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Intrinsic cellular persistent firing sustains hippocampal spatial representations during working memory.

Code ↔ Paper

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The 9 matches · 4 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › In vivo data analysis › Bayesian position decoding ↔ Fig 7.zip/decode_position_from_DataRateMaps.m, lines 1 to 45 · score 0.82 · posterior marginals, Decoded position, bin midpoint, Euclidean, linear, dimension · Report a mismatch
  2. [2] § MATERIALS AND METHODS › In vivo data analysis › SVM analysis ↔ Fig 5.zip/Acc_SVM5.m, the whole file · a weak match · score 0.73 · cross validation, SVM model, fitcsvm, linear, kernel, class · Report a mismatch
  3. [3] § MATERIALS AND METHODS › In vivo data analysis › Bayesian position decoding ↔ Fig 7.zip/decode_position_from_DataRateMaps.m, lines 1 to 45 · score 0.71 · rate map, Poisson, likelihood, uniform, log, numerical · Report a mismatch
  4. [4] § MATERIALS AND METHODS › In vivo data analysis › Cell-type classification based on activity change ↔ Fig4.zip/PlotPlotBinFirRateBatchResult.m, the whole file · a weak match · score 0.65 · 0–25 s, binned firing rate, delay period, 15 s, baseline, cells · Report a mismatch
  5. [5] § RESULTS › TRPC4 KD suppresses in vivo persistent firing ↔ Fig 2 Fig S2 and FigS3.zip/Fig2.m, lines 798 to 862 · score 0.62 · 2.5–4, 0–2.5, 4–8, 25 s, Figure 2, KD · Report a mismatch
  6. [6] § RESULTS › Activity outside the delay period is intact ↔ Fig4.zip/PlotBinFirRateBatch.m, the whole file · a weak match · score 0.58 · 20–21 s, 10–11, 20 s, Figure 4, firing · Report a mismatch
  7. [7] § MATERIALS AND METHODS › Statistical analysis ↔ FigS8.zip/FigS8.m, lines 23 to 106 · score 0.56 · Outliers exclusion, Q1, Q3, IQR, rank, Tukey · Report a mismatch
  8. [8] § MATERIALS AND METHODS › In vivo data analysis › Bayesian position decoding ↔ Fig 7.zip/Fig7_H_K.m, lines 18 to 90 · score 0.52 · rate maps, complement, kernel, decoding, smoothed, Gaussian · Report a mismatch
  9. [9] § MATERIALS AND METHODS › In vivo data analysis › SVM analysis ↔ Fig4.zip/PlotPlotBinFirRateBatchResult.m, the whole file · a weak match · score 0.51 · 0–25 s, delay period, binned · Report a mismatch

