61 papers
This paper introduces a genetic programming-based method for the symbolic discovery of stochastic differential equations that jointly optimizes drift and diffusion functions via maximum likelihood estimation, enabling the accurate, scalable, and interpretable modeling of noisy dynamical systems.
This paper proposes a deep learning framework that jointly discovers optimal coordinates and flow maps to enable precise, computationally efficient time-stepping for multiscale systems, achieving state-of-the-art predictive accuracy with reduced costs on complex models like the Fitzhugh-Nagumo neuron and Kuramoto-Sivashinsky equations.
This paper introduces Multimodal Large Language Model-assisted Evolutionary Search (MLES), a novel framework that combines multimodal LLMs with evolutionary search and visual feedback to automatically generate transparent, verifiable, and human-aligned programmatic control policies that match the performance of deep reinforcement learning methods like PPO.
This paper presents a physics-informed neural network (PINN) framework that robustly reconstructs hidden state variables and estimates biophysical parameters in multiscale neuronal models using only partial, noisy voltage observations, effectively overcoming the convergence failures and sensitivity issues common in traditional numerical methods.
This paper introduces Hebbian-Oscillatory Co-Learning (HOC-L), a unified two-timescale framework that integrates hyperbolic sparse geometry with Kuramoto-based phase synchronization to enable connectivity consolidation only when phase coherence signals meaningful patterns, thereby achieving provable convergence and complexity in bio-inspired neural architectures.
This paper demonstrates that synergistically integrating Supervised Contrastive Learning, Hopfield networks, and Hierarchical Gated Recurrent Networks into Spiking Neural Networks achieves optimal neuromorphic vision performance on N-MNIST by balancing accuracy, energy efficiency, and structured neuronal clustering, rather than relying on isolated architectural optimizations.
This paper proposes an exact, parameterization-aware flatness measure tailored to the geometric structure of convolutional neural networks with global average pooling, demonstrating its effectiveness as a robust proxy for estimating and comparing generalization performance across various CNN architectures.
The paper proposes B-DENSE, a novel distillation framework that leverages multi-branch trajectory alignment to enforce dense intermediate supervision, thereby overcoming the structural information loss and discretization errors of existing methods to achieve superior image generation quality with reduced inference latency.
This paper proposes an energy-aware spike budgeting framework that integrates experience replay, learnable neuron parameters, and an adaptive scheduler to effectively mitigate catastrophic forgetting while optimizing both accuracy and energy efficiency in Spiking Neural Networks across diverse frame-based and event-based neuromorphic vision benchmarks.
This paper proposes a biologically plausible, local learning framework for time-continuous neuronal networks that approximates backpropagation through time by deriving real-time error dynamics from a prospective energy function, thereby unifying and extending the Generalized Latent Equilibrium model to enable spatiotemporal credit assignment consistent with brain circuitry.
This paper introduces DendroNN, a novel dendrocentric neural network that leverages non-differentiable sequence detection and a rewiring phase to efficiently classify event-based spatiotemporal data, achieving competitive accuracy with up to 4x higher energy efficiency than state-of-the-art neuromorphic hardware through a dedicated asynchronous digital architecture.
This paper presents a 1.6-fJ/spike subthreshold analog Leaky Integrate-and-Fire neuron fabricated in 28 nm CMOS, which achieves a 300 kHz spiking frequency at 250 mV and demonstrates 82.5% accuracy on the MNIST dataset when used in a quantized Spiking Neural Network, validating its potential for energy-efficient embedded machine learning.
This survey reviews the application of utility theory to cognitive modeling in robotics, tracing its evolution from behavior-based approaches to value systems that guide decision-making, learning, and cooperation in single and multi-agent environments, while identifying current limitations and proposing future research directions.
This report from the ALICE 2026 workshop details the SymBa group's efforts to revive and extend Nils Aall Barricelli's 1953 research on symbiogenesis by replicating his 1D numerical organisms, developing 2D extensions and DNA-norm experiments, and exploring the implications of symbiogenesis for the origins of life, open-ended evolution, and collective intelligence in artificial systems.
This paper proposes a rank-order based neural network inspired by the STG-LIFG-PMC pathway that demonstrates how rank-order coding effectively bridges acoustic input to abstract structure, enabling robust sequence reconstruction, global novelty detection, and proto-syntactic generalization through context-sensitive sensorimotor representations.
This paper addresses the multi-objective chance-constrained multiple-choice knapsack problem with implicit probability distributions by proposing an efficient order-preserving Monte Carlo evaluation method and a hybrid evolutionary algorithm (NHILS) that outperforms state-of-the-art optimizers in solving real-world 5G network configuration challenges.
This paper introduces two novel model-driven evolutionary frameworks, NEMO-DE and NEEF-DE, that leverage differential evolution to perform near-field multi-source localization on continuous spherical-wave models with arbitrary array geometries, effectively overcoming the limitations of traditional grid-based subspace methods and data-dependent deep learning approaches without requiring labeled data or discretized grids.
This paper proposes a self-supervised evolutionary learning framework that extracts individualized neurodynamic progressions and identity manifolds from unlabeled EEG data during safety-critical decision-making, enabling robust user authentication, anomaly detection, and improved generalization without relying on external labels or predefined cognitive models.
This paper introduces a closed-loop, large-language-model-driven evolutionary framework that automatically reconstructs all seven key components of Adaptive Large Neighborhood Search (ALNS), achieving superior performance on TSPLIB benchmarks and revealing novel design patterns through a MAP-Elites guided process.
This paper introduces a two-time scale neurodynamic duplex approach utilizing projection equations to solve distributionally robust geometric joint chance-constrained optimization problems with unknown distributions, demonstrating convergence to the global optimum through neural networks in applications such as shape optimization and telecommunications.