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Hazy, MD", "links": { "alternate": { "href": "https://www.zotero.org/thazy", "type": "text/html" } } }, "creatorSummary": "Westeinde et al.", "parsedDate": "2024-02", "numChildren": 1 }, "data": { "key": "DRT3IPGT", "version": 87201, "itemType": "journalArticle", "title": "Transforming a head direction signal into a goal-oriented steering command", "creators": [ { "creatorType": "author", "firstName": "Elena A.", "lastName": "Westeinde" }, { "creatorType": "author", "firstName": "Emily", "lastName": "Kellogg" }, { "creatorType": "author", "firstName": "Paul M.", "lastName": "Dawson" }, { "creatorType": "author", "firstName": "Jenny", "lastName": "Lu" }, { "creatorType": "author", "firstName": "Lydia", "lastName": "Hamburg" }, { "creatorType": "author", "firstName": "Benjamin", "lastName": "Midler" }, { "creatorType": "author", "firstName": "Shaul", "lastName": "Druckmann" }, { "creatorType": "author", "firstName": "Rachel I.", "lastName": "Wilson" } ], "abstractNote": "To navigate, we must continuously estimate the direction we are headed in, and we must correct deviations from our goal1. Direction estimation is accomplished by ring attractor networks in the head direction system2,3. However, we do not fully understand how the sense of direction is used to guide action. Drosophila connectome analyses4,5 reveal three cell populations (PFL3R, PFL3L and PFL2) that connect the head direction system to the locomotor system. Here we use imaging, electrophysiology and chemogenetic stimulation during navigation to show how these populations function. Each population receives a shifted copy of the head direction vector, such that their three reference frames are shifted approximately 120° relative to each other. Each cell type then compares its own head direction vector with a common goal vector; specifically, it evaluates the congruence of these vectors via a nonlinear transformation. The output of all three cell populations is then combined to generate locomotor commands. PFL3R cells are recruited when the fly is oriented to the left of its goal, and their activity drives rightward turning; the reverse is true for PFL3L. Meanwhile, PFL2 cells increase steering speed, and are recruited when the fly is oriented far from its goal. PFL2 cells adaptively increase the strength of steering as directional error increases, effectively managing the tradeoff between speed and accuracy. Together, our results show how a map of space in the brain can be combined with an internal goal to generate action commands, via a transformation from world-centric coordinates to body-centric coordinates.", "publicationTitle": "Nature", "publisher": "Nature Publishing Group", "place": "", "date": "2024-02", "volume": "626", "issue": "8000", "section": "", "partNumber": "", "partTitle": "", "pages": "819-826", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1038/s41586-024-07039-2", "citationKey": "WesteindeKelloggDawsonEtAl24", "url": "https://www.nature.com/articles/s41586-024-07039-2", "accessDate": "2026-04-10T20:45:21Z", "PMID": "", "PMCID": "", "ISSN": "1476-4687", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.nature.com", "callNumber": "", "rights": "2024 The Author(s)", "extra": "", "tags": [ { "tag": "Navigation", "type": 1 }, { "tag": "Network models", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2026-04-10T20:45:21Z", "dateModified": "2026-04-10T20:45:21Z" } }, { "key": "ZJRLZ3CN", "version": 87198, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/ZJRLZ3CN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/ZJRLZ3CN", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/WFHHAEDW", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2407002, "username": "thazy", "name": "Thomas E. 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Hazy, MD", "links": { "alternate": { "href": "https://www.zotero.org/thazy", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "FCD3PR2B", "version": 87193, "parentItem": "3DGSKAA2", "itemType": "attachment", "linkMode": "imported_url", "title": "MussellsPiresZhangParacheEtAl24", "accessDate": "2026-04-10T20:39:16Z", "url": "https://www.nature.com/articles/s41586-023-07006-3.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "MussellsPiresZhangParacheEtAl24.pdf", "md5": "b0e2308e3ca1a71ea4cf906c7331c4e6", "mtime": 1775853556997, "tags": [], "relations": {}, "dateAdded": "2026-04-10T20:39:16Z", "dateModified": "2026-04-10T20:40:00Z" } }, { "key": "3DGSKAA2", "version": 87190, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/3DGSKAA2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/3DGSKAA2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2407002, "username": "thazy", "name": "Thomas E. Hazy, MD", "links": { "alternate": { "href": "https://www.zotero.org/thazy", "type": "text/html" } } }, "creatorSummary": "Mussells Pires et al.", "parsedDate": "2024-02", "numChildren": 1 }, "data": { "key": "3DGSKAA2", "version": 87190, "itemType": "journalArticle", "title": "Converting an allocentric goal into an egocentric steering signal", "creators": [ { "creatorType": "author", "firstName": "Peter", "lastName": "Mussells Pires" }, { "creatorType": "author", "firstName": "Lingwei", "lastName": "Zhang" }, { "creatorType": "author", "firstName": "Victoria", "lastName": "Parache" }, { "creatorType": "author", "firstName": "L. F.", "lastName": "Abbott" }, { "creatorType": "author", "firstName": "Gaby", "lastName": "Maimon" } ], "abstractNote": "Neuronal signals that are relevant for spatial navigation have been described in many species1–10. However, a