Biology subjects

Fiete, I.

Publications and source records attributed to Fiete, I..

At least 19 recordsLinked to original sources

bioRxiv2026

A metric for comparing complex systems by their dynamics

Comparisons are fundamental to science: experiment against model, one organism against another, a system against itself across time. Because many systems, from brains to climate, are characterized by how they evolve in time, it is a natural goal to compare their dynamics. Dynamical systems comparison is well defined, but has been intractable for nonlinear, high-dimensional, noisy, and partially observed data. As a result, standard comparison methods have focused on the geometry or topology of data. Here we present Dynamical Similarity Analysis (DSA), a class of metrics to compare systems by their temporal evolution. Its foundation is Koopman Operator theory, which recasts nonlinear systems as linear operators. We estimate these operators from data, then compare the operators across systems. The computation is fast, scalable, and robust to noise and partial observation. It is also differentiable. DSA identifies dynamical structure that geometric and topological methods miss. It matches recordings from the head direction circuit to ring attractor models. It shows that macaque motor cortex dynamics for two reaching tasks drift apart across years despite preserved behavior, and that primary motor cortex breaks from premotor cortex as movement begins. As an optimization objective, it induces neural networks to learn never-before hypothesized solutions that run counter to their inductive biases. Thus, DSA transforms the dynamics of a system into an object that can be measured, compared, and optimized.

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bioRxiv2026

Dopamine signatures of excessive and compulsive cocaine and fentanyl use

Excessive and compulsive drug use despite adverse consequences is a hallmark of substance use disorder, yet individuals differ markedly in their vulnerability to develop these behaviors. Drugs of abuse are long known to alter endogenous dopamine (DA) signaling, but shared principles for how DA dynamics impact compulsive use among individuals and across drug classes are lacking. Here, we monitored DA release in the medial shell of nucleus accumbens (NAc) during cocaine and fentanyl self-administration, with or without coincident punishment, in large cohorts of mice. Contingent cocaine and fentanyl self-administration evoked complex and individually distinct DA dynamics; nevertheless, a robust negative correlation held across both drugs, such that high takers exhibited lower drug-evoked DA signals. During punished drug taking, cocaine and fentanyl cases were associated with distinct DA signatures of compulsivity. For cocaine, punishment-resistant mice showed lower sustained DA responses during the post-shock, drug-associated cue period, whereas for fentanyl, punishment-resistant mice displayed larger phasic DA at the co-occurrence of footshock and drug infusion. To identify common principles underlying these observations, we developed a computational model grounded in an Actor-Critic temporal-difference (TD) learning framework that incorporates internal states, agents uncertainty, and drug-specific effects. Remarkably, this model captures the observed diversity in DA dynamics across drug classes and among mice with variable drug taking propensities, hereby providing a unified interpretation of NAc DA signals as encoding TD reward prediction errors.

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bioRxiv2025

How learned expectations shape brain-wide responses

Expectations stemming from prior knowledge have long been known to influence stimulus processing and decision-making. Empowered by brain-wide recordings during a two-choice sensory decision-making task from the International Brain Laboratory1, we sought to identify the form of expectation-based modulations and how they influence ongoing computations. To discern where, when, and how prior knowledge about the stimulus - known to be stored in the brain over the inter-trial period2 - influences brain activity over the arc from stimulus processing to choice selection and action generation, we disentangled the encoding of the highly correlated stimulus, choice, and prior variables as sensory processors, choice/integration processors, and movement generators. We examined which brain regions were modulated by prior expectations and at which point in the task, and characterized whether those modulations take the form of biasing initial activations or response gains. We found that the activities of choice/integrators and movement generators were influenced by prior expectation, while purely stimulus processors were not significantly modulated by prior expectation. In addition, we found that expectation-based modulation takes the form of both initial activity bias and gain modulation in integration/choice computations and in movement generators. We further find that the brain-wide recordings reveal an emergent simplicity: A few-parameter mechanistic neural circuit model captures the population dynamics of stimulus response, integration/choice processing, and action initiation, as well as their modulations by the prior. After fitting to physiology, the models behavior predicts the psychometric and chronometric behavior of mice with no additional parameters. It reveals that the larger bias and gain modulations of movement initiators relative to integration/choice in the data and model is inherited by amplifying integrator/choice inputs rather than being an additional expectation-based effect. Collectively, our results characterize in fine spatiotemporal detail and brain-wide breadth the mechanisms and loci where prior expectations influence cognition, while the novel synthesis between large-scale electrophysiology and mechanistic modeling reveals an organizing simplicity behind perceptual decision making across the brain.

