Tootfinder

Simultaneously Determining Regional Heterogeneity and Connection Directionality from Neural Activity and Symmetric Connection
Jiawen Chang, Zhuda Yang, Changsong Zhou
https://arxiv.org/abs/2510.04110

Simultaneously Determining Regional Heterogeneity and Connection Directionality from Neural Activity and Symmetric Connection
The spatiotemporal patterns of neural dynamics are jointly shaped by directed structural interactions and heterogeneous intrinsic features of the neural components. Despite well-developed methods for estimating directionality in network connections from network of homogeneous nodes, how local heterogeneity impacts on directionality estimation remains poorly understood. In particular, the role of excitatory-inhibitory interactions in shaping network directionality and how these interactions shou…

Synchrony in firing rate neural networks with multiple delays: A harmonic balance approach
S Coombes, H G E Meijer
https://arxiv.org/abs/2509.22495 https://

Synchrony in firing rate neural networks with multiple delays: A harmonic balance approach
Networks of neural mass nodes with delayed interactions are increasingly being used as models for large-scale brain activity. To complement the growing number of computational studies of such networks, it is timely to develop new mathematical studies of their solution structure and bifurcations. The analysis of steady states and their stability is relatively well developed, though that of time-periodic solutions is far less so. Here, we show how the method of harmonic balance is ideally suited …

A Graph-Based Approach to Alert Contextualisation in Security Operations Centres
Magnus Wiik Eckhoff, Peter Marius Flydal, Siem Peters, Martin Eian, Jonas Halvorsen, Vasileios Mavroeidis, Gudmund Grov
https://arxiv.org/abs/2509.12923

A Graph-Based Approach to Alert Contextualisation in Security Operations Centres
Interpreting the massive volume of security alerts is a significant challenge in Security Operations Centres (SOCs). Effective contextualisation is important, enabling quick distinction between genuine threats and benign activity to prioritise what needs further analysis.This paper proposes a graph-based approach to enhance alert contextualisation in a SOC by aggregating alerts into graph-based alert groups, where nodes represent alerts and edges denote relationships within defined time-windows…

Toward a Physics of Deep Learning and Brains
Arsham Ghavasieh, Meritxell Vila-Minana, Akanksha Khurd, John Beggs, Gerardo Ortiz, Santo Fortunato
https://arxiv.org/abs/2509.22649

Toward a Physics of Deep Learning and Brains
Deep neural networks and brains both learn and share superficial similarities: processing nodes are likened to neurons and adjustable weights are likened to modifiable synapses. But can a unified theoretical framework be found to underlie them both? Here we show that the equations used to describe neuronal avalanches in living brains can also be applied to cascades of activity in deep neural networks. These equations are derived from non-equilibrium statistical physics and show that deep neural…

Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks
Kijung Yoon
https://arxiv.org/abs/2509.17174 https://

Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks
Inferring synaptic connectivity from neural population activity is a fundamental challenge in computational neuroscience, complicated by partial observability and mismatches between inference models and true circuit dynamics. In this study, we propose a graph-based neural inference model that simultaneously predicts neural activity and infers latent connectivity by modeling neurons as interacting nodes in a graph. The architecture features two distinct modules: one for learning structural conne…