arXiv Machine Learning August 14, 2026 By Abolfazl Moslemi, Milad Sarabadani, Fatemeh Sefidian, Hossein Peyvandi

A Bayes-Markov Neuromorphic Model of Cortical Orientation Selectivity: A Computational Re-implementation and Quantitative Simulation Study

Read the original on arXiv Machine Learning →

arXiv:2608. 12388v1 Announce Type: cross Abstract: The emergence of orientation selectivity in the primary visual cortex (V1) remains a central question in computational neuroscience.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 4

Recursive Gaussian Processes and the Bayesian Brain

arXiv:2608. 00503v1 Announce Type: cross Abstract: Predictive coding offers a powerful framework for cortical computation, yet scalable implementations that respect both Bayesian exactness and neurobiological constraints remain scarce.

By Moumita Das, Dipanjan Ray, Sourabh Bhattacharya

arXiv Machine Learning

Jun 26

Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration

arXiv:2606. 26168v1 Announce Type: new Abstract: Living systems navigate environments using noisy and incomplete sensory signals.

By Ruyi Tang (LCQB-AG), Gr\'egoire Sergeant-Perthuis (LCQB-AG), David Colliaux

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 3

Dendritic In-Context Learning in a Single-Layer Spiking Neural Network

arXiv:2607. 02283v1 Announce Type: cross Abstract: In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state-space models, and MLPs.

By Juwei Shen, Yujie Wu, Changwen Chen

llms benchmarks

arXiv AI

Jul 20

Learning the Brain's Dynamics as a Port-Hamiltonian System: A GNN-Surrogate Metriplectic Twin for Non-Equilibrium Cortical Dynamics and Closed-Loop Neuromodulation

arXiv:2607. 10439v2 Announce Type: replace-cross Abstract: We model human motor cortex, recorded during rest and motor-imagery BCI conditions, as a port-Hamiltonian system: a conservative interconnection (skew-symmetric coupling between band-limited neural phasors) together with a dissipative port whose state-dependent decay is set by a graph-neural-network surrogate.

By Dibakar Sigdel

arXiv Machine Learning

Jun 29

CANNs: A Toolkit for Research on Continuous Attractor Neural Networks

arXiv:2606. 27783v1 Announce Type: cross Abstract: Continuous attractor neural networks (CANNs) are the canonical computational framework for how the brain encodes continuous variables such as spatial position, head direction, and movement direction, and explain the activity of hippocampal place cells, entorhinal grid cells, and head-direction cells.

By Sichao He, Aiersi Tuerhong, Shangjun She, Tianhao Chu, Yuling Wu, Junfeng Zuo, Si Wu

arXiv Machine Learning

Jun 9

Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model

arXiv:2606. 09770v1 Announce Type: cross Abstract: Nearby neurons in cortex share similar response profiles, producing systematic spatial organization across sensory and cognitive systems.

By Badr AlKhamissi, Johannes Mehrer, Lara Marinov, Ahmed Abdelaal, Abdulkadir Gokce, Martin Schrimpf

fine-tuning multimodal

Related stories

Recursive Gaussian Processes and the Bayesian Brain

Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration

Dendritic In-Context Learning in a Single-Layer Spiking Neural Network

Learning the Brain's Dynamics as a Port-Hamiltonian System: A GNN-Surrogate Metriplectic Twin for Non-Equilibrium Cortical Dynamics and Closed-Loop Neuromodulation

CANNs: A Toolkit for Research on Continuous Attractor Neural Networks

Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model