Paper

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The authors' code

MATLAB · 310 lines · 12 KB · CC-BY-4.0 · 2 matches

  1. function [pred_bins, pred_xy, post, px, py, model, R_all, edges_all, gt_bins, gt_xy, gt_x, gt_y, t_mid, x_error, y_error, err_euclid] = decode_position_from_ratemaps( ...
  2. Data, CellSel, DecodeWindows, BinSize, x_dim, y_dim, RateMapsIn, varargin)
  3. % DECODE_POSITION_FROM_RATEMAPS
  4. % Bayesian XY decoding using *externally-provided* ratemaps and grid dimensions.
  5. % Accepts x_dim / y_dim as **EDGES or CENTERS** (auto-detected).
  6. % No occupancy is computed; pass a numeric prior if desired, otherwise uniform.
  7. %
  8. % INPUTS
  9. % Data : Data.root.ts (time vector) and spike(sh,un).i (indices → ts)
  10. % CellSel : [nCells×2] shank/unit list (order matches RateMapsIn)
  11. % DecodeWindows : [nSeg×2] seconds, [t_start t_end] per decode window
  12. % BinSize : scalar seconds for decoding time bins
  13. % x_dim : X grid definition (edges or centers), length nx+1 or nx
  14. % y_dim : Y grid definition (edges or centers), length ny+1 or ny
  15. % RateMapsIn : [ny x nx x nCells] OR [nCells x (nx*ny)], in Hz
  16. %
  17. % Name-Value (optional)
  18. % 'Prior' : 'uniform' (default) | numeric [1×(nx*ny)] prior over states
  19. % 'RateFloor' : scalar Hz floor for λ (default 1e-6)
  20. % 'SmoothCounts' : logical (default false) — smooth spike counts along time
  21. % 'SmoothMethod' : char for smoothdata (default 'gaussian')
  22. % 'SmoothWindow' : integer window size in bins (default 5)
  23. %
  24. % OUTPUTS
  25. % pred_bins : [1×nDecode] MAP linear state (Y-first indexing over [ny×nx])
  26. % pred_xy : [nDecode×2] MAP (X,Y) at grid centers
  27. % post : [nStates×nDecode] posterior (states flattened Y-first)
  28. % px : [nx×nDecode] posterior marginal over X per bin (sum over Y)
  29. % py : [ny×nDecode] posterior marginal over Y per bin (sum over X)
  30. % model : metadata (edges, centers, prior, tuning, etc.); model.T_occ = []
  31. % R_all : 1×nSeg cell, spike counts per window [nCells x nBinsInWindow]
  32. % edges_all : 1×nSeg cell, time edges per window
  33. % gt_bins : [1×nDecode] GT linear state indices (NaN if outside grid)
  34. % gt_xy : [nDecode×2] GT (X,Y) at bin midpoints
  35. % gt_x : [1×nDecode] GT X at bin midpoints
  36. % gt_y : [1×nDecode] GT Y at bin midpoints
  37. % t_mid : [1×nDecode] bin-midpoint times (seconds)
  38. % x_error : [1×nDecode] (pred_x - gt_x) per bin
  39. % y_error : [1×nDecode] (pred_y - gt_y) per bin
  40. % err_euclid: [1×nDecode] Euclidean error per bin = sqrt(x_error^2 + y_error^2)
  41. %
  42. % Notes
  43. % • Y-first linear indexing means: k = sub2ind([ny nx], iy, ix).
  44. % • Likelihood: log P(n|s) ∝ n·log(λ) − λ·Δt (Poisson, independent cells).
  45. % • If you want an occupancy prior, compute it externally and pass it as 'Prior'.
  47. % ---- options
  48. p = inputParser;
  49. addParameter(p,'Prior','uniform', @(q) (ischar(q) || isstring(q)) || isnumeric(q));
  50. addParameter(p,'RateFloor',1e-6, @(x)isnumeric(x)&&isscalar(x)&&x>0);
  51. addParameter(p,'SmoothCounts',false,@(x)islogical(x)&&isscalar(x));
  52. addParameter(p,'SmoothMethod','gaussian',@(s)ischar(s)||isstring(s));
  53. addParameter(p,'SmoothWindow',5,@(x)isnumeric(x)&&isscalar(x)&&x>=1);
  54. parse(p,varargin{:});
  55. opt = p.Results;
  57. % ---- basic checks
  58. assert(isfield(Data,'root'), 'Data.root is required.');
  59. rootObj = Data.root;
  60. ts = rootObj.ts(:);
  61. assert(BinSize>0, 'BinSize must be positive.');
  63. nCells = size(CellSel,1);
  64. assert(size(DecodeWindows,2)==2, 'DecodeWindows must be [nSeg×2].');
  66. % normalize x_dim / y_dim to row vectors
  67. x_dim = x_dim(:).'; y_dim = y_dim(:).';
  68. assert(numel(x_dim)>=2 && numel(y_dim)>=2, 'x_dim and y_dim must have at least 2 elements.');
  70. % ---- interpret grid dimensions (edges vs centers) and infer nx,ny
  71. if ndims(RateMapsIn)==3
  72. % Expect [ny x nx x nCells] (rows=Y, cols=X)
  73. sY = size(RateMapsIn,1);
  74. sX = size(RateMapsIn,2);
  75. assert(size(RateMapsIn,3)==nCells, 'RateMapsIn third dim must match nCells.');
  77. % x_dim: if length == sX+1 → edges; if length == sX → centers → edges
  78. if numel(x_dim)==sX+1
