Predictive Allostatic Organization in Recurrent and Spiking Agents Under Partial Observability
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Computer Science > Neural and Evolutionary Computing
Title:Predictive Allostatic Organization in Recurrent and Spiking Agents Under Partial Observability
Abstract:Adaptive behavior under partial observability depends on internal organization that carries information beyond the current observation. Drawing on Barrett and Miller's account of categorization as predictive, compressive, functionally organized, and allostatically constrained, we test whether recurrent and spiking agents develop internal states with corresponding computational properties. Agents operate in an energy-constrained foraging task requiring resource acquisition, threat avoidance, contact-dependent consumption, and regulation of an internal energy variable. In a frozen benchmark, learned agents outperform random and heuristic baselines; the trace-augmented recurrent policy is strongest overall, while spiking variants show stress-specific differences. Early internal dynamics predict later full-safe-efficient success above permutation baseline, reaching a maximum ROC-AUC of 0.802. Reduced PCA subspaces retain behaviorally relevant information. Feature-family controls show that predictive signal is distributed across trace, policy-head, internal-dynamics, observation, and allostatic variables, and low-energy state remains strongly decodable after explicit energy-related features are removed. Evaluation-time perturbations to temporal state, sensory information, operating conditions, and allostatic mechanisms alter behavior and/or internal prediction. Seed-balanced event probes show weaker but measurable information about future contact, successful consumption, and threat events, alongside strong low-energy decoding. We interpret this pattern as a computational analogue of predictive allostatic organization: distributed control regimes that are predictive, energy-sensitive, action-relevant, and partly causally involved, without claiming biological validation or discrete symbolic categories.
| Comments: | 36 pages, 6 figures. Code and reproducibility materials: this https URL |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.11506 [cs.NE] |
| (or arXiv:2608.11506v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2608.11506
arXiv-issued DOI via DataCite
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