arXiv Machine Learning By Frederick Hayes III

Predictive Allostatic Organization in Recurrent and Spiking Agents Under Partial Observability

Read the original on arXiv Machine Learning →

arXiv:2608. 11506v1 Announce Type: cross Abstract: Adaptive behavior under partial observability depends on internal organization that carries information beyond the current observation.

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

arXiv AI
Jun 2

Rare Events, Real Signals: Functional Ensembles as Units of Computation in Deep Spiking Networks

arXiv:2606. 00073v1 Announce Type: cross Abstract: We investigate how internal representations emerge across hierarchical processing systems by introducing a neuroscience-inspired framework for analyzing deep spiking neural networks (SNN) through the lens of functional connectivity.

By Aditi Aravind, Konstantinos Ladakis, Mario Alexios Savaglio, Stelios M. Smirnakis, Maria Papadopouli