Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
Competition, stability, and functionality in excitatory-inhibitory neural circuits
Università degli Studi di Padova | University of California at San Diego | Rice University | University of California at Santa Barbara
The 30-Second View
IN SHORT: This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where traditional symmetric weight assumptions break down.
Innovation (TL;DR)
- Methodology Introduces a game-theoretic interpretation where each neuron acts as a selfish agent minimizing its own energy, with collective dynamics reaching Nash equilibria rather than global energy minima.
- Methodology Extends the proximal gradient dynamics framework to asymmetric firing rate networks, defining neuron-specific interaction costs {E_int^i(x,u_i)} and activation costs {E_act^i(x_i)}.
- Theory Bridges energy-based models with network stability theory (Lyapunov diagonal stability) to analyze regulation and balancing in excitatory-inhibitory circuits.
Key conclusions
- Asymmetric neural networks can be reformulated as noncooperative games where Nash equilibria correspond to stable network states, providing interpretability without global energy functions.
- The Wilson-Cowan model reveals that excitatory self-connection weight w_EE serves as a principal switch governing transitions between cooperative and antagonistic dynamical regimes.
- Lateral inhibition microcircuits function as contrast enhancers through hierarchical excitation-inhibition interplay, sharpening subtle environmental differences with arbitrary precision.
Abstract: Energy-based models have become a central paradigm for understanding computation and stability in both theoretical neuroscience and machine learning. However, the energetic framework typically relies on symmetry in synaptic or weight matrices - a constraint that excludes biologically realistic systems such as excitatory–inhibitory (E–I) networks. When symmetry is relaxed, the classical notion of a global energy landscape fails, leaving the dynamics of asymmetric neural systems conceptually unanchored. In this work, we extend the energetic framework to asymmetric firing rate networks, revealing an underlying game-theoretic structure for the neural dynamics in which each neuron is an agent that seeks to minimize its own energy. In addition, we exploit rigorous stability principles from network theory to study regulation and balancing of neural activity in E-I networks. We combine the novel game-energetic interpretation and the stability results to revisit standard frameworks in theoretical neuroscience, such as the Wilson-Cowan and lateral inhibition models. These insights allow us to study cortical columns of lateral inhibition microcircuits as contrast enhancer - with the ability to selectively sharpen subtle differences in the environment through hierarchical excitation–inhibition interplay. Our results bridge energetic and game-theoretic views of neural computation, offering a pathway toward the systematic engineering of biologically grounded, dynamically stable neural architectures.