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bioRxiv2025

Similar destabilization of neural dynamics under different general anesthetics

Different classes of anesthetics can induce unconsciousness despite acting through distinct biological mechanisms. This raises the possibility that they produce a convergent effect on the dynamics or temporal evolution of neural population activity. To explore this, we analyzed intracortical electrophysiological recordings during infusions of propofol, ketamine, and dexmedetomidine, using a rigorous method to estimate dynamical stability. We found that all three anesthetics, despite their molecular differences, similarly affect cortical states by destabilizing their dynamics. This destabilization matched the slower recovery from sensory perturbations and longer stimulus-induced autocorrelation times observed during the anesthetic infusions. The destabilization was also reflected predominantly in lower-frequency ranges, linking it to the well-known increase in low-frequency power during anesthesia. Finally, destabilization closely tracked real-time fluctuations in consciousness. Together, these findings suggest that cortical destabilization may be a shared neural correlate of anesthetic-induced unconsciousness, offering a mechanistic explanation for low-frequency oscillations observed during anesthesia.

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bioRxiv2025

Spatially distributed and regionally unbound cellular resolution brain-wide processing loops in mice

Until recently, brain activity could be examined either globally, through regional averaging, or locally, at cellular resolution--but not both at once. As a result, regions were characterized as functionally homogeneous units while single neurons were often characterized as heterogeneously and randomly tuned. Here, we overcome this long-standing divide by leveraging the unprecedented density of International Brain Laboratory electrophysiological recordings in mice performing a sensorimotor decision-making task. Computationally integrating these recordings and ordering neurons by functional similarity reveals directly to the eye the full temporal arc of computations unfurling over the phases of a trial and across the brain at cellular and millisecond resolution, providing a dynamical map of the architecture of brain-wide computation. We report that one of the largest shares of brain-wide variance is explained not by task-related sensory responses, integration, or decision related signals, but by neurons encoding internally generated, trial-timed broadcast sequences that carry contextual information and are biased toward hippocampal and entorhinal involvement. We find that individual neurons reliably specialize in interpretable functions, yet neurons with similar functional roles are widely dispersed across regions. Each region contains cells spanning nearly the full repertoire of response types, rendering functional tuning largely unpredictable from cytoarchitectural boundaries or spatial location alone. In particular, even primary visual cortex contains a broad diversity of response types beyond stimulus-locked responses. The dominant discernible organizational distinction is instead between cortical and subcortical processing. Subcortical neurons carry the bulk of internal computations in expert mice, including integration, decision-making, and movement initiation. These patterns support a model of broadcast rather than staged computation, in which signals are widely shared and transformed rather than serially processed. Finally, we find that despite widespread regional mixing, representations at the single neuron level are not randomly mixed. Neurons combine specific features in highly correlated groupings, contradicting the hypothesis of random mixed selectivity at the cellular level. Together, our findings suggest a revised architecture of brain computation: typically interpretable and non-random selectivity at the level of single neurons, coupled to continual brain-wide broadcast rather than staged processing, in which internally structured activity dominates neural dynamics and scaffolds cognition.

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bioRxiv2025

Slow synaptic plasticity from the hippocampus underlies gradual mapping and fragmentation of novel spaces by grid cells

Animals construct internal "cognitive maps" of the world during navigation in spatial and non-spatial domains, with grid cells in the medial entorhinal cortex (MEC) playing a key role. This requires associating internal position estimates with external cues to reduce spatial uncertainty over time. However, how grid cell representations evolve in novel spaces to support map formation is unclear. To address this question, we longitudinally imaged calcium dynamics of grid cells over 10 days as mice learn operant tasks in novel virtual linear tracks. We observe that spatial tuning of grid cells is present immediately in novel tracks but evolves as a significant fraction of spatial fields shift backward on a run-by-run basis, within and across days. Backward shifts are more prevalent and persistent in successful learners. The fields gradually stabilize across days, anchored by landmarks, suggesting slow plasticity. The backward shifts partially reset daily, reflecting a slower consolidation timescale. While individual fields of a cell shift differentially, co-active fields of co-modular grid cells shift together, indicating their coupled dynamics on the same two-dimensional torus. Spatial learning leads to systematic changes and stabilization of their population phase trajectory, including lateral shift, rotation, and phase resets at landmarks, forming a landmark-fragmented representation for the environment. Next, we build an entorhinal-hippocampal model that provides a mechanistic explanation of the diverse phenomena - grid field shifts, increasing fidelity, and fragmentation of the spatial map - and predicts slow Hebbian plasticity in the hippocampus-to-entorhinal pathway. Supporting this, electrophysiology demonstrates that learning-performance-correlated weakening of local inhibition facilitates potentiation of indirect hippocampal inputs to superficial MEC. Together, our study provides multifaceted evidence of slow hippocampus-to-MEC plasticity, elucidating the formation of stable and fragmented cognitive maps that combine internal and cue-driven positional estimates in rich environments during learning. This mechanism may extend to broader memory processes involving this circuit.