circuit-level understanding of how such signals interact to guide navigational behaviour is lacking. Here we characterize a neuronal circuit in the Drosophila central complex that compares internally generated estimates of the heading and goal angles of the fly—both of which are encoded in world-centred (allocentric) coordinates—to generate a body-centred (egocentric) steering signal. Past work has suggested that the activity of EPG neurons represents the fly’s moment-to-moment angular orientation, or heading angle, during navigation2,11. An animal’s moment-to-moment heading angle, however, is not always aligned with its goal angle—that is, the allocentric direction in which it wishes to progress forward. We describe FC2 cells12, a second set of neurons in the Drosophila brain with activity that correlates with the fly’s goal angle. Focal optogenetic activation of FC2 neurons induces flies to orient along experimenter-defined directions as they walk forward. EPG and FC2 neurons connect monosynaptically to a third neuronal class, PFL3 cells12,13. We found that individual PFL3 cells show conjunctive, spike-rate tuning to both the heading angle and the goal angle during goal-directed navigation. Informed by the anatomy and physiology of these three cell classes, we develop a model that explains how this circuit compares allocentric heading and goal angles to build an egocentric steering signal in the PFL3 output terminals. Quantitative analyses and optogenetic manipulations of PFL3 activity support the model. Finally, using a new navigational memory task, we show that flies expressing disruptors of synaptic transmission in subsets of PFL3 cells have a reduced ability to orient along arbitrary goal directions, with an effect size in quantitative accordance with the prediction of our model. The biological circuit described here reveals how two population-level allocentric signals are compared in the brain to produce an egocentric output signal that is appropriate for motor control.", "publicationTitle": "Nature", "publisher": "Nature Publishing Group", "place": "", "date": "2024-02", "volume": "626", "issue": "8000", "section": "", "partNumber": "", "partTitle": "", "pages": "808-818", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1038/s41586-023-07006-3", "citationKey": "MussellsPiresZhangParacheEtAl24", "url": "https://www.nature.com/articles/s41586-023-07006-3", "accessDate": "2026-04-10T20:39:10Z", "PMID": "", "PMCID": "", "ISSN": "1476-4687", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.nature.com", "callNumber": "", "rights": "2024 The Author(s)", "extra": "", "tags": [ { "tag": "Navigation", "type": 1 }, { "tag": "Network models", "type": 1 }, { "tag": "Neural circuits", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2026-04-10T20:39:10Z", "dateModified": "2026-04-10T20:39:10Z" } }, { "key": "XKRCYQRF", "version": 87187, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/XKRCYQRF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/XKRCYQRF", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "van Heukelum et al.", "parsedDate": "2020-05-01", "numChildren": 2 }, "data": { "key": "XKRCYQRF", "version": 87187, "itemType": "journalArticle", "title": "Where is cingulate cortex? A cross-species view", "creators": [ { "creatorType": "author", "firstName": "Sabrina", "lastName": "van Heukelum" }, { "creatorType": "author", "firstName": "Rogier B.", "lastName": "Mars" }, { "creatorType": "author", "firstName": "Martin", "lastName": "Guthrie" }, { "creatorType": "author", "firstName": "Jan K.", "lastName": "Buitelaar" }, { "creatorType": "author", "firstName": "Christian F.", "lastName": "Beckmann" }, { "creatorType": "author", "firstName": "Paul H. E.", "lastName": "Tiesinga" }, { "creatorType": "author", "firstName": "Brent A.", "lastName": "Vogt" }, { "creatorType": "author", "firstName": "Jeffrey C.", "lastName": "Glennon" }, { "creatorType": "author", "firstName": "Martha N.", "lastName": "Havenith" } ], "abstractNote": "To compare findings across species, neuroscience relies on cross-species homologies, particularly in terms of brain areas. For cingulate cortex, a structure implicated in behavioural adaptation and control, a homologous definition across mammals is available – but currently not employed by most rodent researchers. The standard partitioning of rodent cingulate cortex is inconsistent with that in any other model species, including humans. Reviewing the existing literature, we show that the homologous definition better aligns results of rodent studies with those of other species, and reveals a clearer structural and functional organisation within rodent cingulate cortex itself. Based on these insights, we call for widespread adoption of the homologous nomenclature, and reinterpretation of previous studies originally based on the nonhomologous partitioning of rodent cingulate cortex.", "publicationTitle": "Trends in Neurosciences", "publisher": "", "place": "", "date": "2020-05-01", "volume": "43", "issue": "5", "section": "", "partNumber": "", "partTitle": "", "pages": "285-299", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Trends in Neurosciences", "DOI": "10.1016/j.tins.2020.03.007", "citationKey": "vanHeukelumMarsGuthrieEtAl20", "url": "https://www.sciencedirect.com/science/article/pii/S0166223620300655", "accessDate": "2023-04-21T21:44:33Z", "PMID": "", "PMCID": "", "ISSN": "0166-2236", "archive": "", "archiveLocation": "", "shortTitle": "Where is cingulate cortex?", "language": "en", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "cingulate cortex", "type": 1 }, { "tag": "comparative neuroscience", "type": 1 }, { "tag": "cross-species research", "type": 1 }, { "tag": "humans", "type": 1 }, { "tag": "primates", "type": 1 }, { "tag": "rodents", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2023-04-21T21:44:33Z", "dateModified": "2026-04-08T20:33:59Z" } }, { "key": "F7BZZLSP", "version": 87186, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/F7BZZLSP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/F7BZZLSP", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/IQP53ADF", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2407002, "username": "thazy", "name": "Thomas E. 