Pjanovic, V. | bioRxiv Science

bioRxiv2025

Reconstructing a physiological state space via chronic jugular microdialysis in freely moving mice

Maintaining physiological homeostasis requires a complex interplay among endocrine organs, peripheral tissues, and distributed neuroendocrine control circuits, all of which are coupled through feedback loops that operate over minutes to hours. Although many physiological needs are broadcast through hormones, metabolites, and other chemical compounds circulating in the bloodstream, we rarely observe more than a few of these messengers together and at high cadence during behavior. To address this, we developed a minimally disruptive workflow to measure the free fraction of hundreds of amines and small peptides at a 7.5-minute cadence for [~]8 hrs in freely moving mice using chronic jugular microdialysis implants and chemical isotope labeling Liquid Chromatography-Mass Spectrometry. Single-compound profiles behave according to known physiology, such as purine turnover correlating with movement, delayed histamine/5-HIAA changes, and coordinated amino-acid dynamics. Our multiplexed measures enable high-dimensional analyses that uncover properties of the underlying dynamics. For example, systems-level analyses show that 10 dimensions explain over 70% of the variance in hormone/metabolite covariation, consistent with a low rank description of the physiological state space, with projections aligned to locomotion state transitions. Our work opens avenues for the discovery of hormonal dynamics, compound interactions, and their effects on behavior.

physiology↗

bioRxiv2025

Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems

Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior effectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to reconcile sampling-based methods with operational principles of higher-order brain areas, such as attractor dynamics of persistent neural representations. In this study, we present a spiking neural network model for the head-direction (HD) system that combines sampling-based inference with attractor dynamics. To achieve this, we derive the required spiking neural network dynamics and interactions to perform sampling from a large family of probability distributions--including variables encoded with Poisson noise. We then propose a method that allows the network to update its estimate of the current head direction by integrating angular velocity samples--derived from noisy inputs--with a pull towards a circular manifold, thereby maintaining consistent attractor dynamics. This model makes specific, testable predictions about the HD system that can be examined in future neurophysiological experiments: it predicts correlated subthreshold voltage fluctuations; distinctive short- and long-term firing correlations among neurons; and characteristic statistics of the movement of the neural activity "bump" representing the head direction. Overall, our approach extends previous theories on probabilistic sampling with spiking neurons, offers a novel perspective on the computations responsible for orientation and navigation, and supports the hypothesis that sampling-based methods can be combined with attractor dynamics to provide a viable framework for studying neural dynamics across the brain.

neuroscience↗