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bioRxiv2025

A Multi-Region Brain Model to Elucidate the Role of Hippocampus in Spatially Embedded Decision-Making

Brains excel at robust decision-making and data-efficient learning. Understanding the architectures and dynamics underlying these capabilities can inform inductive biases for deep learning. We present a multi-region brain model that explores the normative role of structured memory circuits in a spatially embedded binary decision-making task from neuroscience. We counterfactually compare the learning performance and neural representations of reinforcement learning (RL) agents with brain models of different interaction architectures between grid and place cells in the entorhinal cortex and hippocampus, coupled with an action-selection cortical recurrent neural network. We demonstrate that a specific architecture- where grid cells receive and jointly encode self-movement velocity signals and decision evidence increments-optimizes learning efficiency while best reproducing experimental observations relative to alternative architectures. Our findings thus suggest brain-inspired structured architectures for efficient RL. Importantly, the models make novel, testable predictions about organization and information flow within the entorhinal-hippocampal-neocortical circuit: we predict that grid cells must conjunctively encode position and evidence for effective spatial decision-making, directly motivating new neurophysiological experiments.*

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bioRxiv2025

From Synapses to Dynamics: Obtaining Function from Structure in a Connectome Constrained Model of the Head Direction Circuit

How precisely does circuit wiring specify function? This fundamental question is particularly relevant for modern neuroscience, as large-scale electron microscopy now enables the reconstruction of neural circuits at single-synapse resolution across many organisms. To interpret circuit function from such datasets, we must understand the extent to which [measured] structure constrains dynamics. We investigate this question in the Drosophila head direction (HD) circuit, which maintains an internal heading estimate through attractor dynamics that integrate self-motion velocity cues. This circuit serves as a sensitive assay for functional specification: continuous attractor networks are theoretically known to require finely tuned wiring, whereas connectomes reveal that biological wiring can be variable and omit key cellular parameters such as synaptic gains, neuronal thresholds, and time constants. We introduce a method that combines self-supervised and unsupervised learning objectives to estimate unknown parameters at the level of cell types, rather than individual neurons and synapses. Given the raw connectivity matrix, our approach recovers a network that robustly exhibits continuous attractor dynamics and accurately integrates a range of velocity inputs, despite minimal parameter tuning on a connectome which notably departs from the symmetric regularity of an idealized ring attractor. We characterize how deviations from the original connectome shape the space of viable solutions. We also perform in-silico ablation experiments to probe the distinct functional roles of specific cell types in the circuit, demonstrating how connectome-derived structure, when augmented with minimal, biologically grounded tuning, can replicate known physiology and elucidate circuit function.

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bioRxiv2024

Self-organized emergence of modularity, hierarchy, and mirror reversals from competitive synaptic growth in a developmental model of the visual pathway

A hallmark of the primate visual system is its architectural organization consisting of multiple distinct (modular) areas that connect hierarchically. These areas exhibit specific spatial organization on the cortical sheet, with primary visual cortex at the center and subsequent regions in the hierarchy encircling the earlier one, and detailed topological organization, with retinotopy in each area but striking mirror reversals across area boundaries. The developmental rules that drive the simultaneous formation of these architectural, spatial, and topographic aspects of organization are unknown. Here we demonstrate that a simple synaptic growth rule driven by spontaneous activity and heterosynaptic competition generates a detailed connectome of the visual pathway, with emergence of all three types of organization. We identify a theoretical principle -- local greedy wiring minimization via spontaneous drive (GWM-S) -- implemented by the mechanism, and use this insight to propose biologically distinct growth rules that predict similar endpoints but testably distinguishable developmental trajectories. The same rules predict how input geometry and cortical geometry together drive emergence of hierarchical, convolution-like, spatially and topographically organized sensory processing pathways for different modalities and species, providing a possible explanation for the observed pluripotency of cortical structure formation. We find that the few parameters governing structure emergence in the growth rule constitute simple knobs for rich control, that could (potentially genetically) encode a projection neuron-like connectivity patterns and interneuron-like ones. In all, the presented rules provide a parsimonious mechanistic model for the organization of sensory cortical hierarchies even without detailed genetic cues for features like map reversal, and provide numerous predictions for experiment during normal and perturbed development.

