@article {69794, title = {Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems}, journal = {bioRxiv}, year = {2025}, month = {2025 Feb 26}, abstract = {

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.

}, doi = {10.1101/2025.02.25.640158}, url = {https://www.biorxiv.org/content/early/2025/02/26/2025.02.25.640158}, author = {Pjanovic, Vojko and Zavatone-Veth, Jacob A and Masset, Paul and Keemink, Sander W and Nardin, Michele} } @article {69577, title = {Learning reshapes the hippocampal representation hierarchy}, journal = {Proc. Natl. Acad. Sci. U.S.A.}, year = {2025}, month = {2025 Mar 10}, abstract = {

Biological neural networks seem to efficiently select and represent task-relevant features of their inputs, an ability that is highly sought after also in artificial networks. A lot of work has gone into identifying such representations in both sensory and motor systems; however, less is understood about how representations form during complex learning conditions to support behavior, especially in higher associative brain areas. Our work shows that the hippocampus maintains a robust hierarchical representation of task variables and that this structure can support new learning through minimal changes to the neural representations.

bioRxiv Preprint:\ https://www.doi.org/10.1101/2024.08.21.608911

}, doi = {10.1073/pnas.2417025122}, url = {https://doi.org/10.1073/pnas.2417025122}, author = {Chiossi, Heloisa S. C. and Nardin, Michele and Tka{\v c}ik, Ga{\v s}per and Csicsvari, Jozsef L.} } @article {70222, title = {Reconstructing a physiological state space via chronic jugular microdialysis in freely moving mice}, journal = {bioRxiv}, year = {2025}, month = {2025 Dec 11}, abstract = {

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.

}, doi = {10.64898/2025.12.08.692974}, url = {https://www.biorxiv.org/content/early/2025/12/11/2025.12.08.692974}, author = {Nardin, Michele and Wang, Nan and Elziny, Soad and Boyer, Claire and Pjanovic, Vojko and Schuster, Luisa and Boklund, Peter and Lindo, Sarah and Morris, Kendra and Ilanges, Anoj and Voigts, Jakob and Dennis, Emily Jane} } @article {69383, title = {Hippocampal cholecystokinin-expressing interneurons regulate temporal coding and contextual learning}, journal = {Neuron}, year = {2024}, month = {2024 Apr 17}, abstract = {

Cholecystokinin-expressing interneurons (CCKIs) are hypothesized to shape pyramidal cell-firing patterns and regulate network oscillations and related network state transitions. To directly probe their role in the CA1 region, we silenced their activity using optogenetic and chemogenetic tools in mice. Opto-tagged CCKIs revealed a heterogeneous population, and their optogenetic silencing triggered wide disinhibitory network changes affecting both pyramidal cells and other interneurons. CCKI silencing enhanced pyramidal cell burst firing and altered the temporal coding of place cells: theta phase precession was disrupted, whereas sequence reactivation was enhanced. Chemogenetic CCKI silencing did not alter the acquisition of spatial reference memories on the Morris water maze but enhanced the recall of contextual fear memories and enabled selective recall when similar environments were tested. This work suggests the key involvement of CCKIs in the control of place-cell temporal coding and the formation of contextual memories.

}, issn = {08966273}, doi = {10.1016/j.neuron.2024.03.019}, url = {https://doi.org/10.1016/j.neuron.2024.03.019}, author = {Rangel Guerrero, D{\'a}maris K. and Balueva, Kira and Barayeu, Uladzislau and Baracskay, Peter and Gridchyn, Igor and Nardin, Michele and Roth, Chiara Nina and Wulff, Peer and Csicsvari, Jozsef} } @article {68999, title = {Theta oscillations as a substrate for medial prefrontal-hippocampal assembly interactions.}, journal = {Cell Reports}, volume = {42}, year = {2023}, month = {2023 Aug 24}, pages = {113015}, abstract = {

The execution of cognitive functions requires coordinated circuit activity across different brain areas that involves the associated firing of neuronal assemblies. Here, we tested the circuit mechanism behind assembly interactions between the hippocampus and the medial prefrontal cortex (mPFC) of adult rats by recording neuronal populations during a rule-switching task. We identified functionally coupled CA1-mPFC cells that synchronized their activity beyond that expected from common spatial coding or oscillatory firing. When such cell pairs fired together, the mPFC cell strongly phase locked to CA1 theta oscillations and maintained consistent theta firing phases, independent of the theta timing of their CA1 counterpart. These functionally connected CA1-mPFC cells formed interconnected assemblies. While firing together with their CA1 assembly partners, mPFC cells fired along specific theta sequences. Our results suggest that upregulated theta oscillatory firing of mPFC cells can signal transient interactions with specific CA1 assemblies, thus enabling distributed computations.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2023.113015}, author = {Nardin, Michele and Kaefer, Karola and Stella, Federico and Csicsvari, Jozsef} }