This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Bayesian inference is widely regarded as a core computational principle of brain function, providing a normative framework for perception, decision-making, and learning under uncertainty. By combining a biologically inspired spiking neural model with Bayesian inference principles, we propose a brain-like control algorithm capable of operating in uncertain environments. We use the mountain car parking problem as a benchmark with non-linear dynamics. Our results demonstrate that the proposed controller can successfully update states in real time and generate goal-directed action plans through spike-driven dynamics. The results highlight the proposed model's potential as a bridge between computational neuroscience and probabilistic control theory.
We propose IM-LEPP (Integrated Multimodal Latent Energy-based Predictive Processing), a hierarchical, energy-based model of multimodal cognition that extends a previously proposed single-modality model (LEPP) to integrate vision and language. Following the view that generative neural networks are effective theories of cognitive dynamics, analogous to how statistical mechanics relates to thermodynamics, IM-LEPP models cognition as latent states flowing through learned energy landscapes rather than as an account of neural circuitry. The architecture is a hub-and-spoke hierarchy, grounded in the controlled semantic cognition framework of Lambon Ralph et al., in which predictive-coding pipelines for visual objects, scenes, and linguistic units converge on a shared amodal hub modeled on the anterior temporal lobe. Each pipeline's own prediction is conditioned by, rather than overwritten by, the current hub state, preserving pipeline-specific identity while letting every prediction reflect the full multimodal context. We show this architecture gives a mechanistic account of attentional phenomena such as inattentional blindness and Necker-cube bistability, and that its structure recovers or motivates independently established findings in psycholinguistics, including surprisal theory, the N400/P600 ERP components, and garden-path reanalysis, alongside a falsifiable contrast with transformer language models on trajectory-sensitivity in next-word prediction. We also discuss data-efficient language acquisition relative to LLMs, outline a semantic/episodic memory subsystem, situate the model against predictive coding, the free-energy principle, JEPA, and Hierarchical Temporal Memory, and propose concrete experimental predictions to test its central claims Key Words: predictive processing; predictive coding; energy-based models; diffusion models; effective theory; computational neuroscience.
The emergence of orientation selectivity in the primary visual cortex (V1) remains a central question in computational neuroscience. Shirazi's Bayes-Markov model proposed a probabilistic explanation for how orientation-selective inhibition can arise from non-oriented lateral geniculate nucleus (LGN) inputs through local inference. In that formulation, the activity pattern of striate cortical inhibitory (SCI) cells is estimated from the LGN activity pattern by a maximum a posteriori (MAP) criterion over a two-layer hierarchical Markov random field, and the resulting inference is implemented through a local parallel relaxation algorithm. We provide a computationally explicit re-implementation and quantitative simulation study of this framework. We reconstruct the mathematical model, describe its fully LGN-driven update rule, and implement a vectorized simulation framework that preserves the original local clique operations while making systematic parameter sweeps feasible. We evaluate the model using orientation tuning curves, an orientation selectivity index (OSI), controlled LGN noise perturbations, contrast tests, and model-variant comparisons. We further add a spiking SCI-layer realization using leaky integrate-and-fire and Hodgkin-Huxley neurons to examine whether the rate-coded SCI field can be expressed through temporally explicit neural activity. The simulations support the central qualitative behavior of the Bayes-Markov framework: sharp orientation selectivity, robustness to moderate LGN noise, and a biologically interpretable proof-of-concept spiking realization of the inferred inhibitory field.