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Ila R. Fiete

Publications and source records attributed to Ila R. Fiete.

11 recordsLinked to original sources
arXiv2026

Mean-field theory of rich oscillatory dynamics in low-rank recurrent networks with activity-dependent adaptation

We develop a dynamical mean-field theory for random recurrent networks with low-rank structure and firing-rate-driven adaptation. When the random connectivity is strong enough to generate chaos, increasing adaptation strength drives the network through four regimes: a static coherent state, noise-sustained oscillations that progress from regular to irregular, stochastic switching between symmetric wells, and a global limit cycle. The theory identifies two instability mechanisms, chaos onset from the random connectivity and a Hopf bifurcation of the coherent mode, and shows how adaptation shapes both through the frequency-dependent single-neuron transfer function. A reduced three-dimensional model captures the bifurcation structure of the full network. Above the chaos threshold, coherent population-level oscillations coexist with heterogeneous firing rates and network-generated stochasticity at the single-neuron level. The interaction of adaptation with random and low-rank connectivity produces a rich oscillatory repertoire, including waxing-and-waning rhythmic episodes, persistent state switching, and slow Up-Down alternations, dynamics that have been observed during wakefulness, sleep, and anesthesia.

q-bio.NC↗
arXiv2026

Characterizing control between interacting subsystems with deep Jacobian estimation

Biological function arises through the dynamical interactions of multiple subsystems, including those between brain areas, within gene regulatory networks, and more. A common approach to understanding these systems is to model the dynamics of each subsystem and characterize communication between them. An alternative approach is through the lens of control theory: how the subsystems control one another. This approach involves inferring the directionality, strength, and contextual modulation of control between subsystems. However, methods for understanding subsystem control are typically linear and cannot adequately describe the rich contextual effects enabled by nonlinear complex systems. To bridge this gap, we devise a data-driven nonlinear control-theoretic framework to characterize subsystem interactions via the Jacobian of the dynamics. We address the challenge of learning Jacobians from time-series data by proposing the JacobianODE, a deep learning method that leverages properties of the Jacobian to directly estimate it for arbitrary dynamical systems from data alone. We show that JacobianODEs outperform existing Jacobian estimation methods on challenging systems, including high-dimensional chaos. Applying our approach to a multi-area recurrent neural network (RNN) trained on a working memory selection task, we show that the "sensory" area gains greater control over the "cognitive" area over learning. Furthermore, we leverage the JacobianODE to directly control the trained RNN, enabling precise manipulation of its behavior. Our work lays the foundation for a theoretically grounded and data-driven understanding of interactions among biological subsystems.

q-bio.QM↗
arXiv2025

Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review

Traditional closed peer review systems, which have played a central role in scientific publishing, are often slow, costly, non-transparent, stochastic, and possibly subject to biases - factors that can impede scientific progress and undermine public trust. Here, we propose and examine the efficacy and accuracy of an alternative form of scientific peer review: through an open, bottom-up process. First, using data from two major scientific conferences (CCN2023 and ICLR2023), we highlight how high variability of review scores and low correlation across reviewers presents a challenge for collective review. We quantify reviewer agreement with community consensus scores and use this as a reviewer quality estimator, showing that surprisingly, reviewer quality scores are not correlated with authorship quality. Instead, we reveal an inverted U-shape relationship, where authors with intermediate paper scores are the best reviewers. We assess empirical Bayesian methods to estimate paper quality based on different assessments of individual reviewer reliability. We show how under a one-shot review-then-score scenario, both in our models and on real peer review data, a Bayesian measure significantly improves paper quality assessments relative to simple averaging. We then consider an ongoing model of publishing, reviewing, and scoring, with reviewers scoring not only papers but also other reviewers. We show that user-generated reviewer ratings can yield robust and high-quality paper scoring even when unreliable (but unbiased) reviewers dominate. Finally, we outline incentive structures to recognize high-quality reviewers and encourage broader reviewing coverage of submitted papers. These findings suggest that a self-selecting open peer review process is potentially scalable, reliable, and equitable with the possibility of enhancing the speed, fairness, and transparency of the peer review process.

cs.SI↗
arXiv2024

Fault-Tolerant Neural Networks from Biological Error Correction Codes

It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error correction codes have been observed to protect states against neural spiking noise, but their role in information processing is unclear. Here, we use these biological error correction codes to develop a universal fault-tolerant neural network that achieves reliable computation if the faultiness of each neuron lies below a sharp threshold; remarkably, we find that noisy biological neurons fall below this threshold. The discovery of a phase transition from faulty to fault-tolerant neural computation suggests a mechanism for reliable computation in the cortex and opens a path towards understanding noisy analog systems relevant to artificial intelligence and neuromorphic computing.

cs.LG↗
arXiv2023

Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks

Recurrent neural networks (RNNs) trained on compositional tasks can exhibit functional modularity, in which neurons can be clustered by activity similarity and participation in shared computational subtasks. Unlike brains, these RNNs do not exhibit anatomical modularity, in which functional clustering is correlated with strong recurrent coupling and spatial localization of functional clusters. Contrasting with functional modularity, which can be ephemerally dependent on the input, anatomically modular networks form a robust substrate for solving the same subtasks in the future. To examine whether it is possible to grow brain-like anatomical modularity, we apply a recent machine learning method, brain-inspired modular training (BIMT), to a network being trained to solve a set of compositional cognitive tasks. We find that functional and anatomical clustering emerge together, such that functionally similar neurons also become spatially localized and interconnected. Moreover, compared to standard $L_1$ or no regularization settings, the model exhibits superior performance by optimally balancing task performance and network sparsity. In addition to achieving brain-like organization in RNNs, our findings also suggest that BIMT holds promise for applications in neuromorphic computing and enhancing the interpretability of neural network architectures.