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bioRxiv2023

High-capacity flexible hippocampal associative and episodic memory enabled by prestructured ''spatial'' representations

Hippocampal circuits in the brain enable two distinct cognitive functions: the construction of spatial maps for navigation and the storage of sequential episodic memories. This dual role remains an enduring enigma. While there have been advances in modeling spatial representations in the hippocampus, we lack good models of its role in episodic memory. Here we present a neocortical-entorhinal-hippocampal network model that implements a high-capacity general associative memory, spatial memory, and episodic memory by factorizing content storage from the dynamics of generating error-correcting stable states. Unlike existing neural memory models, which exhibit a memory cliff, the circuit (which we call Vector-HaSH, Vector Hippocampal Scaffolded Heteroassociative Memory) exhibits a graceful tradeoff between number of stored items and detail. Next, we show that structured internal scaffold states are essential for constructing episodic memory: they enable high-capacity sequence memorization by abstracting the chaining problem into one of learning low-dimensional transitions. Finally, we show that previously learned spatial sequences in the form of cortico-hippocampal location-landmark associations can in turn be used as larger scaffolds and associated with neocortical inputs for a high-fidelity one-shot memory, providing the first circuit model of the "memory palaces" used in the striking feats of memory athletes.

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bioRxiv2023

Propofol anesthesia destabilizes neural dynamics across cortex

Every day, hundreds of thousands of people undergo general anesthesia. One hypothesis is that anesthesia disrupts dynamic stability, the ability of the brain to balance excitability with the need to be stable and thus controllable. We tested this hypothesis using a new method for quantifying population-level dynamic stability in complex systems, Delayed Linear Analysis for Stability Estimation (DeLASE). Propofol was used to transition animals between the awake state and anesthetized unconsciousness. DeLASE was applied to macaque cortex local field potentials (LFPs). We found that neural dynamics were more unstable in unconsciousness compared to the awake state. Cortical trajectories mirrored predictions from destabilized linear systems. We mimicked the effect of propofol in simulated neural networks by increasing inhibitory tone. Paradoxically, increased inhibition also destabilized the networks. Our results suggest that anesthesia disrupts dynamical stability that is required for consciousness.

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bioRxiv2022

No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit

Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance based on deep learning. Unique to Neuroscience, deep learning models can be used not only as a tool but interpreted as models of the brain. The central claims of recent deep learning-based models of brain circuits are that they make novel predictions about neural phenomena or shed light on the fundamental functions being optimized. We show, through the case-study of grid cells in the entorhinal-hippocampal circuit, that one may get neither. We begin by reviewing the principles of grid cell mechanism and function obtained from first-principles modeling efforts, then rigorously examine the claims of deep learning models of grid cells. Using large-scale architectural and hyperparameter sweeps and theory-driven experimentation, we demonstrate that the results of such models may be more strongly driven by particular, non-fundamental, and post-hoc implementation choices than fundamental truths about neural circuits or the loss function(s) they might optimize. We discuss why these models cannot be expected to produce accurate models of the brain without the addition of substantial amounts of inductive bias, an informal No Free Lunch result for Neuroscience. Based on first principles work, we provide hypotheses for what additional loss functions will produce grid cells more robustly. In conclusion, circumspection and transparency, together with biological knowledge, are warranted in building and interpreting deep learning models in Neuroscience.

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bioRxiv2022

Primate neocortex performs balanced sensory amplification

Sensory cortex amplifies relevant features of external stimuli. This sensitivity and selectivity arise through the transformation of inputs by cortical circuitry. We characterize the circuit mechanisms and dynamics of cortical amplification by making large-scale simultaneous measurements of single cells in awake primates and by testing computational models. By comparing network activity in both driven and spontaneous states with models, we identify the circuit as operating in a regime of balanced amplification. Incoming inputs are strongly but transiently amplified by recurrent excitation. Inhibition acts to counterbalance this excitation by rapidly quenching responses, thereby permitting tracking of time-varying stimuli. One-Sentence SummarySensory cortex uses balanced excitatory and inhibitory circuitry to boost weak signals while maintaining fast sensory dynamics in a changing environment.

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bioRxiv2021

A structured scaffold underlies activity in the hippocampus

Place cells are believed to organize memory across space and time, inspiring the idea of the cognitive map. Yet unlike the structured activity in the associated grid and head-direction cells, they remain an enigma: their responses have been difficult to predict and are complex enough to be statistically well-described by a random process. Here we report one step toward the ultimate goal of understanding place cells well enough to predict their fields. Within a theoretical framework in which place fields are derived as a conjunction of external cues with internal grid cell inputs, we predict that even apparently random place cell responses should reflect the structure of their grid inputs and that this structure can be unmasked if probed in sufficiently large neural populations and large environments. To test the theory, we design experiments in long, locally featureless spaces to demonstrate that structured scaffolds undergird place cell responses. Our findings, together with other theoretical and experimental results, suggest that place cells build memories of external inputs by attaching them to a largely prespecified grid scaffold.

