we propose that cross-reactive CRISPR targeting can lead to heterologous autoimmunity, whereby foreign spacers guide self-targeting in a spacer-length dependent fashion. Balancing antiviral defense against autoimmunity predicts a scaling relation between spacer length and CRISPR repertoire size. We find evidence for this scaling through comparative analysis of sequenced prokaryotic genomes, and show that this association also holds at the level of CRISPR types. In contrast, the scaling is absent in strains with nonfunctional CRISPR loci. Finally, we demonstrate that stochastic spacer loss can explain variations around the scaling relation, even between strains of the same species. Our results suggest that heterologous autoimmunity is a selective factor shaping the evolution of CRISPR-Cas systems.

biophysics↗