q-bio.NC↗
arXiv2023

Noisy dynamical systems evolve error correcting codes and modularity

Noise is a ubiquitous feature of the physical world. As a result, the first prerequisite of life is fault tolerance: maintaining integrity of state despite external bombardment. Recent experimental advances have revealed that biological systems achieve fault tolerance by implementing mathematically intricate error-correcting codes and by organizing in a modular fashion that physically separates functionally distinct subsystems. These elaborate structures represent a vanishing volume in the massive genetic configuration space. How is it possible that the primitive process of evolution, by which all biological systems evolved, achieved such unusual results? In this work, through experiments in Boolean networks, we show that the simultaneous presence of error correction and modularity in biological systems is no coincidence. Rather, it is a typical co-occurrence in noisy dynamic systems undergoing evolution. From this, we deduce the principle of error correction enhanced evolvability: systems possessing error-correcting codes are more effectively improved by evolution than those without.

q-bio.PE↗
arXiv2022

Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold

Content-addressable memory (CAM) networks, so-called because stored items can be recalled by partial or corrupted versions of the items, exhibit near-perfect recall of a small number of information-dense patterns below capacity and a 'memory cliff' beyond, such that inserting a single additional pattern results in catastrophic loss of all stored patterns. We propose a novel CAM architecture, Memory Scaffold with Heteroassociation (MESH), that factorizes the problems of internal attractor dynamics and association with external content to generate a CAM continuum without a memory cliff: Small numbers of patterns are stored with complete information recovery matching standard CAMs, while inserting more patterns still results in partial recall of every pattern, with a graceful trade-off between pattern number and pattern richness. Motivated by the architecture of the Entorhinal-Hippocampal memory circuit in the brain, MESH is a tripartite architecture with pairwise interactions that uses a predetermined set of internally stabilized states together with heteroassociation between the internal states and arbitrary external patterns. We show analytically and experimentally that for any number of stored patterns, MESH nearly saturates the total information bound (given by the number of synapses) for CAM networks, outperforming all existing CAM models.

cs.AI↗
arXiv2022

Attractor and integrator networks in the brain

In this review, we describe the singular success of attractor neural network models in describing how the brain maintains persistent activity states for working memory, error-corrects, and integrates noisy cues. We consider the mechanisms by which simple and forgetful units can organize to collectively generate dynamics on the long time-scales required for such computations. We discuss the myriad potential uses of attractor dynamics for computation in the brain, and showcase notable examples of brain systems in which inherently low-dimensional continuous attractor dynamics have been concretely and rigorously identified. Thus, it is now possible to conclusively state that the brain constructs and uses such systems for computation. Finally, we look ahead by highlighting recent theoretical advances in understanding how the fundamental tradeoffs between robustness and capacity and between structure and flexibility can be overcome by reusing and recombining the same set of modular attractors for multiple functions, so they together produce representations that are structurally constrained and robust but exhibit high capacity and are flexible.

q-bio.NC↗
arXiv2009

Accurate path integration in continuous attractor network models of grid cells

Grid cells in the rodent entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space, and have been hypothesized to form the neural substrate for dead-reckoning. However, in previous models suggested for grid cell activity, errors accumulate rapidly in the integration of velocity inputs. To produce grid-cell like responses, these models would require frequent resets triggered by external sensory cues, casting doubt on the dead-reckoning potential of the grid cell system. Here we focus on the accuracy of path integration in continuous attractor models of grid cell activity. We show, in contrast to previous models, that continuous attractor models can generate regular triangular grid responses, based on inputs that encode only the rat's velocity and heading. We consider the role of the network boundary in integration performance, and show that both periodic and aperiodic networks are capable of accurate path integration, despite important differences in their attractor manifolds. We show that the rate at which errors accumulate in the integration of velocity depends on the network's organization and size, and on the intrinsic noise within the network. With a plausible range of parameters and the inclusion of spike variability, our model can accurately integrate velocity inputs over a maximum of ~10-100 m and ~1-10 minutes. These findings form a proof-of-concept that continuous attractor dynamics may underlie velocity integration in dMEC. The simulations also generate pertinent upper bounds on the accuracy of integration that may be achieved by continuous attractor dynamics in the grid cell network. We suggest experiments to test the continuous attractor model and differentiate it from models in which single cells establish their responses independently of each other.

q-bio.NC↗
arXiv2006

Gradient learning in spiking neural networks by dynamic perturbation of conductances

We present a method of estimating the gradient of an objective function with respect to the synaptic weights of a spiking neural network. The method works by measuring the fluctuations in the objective function in response to dynamic perturbation of the membrane conductances of the neurons. It is compatible with recurrent networks of conductance-based model neurons with dynamic synapses. The method can be interpreted as a biologically plausible synaptic learning rule, if the dynamic perturbations are generated by a special class of ``empiric'' synapses driven by random spike trains from an external source.

q-bio.NC↗