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bioRxiv2021

Fragmented Spatial Maps: State Abstraction and Efficient Planning from Surprisal

When animals explore spatial environments, their representations often fragment into multiple maps. What determines these map fragmentations, and can we predict where they will occur with simple principles? We pose the problem of fragmentation of an environment as one of (online) spatial clustering. Taking inspiration from the notion of a contiguous region in robotics, we develop a theory in which fragmentation decisions are driven by surprisal. When this criterion is implemented with boundary, grid, and place cells in various environments, it produces map fragmentations from the first exploration of each space. Augmented with a long-term spatial memory and a rule similar to the distance-dependent Chinese Restaurant Process for selecting among relevant memories, the theory predicts the reuse of map fragments in environments with repeating substructures. Our model provides a simple rule for generating spatial state abstractions and predicts map fragmentations observed in electrophysiological recordings. It further predicts that there should be "fragmentation decision" or "fracture" cells, which in multicompartment environments could be called "doorway" cells. Finally, we show that the resulting abstractions can lead to large (orders of magnitude) improvements in the ability to plan and navigate through complex environments.

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bioRxiv2021

Spontaneous emergence of topologically robust grid cell modules: A multiscale instability theory

Modular structure and function are ubiquitous in biology, from the organization of animal bodies and brains to the scale of ecosystems. However, the mechanisms of modularity emergence remain unclear. Here we introduce the principle of peak selection, a process by which purely local interactions and smooth gradients can result in global modular organization. It can lead to the self-organization of discontinuous module boundaries from a smooth global gradient, unifying the positional hypothesis and the Turing pattern formation hypothesis for morphogenesis. Applied to the brain's grid cell networks, peak selection results in the spontaneous emergence of functionally distinct modules with discretely spaced spatial periods. Applied to ecological systems, a generalization of the process results in discrete systems-level niches. The dynamics exhibits emergent self-scaling to variations in system size and "topological robustness" that renders module emergence and module properties insensitive to most parameters. Further, peak selection confers robustness within modules. It ameliorates the fine-tuning requirement of continuous attractor dynamics even in single grid cell modules. It makes a detail-independent prediction that grid module period ratios should approximate adjacent integer ratios, furnishing the most accurate match to data to date. Additional testable predictions promise to bridge physiology, connectomics, and transcriptomics. In sum, our results indicate that local interactions combined with low-information global gradients can drive robust global module emergence.

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bioRxiv2019

Flexible representation and memory of higher-dimensional cognitive variables with grid cells

We shed light on the theoretical capabilities of entorhinal grid cells to encode variables of dimension greater than two. Our model constructs representations of high-dimensional inputs through a combination of low-dimensional random projections and \"classical\" low-dimensional hexagonal grid cell responses. Without reconfiguration of the recurrent circuit, the same system can flexibly encode multiple variables of different dimensions while maximizing the coding range (per dimension) by automatically trading-off dimension with an exponentially large coding range. In contrast to previously proposed schemes, the model does not require the formation of higher-dimensional grid responses, a cell-inefficient and rigid mechanism. The firing fields observed in flying bats or climbing rats can be generated by neurons that combine activity from multiple grid modules, each representing higher-dimensional spaces according to our model. The idea expands our understanding of grid cells, suggesting that they could implement a general circuit that generates on-demand coding and memory states for variables in high-dimensional vector spaces.

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bioRxiv2019

The population dynamics of a canonical cognitive circuit

The brain constructs distributed representations of key low-dimensional variables. These variables may be external stimuli or internal constructs of quantities relevant for survival, such as a sense of ones location in the world. We consider that the high-dimensional population-level activity vectors are the fundamental representational currency of a neural circuit, and these vectors trace out a low-dimensional manifold whose dimension and topology matches those of the represented variable. This manifold perspective -- applied to the mammalian head direction circuit across rich waking behaviors and sleep -- enables powerful inferences about circuit representation and mechanism, including: Direct visualization and blind discovery that the network represents a one-dimensional circular variable across waking and REM sleep; fully unsupervised decoding of the coded variable; stability and attractor dynamics in the representation; the discovery of new dynamical trajectories during sleep; the limiting role of external rather than internal noise in the fidelity of memory states; and the conclusion that the circuit is set up to integrate velocity inputs according to classical continuous attractor models.

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