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O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Ringstrom and Schrater", "parsedDate": "2025-06-11", "numChildren": 3 }, "data": { "key": "7KX7YBBY", "version": 87179, "itemType": "preprint", "title": "A unified theory of compositionality, modularity, and interpretability in markov decision processes", "creators": [ { "creatorType": "author", "firstName": "Thomas J.", "lastName": "Ringstrom" }, { "creatorType": "author", "firstName": "Paul R.", "lastName": "Schrater" } ], "abstractNote": "We introduce Option Kernel Bellman Equations (OKBEs) for a new reward-free Markov Decision Process. Rather than a value function, OKBEs directly construct and optimize a predictive map called a state-time option kernel (STOK) to maximize the probability of completing a goal while avoiding constraint violations. 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The key idea in deriving such algorithms is to approximate the posterior using candidate distributions estimated by using natural gradients. Different candidate distributions result in different algorithms and further approximations to natural gradients give rise to variants of those algorithms. 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Here, we show that online natural gradient descent on F, under Poisson assumptions, leads to a recurrent spiking neural network that performs variational inference via membrane potential dynamics. The resulting model -- the iterative Poisson variational autoencoder (iP-VAE) -- replaces the encoder network with local updates derived from natural gradient descent on F. Theoretically, iP-VAE yields a number of desirable features such as emergent normalization via lateral competition, and hardware-efficient integer spike count representations. Empirically, iP-VAE outperforms both standard VAEs and Gaussian-based predictive coding models in sparsity, reconstruction, and biological plausibility. iP-VAE also exhibits strong generalization to out-of-distribution inputs, exceeding hybrid iterative-amortized VAEs. 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O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } } }, "data": { "key": "JMVXTX8L", "version": 87144, "parentItem": "2RJ6PSG9", "itemType": "attachment", "linkMode": "imported_url", "title": "Snapshot", "accessDate": "2026-04-06T23:18:45Z", "url": "http://arxiv.org/abs/2602.13421", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "2602.html", "md5": "162208318f93b32b329f5cce89f8df0d", "mtime": 1775517525790, "tags": [], "relations": {}, "dateAdded": "2026-04-06T23:18:45Z", "dateModified": "2026-04-06T23:18:45Z" } }, { "key": "2RJ6PSG9", "version": 87142, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/2RJ6PSG9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/2RJ6PSG9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Vafaii and Yates", "parsedDate": "2026-02-13", "numChildren": 2 }, "data": { "key": "2RJ6PSG9", "version": 87142, "itemType": "preprint", "title": "Metabolic cost of information processing in Poisson variational autoencoders", "creators": [ { "creatorType": "author", "firstName": "Hadi", "lastName": "Vafaii" }, { "creatorType": "author", "firstName": "Jacob L.", "lastName": "Yates" } ], "abstractNote": "Computation in biological systems is fundamentally energy-constrained, yet standard theories of computation treat energy as freely available. Here, we argue that variational free energy minimization under a Poisson assumption offers a principled path toward an energy-aware theory of computation. Our key observation is that the Kullback-Leibler (KL) divergence term in the Poisson free energy objective becomes proportional to the prior firing rates of model neurons, yielding an emergent metabolic cost term that penalizes high baseline activity. This structure couples an abstract information-theoretic quantity -- the *coding rate* -- to a concrete biophysical variable -- the *firing rate* -- which enables a trade-off between coding fidelity and energy expenditure. Such a coupling arises naturally in the Poisson variational autoencoder (P-VAE) -- a brain-inspired generative model that encodes inputs as discrete spike counts and recovers a spiking form of *sparse coding* as a special case -- but is absent from standard Gaussian VAEs. To demonstrate that this metabolic cost structure is unique to the Poisson formulation, we compare the P-VAE against Grelu-VAE, a Gaussian VAE with ReLU rectification applied to latent samples, which controls for the non-negativity constraint. Across a systematic sweep of the KL term weighting coefficient $β$ and latent dimensionality, we find that increasing $β$ monotonically increases sparsity and reduces average spiking activity in the P-VAE. In contrast, Grelu-VAE representations remain unchanged, confirming that the effect is specific to Poisson statistics rather than a byproduct of non-negative representations. 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