  79. xedges = x_dim;
  80. elseif numel(x_dim)==sX && sX>=2
  81. xedges = centers_to_edges(x_dim);
  82. else
  83. error('x_dim length (%d) incompatible with RateMapsIn size X (%d).', numel(x_dim), sX);
  84. end
  86. % y_dim: if length == sY+1 → edges; if length == sY → centers → edges
  87. if numel(y_dim)==sY+1
  88. yedges = y_dim;
  89. elseif numel(y_dim)==sY && sY>=2
  90. yedges = centers_to_edges(y_dim);
  91. else
  92. error('y_dim length (%d) incompatible with RateMapsIn size Y (%d).', numel(y_dim), sY);
  93. end
  95. ny = sY;
  96. nx = sX;
  97. nStates = nx*ny;
  99. % Y-first flatten (MATLAB column-major) -> [nCells x nStates]
  100. RateMaps = reshape(RateMapsIn, ny*nx, nCells).';
  102. elseif ismatrix(RateMapsIn)
  103. % RateMaps given as [nCells x (nx*ny)] — deduce nx,ny from x_dim,y_dim
  104. nStates_in = size(RateMapsIn,2);
  105. assert(size(RateMapsIn,1)==nCells, 'RateMapsIn rows must match nCells.');
  107. A = (numel(x_dim)-1) * (numel(y_dim)-1); % edges case
  108. B = (numel(x_dim)) * (numel(y_dim)); % centers case
  109. if A == nStates_in
  110. xedges = x_dim; yedges = y_dim; nx = numel(xedges)-1; ny = numel(yedges)-1;
  111. elseif B == nStates_in
  112. xedges = centers_to_edges(x_dim); yedges = centers_to_edges(y_dim);
  113. nx = numel(x_dim); ny = numel(y_dim);
  114. else
  115. error(['RateMapsIn has %d states, but x_dim/y_dim lengths do not match either ' ...
  116. '(edges-1)*(edges-1) or (centers)*(centers).'], nStates_in);
  117. end
  118. nStates = nx*ny;
  119. RateMaps = RateMapsIn;
  120. else
  121. error('Unsupported RateMapsIn shape.');
  122. end
  124. % ---- apply floor, precompute log terms
  125. lam_tuning = max(RateMaps, opt.RateFloor); % [nCells x nStates], Hz
  126. log_lam = log(lam_tuning); % [nCells x nStates]
  127. sum_lam = sum(lam_tuning, 1); % [1 x nStates]
  129. % ---- centers (for reporting MAP XY)
  130. xcent = 0.5*(xedges(1:end-1)+xedges(2:end));
  131. ycent = 0.5*(yedges(1:end-1)+yedges(2:end));
  133. % ---- sanitize & merge decode windows
  134. tf = isfinite(ts);
  135. t_min = min(ts(tf)); t_max = max(ts(tf));
  136. DecodeWindows = sortrows(DecodeWindows,1);
  137. DecodeWindows(:,1) = max(DecodeWindows(:,1), t_min);
  138. DecodeWindows(:,2) = min(DecodeWindows(:,2), t_max);
  139. DecodeWindows = DecodeWindows(DecodeWindows(:,2) > DecodeWindows(:,1), :);
  140. DecodeWindows = mergeIntervals(DecodeWindows);
  142. % ---- prior (uniform or user-provided numeric only)
  143. if ischar(opt.Prior) || isstring(opt.Prior)
  144. switch lower(string(opt.Prior))
  145. case "uniform"
  146. prior = ones(1,nStates,'double')/nStates;
  147. otherwise
  148. error('Prior "%s" not supported in this version. Use ''uniform'' or a numeric prior vector.', string(opt.Prior));
  149. end
  150. else
  151. prior = opt.Prior(:).';
  152. assert(numel(prior)==nStates,'Numeric prior must be [1×(nx*ny)].');
  153. s = sum(prior); assert(isfinite(s) && s>0); prior = prior / s;
  154. end
  155. logPrior = log(prior + eps);
  157. % ---- decode each window
  158. pred_bins = []; pred_xy = []; post = [];
  159. px = []; % [nx x nDecode] — X-marginal posterior per bin (sum over Y)
  160. py = []; % [ny x nDecode] — Y-marginal posterior per bin (sum over X)
  161. R_all = cell(1,size(DecodeWindows,1)); edges_all = R_all;
  163. for w = 1:size(DecodeWindows,1)
  164. t0 = DecodeWindows(w,1); t1 = DecodeWindows(w,2);
  165. edges = t0:BinSize:t1; if abs(edges(end)-t1)>eps(t1), edges=[edges t1]; end
  166. nBinsW = numel(edges)-1;
  168. % spike counts per bin
  169. R = zeros(nCells, nBinsW, 'double');
  170. for c = 1:nCells
  171. sh = CellSel(c,1); un = CellSel(c,2);
  172. spk_i = rootObj.spike(sh,un).i(:);
  173. if isempty(spk_i), continue; end
  174. spk_i = spk_i(spk_i>=1 & spk_i<=numel(ts));
  175. if isempty(spk_i), continue; end
  176. spk_t = ts(spk_i);
  177. m = (spk_t >= t0) & (spk_t <= t1);
  178. if any(m), R(c,:) = histcounts(spk_t(m), edges); end
  179. end
  181. % optional smoothing along time
  182. if opt.SmoothCounts
  183. for c = 1:nCells
  184. R(c,:) = smoothdata(R(c,:), 2, opt.SmoothMethod, opt.SmoothWindow);
  185. end
  186. end
  188. % decode this window
  189. bin_durs = diff(edges);
  190. post_w = nan(nStates, nBinsW);
