arXiv AI

Creating Intelligence: A Computational Foundation for AGI

arXiv:2606. 31819v1 Announce Type: new Abstract: This work introduces a new computational theory of mind grounded in set theory and hyperdimensional computing.

arXiv AI

Jun 8

A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders

arXiv:2606. 07007v1 Announce Type: cross Abstract: We propose a unified mathematical framework for a geometric understanding of concept learning and neuron interpretation in sparse autoencoders (SAEs).

By Chenhao Zhang, Chris Lin, Su-In Lee

efficiency safety

arXiv AI

Aug 3

Information Processing by Neuron Populations in the Central Nervous System: A Theory of the Mathematical Structure of Data and Operations

arXiv:2309. 02332v3 Announce Type: replace-cross Abstract: In the mammalian central nervous system, neurons are organized into populations communicating by spike trains propagating along axonal bundles.

By Martin N. P. Nilsson

rag

arXiv AI

Jun 16

No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks

arXiv:2405. 02369v2 Announce Type: replace-cross Abstract: In the past decade, many successful networks are on novel architectures, which almost exclusively use the same type of neurons.

By Feng-Lei Fan, Meng Wang, Hang-Cheng Dong, Jianwei Ma, Tieyong Zeng

benchmarks safety

arXiv AI

Jul 23

The Giant Hippocampus: From Structural Monoculture to a System of Systems

arXiv:2607. 19973v1 Announce Type: new Abstract: AI researchers describe state-of-the-art models as one thing repeated at scale: the Transformer, wired identically for text, pixels, or speech.

By Jaeho Seol

llms computer-vision benchmarks safety

arXiv AI

Aug 11

SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks

arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.

By Prasanna Date, Kevin Zhu, Shruti Kulkarni, Ashish Gautam, Chathika Gunaratne, Robert Patton, Tyler Nitzsche, Ian Mulet, Zachary Johnson-Scott, Addison Helms, Duncan Rowden, Simon Weston, Maryam Parsa, Catherine Schuman, Thomas Potok

benchmarks

arXiv AI

Jul 15

Enabling Energy-Efficient Simultaneous Multi-Task Reinforcement Learning through Spiking Neural Networks with Active Dendrites for Bio-inspired Generalist Agents

arXiv:2412. 04847v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has demonstrated remarkable capabilities in training agents to solve complex tasks autonomously, such as mobile robots, UAVs/UGVs, and game-playing agents).

By Rachmad Vidya Wicaksana Putra, Avaneesh Devkota, Muhammad Shafique

agents reinforcement-learning robotics benchmarks

arXiv AI

Jun 16

Harnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks

arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.

By Mo Shakiba, Rana Rokni, Mohammad Mohammadi, Nima Dehghani

rag

arXiv Machine Learning

Jul 14

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks

arXiv:2511. 02584v2 Announce Type: replace-cross Abstract: Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues.

By Mark Bl\"umel, Andreas C. Schneider, Valentin Neuhaus, David A. Ehrlich, Marcel Graetz, Michael Wibral, Abdullah Makkeh, Viola Priesemann

benchmarks

arXiv AI

3d ago

NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence

arXiv:2604.18637v3 Announce Type: replace-cross Abstract: Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on...

By Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski, Gina Adam, James B Aimone, Akwasi Akwaboah, Yiannis Aloimonos, Carmen Amo Alonso, Chiara Bartolozzi, Michael J. Bennington, Michael Berry, Bing W. Brunton, Gert Cauwenberghs, Hillel J. Chiel, Tobi Delbruck, John Doyle, Jason Eshraghian, Ralph Etienne-Cummings, Cornelia Fermuller, Matthew Jacobsen, Ali A. Minai, Barbara Oakley, Alexander G. Ororbia II, Joe Paton, Blake Richards, Yulia Sandamirskaya, Abhronil Sengupta, Shihab Shamma, Michael P. Stryker, Seong Jong Yoo, Steven W. Zucker

arXiv Machine Learning

Jun 5

Vision Hopfield Memory Networks

arXiv:2603. 25157v2 Announce Type: replace Abstract: Recent vision and multimodal foundation backbones, such as Transformer families and state-space models like Mamba, have achieved remarkable progress, enabling unified modeling across images, text, and beyond.

By Jianfeng Wang, Amine M'Charrak, Luk Koska, Xiangtao Wang, Daniel Petriceanu, Ruizhi Wang, Michael Bumbar, Luca Pinchetti, Thomas Lukasiewicz

llms computer-vision multimodal benchmarks safety

arXiv Machine Learning

Aug 31

Biologically Inspired Mechanisms for Facilitating Grokking in Multilayer Perceptrons

arXiv:2608.28184v1 Announce Type: new Abstract: Grokking is a delayed transition from memorization to generalization that is often accompanied by substantial reorganization of internal representation...

By Florin Leon

llms benchmarks

arXiv Machine Learning

Aug 10

Mathematical Principles and Experimental Discoveries of the Emergence of Symbolic Patterns in Artificial Neural Networks

arXiv:2608. 06839v1 Announce Type: new Abstract: Artificial Neural networks (ANNs) are often treated as black-box models, making explainability a central challenge in deep learning.

By Quanshi Zhang, Qihan Ren, Siyu Lou

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