  191. pred_bins_w = nan(1, nBinsW);
  192. pred_xy_w = nan(nBinsW, 2);
  193. % per-window X/Y marginals
  194. px_w = nan(nx, nBinsW);
  195. py_w = nan(ny, nBinsW);
  197. for b = 1:nBinsW
  198. r = R(:,b); % [nCells x 1]
  199. if all(r==0)
  200. p = prior(:); % no spikes → revert to prior
  201. else
  202. ll = (r.' * log_lam) - (sum_lam * bin_durs(b));
  203. ll = ll + logPrior;
  204. ll = ll - max(ll); % stabilize
  205. p = exp(ll);
  206. s = sum(p); if s>0, p=p/s; else, p=prior(:); end
  207. end
  208. post_w(:,b) = p(:);
  210. % MAP state
  211. [~, k_hat] = max(p);
  212. [iy_hat, ix_hat] = ind2sub([ny nx], k_hat); % Y-first indexing
  213. pred_bins_w(b) = k_hat;
  214. pred_xy_w(b,:) = [xcent(ix_hat), ycent(iy_hat)];
  216. % === NEW: Posterior marginals over X and Y ===
  217. % p is [nStates×1] flattened Y-first; reshape to [ny×nx], rows=Y, cols=X
  218. Pgrid = reshape(p, ny, nx);
  219. px_w(:,b) = sum(Pgrid, 1).'; % sum over Y → [nx×1]
  220. py_w(:,b) = sum(Pgrid, 2); % sum over X → [ny×1]
  221. % =============================================
  222. end
  224. R_all{w} = R;
  225. edges_all{w} = edges;
  227. % accumulate window-wise results
  228. pred_bins = [pred_bins, pred_bins_w];
  229. pred_xy = [pred_xy; pred_xy_w];
  230. post = [post, post_w];
  231. px = [px, px_w];
  232. py = [py, py_w];
  233. end
  235. % ---- pack model (T_occ intentionally empty)
  236. model = struct();
  237. model.prior = prior;
  238. model.nx = nx; model.ny = ny;
  239. model.xedges = xedges; model.yedges = yedges;
  240. model.xcenters = xcent; model.ycenters = ycent;
  241. model.RateMaps = lam_tuning; % [nCells x nStates], Hz
  242. model.T_occ = []; % no occupancy computed
  243. model.cells_used = true(nCells,1);
  244. model.decode_windows = DecodeWindows;
  245. model.decode_edges = edges_all;
  246. model.BinSize = BinSize;
  247. model.notes = "External ratemaps; x_dim/y_dim may be edges or centers; NO occupancy.";
  249. % ===================== NEW: Ground-truth aligned to decoded bins =====================
  250. % Build bin-midpoint time vector for all decoded bins
  251. t_mid = [];
  252. for w = 1:numel(edges_all)
  253. e = edges_all{w};
  254. if numel(e) >= 2
  255. t_mid = [t_mid, 0.5*(e(1:end-1) + e(2:end))]; %#ok<AGROW>
  256. end
  257. end
  258. nDecode = numel(t_mid);
  260. % Interpolate GT XY at those midpoints
  261. xs = Data.root.x(:); ys = Data.root.y(:);
  262. gt_x = interp1(ts, xs, t_mid, 'linear', 'extrap');
  263. gt_y = interp1(ts, ys, t_mid, 'linear', 'extrap');
  264. gt_xy = [gt_x(:), gt_y(:)];
  266. % Map GT XY to grid state (Y-first)
  267. ix = discretize(gt_x, xedges); % 1..nx or NaN
  268. iy = discretize(gt_y, yedges); % 1..ny or NaN
  269. gt_bins = nan(1, nDecode);
  270. ok = isfinite(ix) & isfinite(iy);
  271. gt_bins(ok) = sub2ind([ny, nx], iy(ok), ix(ok)); % Y-first linear index
  272. % ===================================================================
  274. % ===================== NEW: Errors =====================
  275. % Component errors (pred - GT), returned as row vectors [1 x nDecode]
  276. x_error = (pred_xy(:,1) - gt_x(:)).';
  277. y_error = (pred_xy(:,2) - gt_y(:)).';
  278. % Euclidean error per bin
  279. err_euclid = sqrt(x_error.^2 + y_error.^2);
  280. % =======================================================
  282. end
  284. % ---------------- helpers ----------------
  285. function edges = centers_to_edges(centers)
  286. % Convert a strictly monotonic vector of centers to edges by midpoints,
  287. % extrapolating half-step at both ends.
  288. centers = centers(:).';
  289. assert(numel(centers)>=2, 'Need at least 2 centers to form edges.');
  290. d = diff(centers);
  291. inner = (centers(1:end-1) + centers(2:end))/2;
  292. first = centers(1) - d(1)/2;
  293. last = centers(end) + d(end)/2;
  294. edges = [first, inner, last];
  295. end
  297. function I = mergeIntervals(I)
  298. % Merge overlapping intervals in a [n×2] list of [start end] rows.
  299. if isempty(I), return; end
  300. I = sortrows(I,1);
  301. out = I(1,:);
  302. for k=2:size(I,1)
  303. if I(k,1) <= out(end,2)
  304. out(end,2) = max(out(end,2), I(k,2));
  305. else
  306. out = [out; I(k,:)]; %#ok<AGROW>
  307. end
  308. end
  309. I = out;
  310. end

decode_position_from_DataRateMaps.m, under CC-BY-4.0 · at the source

Overview

Authors: Babak Saber Marouf1,2,3, Antonio Reboreda1,2, Frederik Theissen1,2, Rahul Kaushik4, Magdalena Sauvage1,5,6, Alexander Dityatev4,5,6, Motoharu Yoshida1,2,6
ORCID iDs: Babak Saber Marouf, Antonio Reboreda, Rahul Kaushik, Magdalena Sauvage, Alexander Dityatev, Motoharu Yoshida
  1. Leibniz Institute for Neurobiology, Brenneckestraße 6, 39118 Magdeburg, Germany
  2. Cognitive Neurophysiology Group, German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
  3. Institute of Physiology, Medical Faculty, Otto-Von-Guericke University, 39120 Magdeburg, Germany
  4. Molecular Neuroplasticity Group, German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
  5. Medical Faculty, Otto-von-Guericke University (OVGU), 39120 Magdeburg, Germany
  6. Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany
Institutions: German Center for Neurodegenerative Diseases (Germany); Otto-von-Guericke-Universität Magdeburg (Germany); Leibniz Institute for Neurobiology (Germany); Center for Behavioral Brain Sciences (Germany)
Journal: Science advances, volume 12, issue 44, article eaef9907
Dates: received 12 February 2026; accepted 24 September 2026; published online 30 October 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aef9907 · PMID 42962002 · PMCID PMC13721955 · OpenAlex W7224749123
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging
MeSH: Action Potentials*, Hippocampus*, Memory, Short-Term*, Neurons*, Animals, Mice (* major topic)
Journal subjects: Neuroscience, Cognitive Neuroscience
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (YO177/4-1, YO177/4-3, YO177/7-1); Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE); Europäischer Sozialfonds (ZS/2016/08/80645)
Citations: not cited yet (Europe PMC); 77 references in the paper

Abstract

Neurons are the fundamental units of cognitive processing. The prevailing view holds that neurons are input-output units that fire action potentials only upon receiving sufficient synaptic input. Here, we demonstrate that this may not be the sole operational mode of neurons in vivo during cognitive tasks, by showing that hippocampal place cells actively maintain firing and preserve spatial information through molecularly defined intrinsic mechanisms. Using in vitro and in vivo recordings coupled with molecular manipulation in mice, we reveal that hippocampal persistent firing during spatial working memory is supported by individual neurons via TRPC4 ion channels. Despite the general belief that persistent activity maintains working memory content, we demonstrate that persistent firing instead maintains immediate spatial representations, potentially supporting the encoding of spatial information to guide behavior. These findings position individual neurons as active contributors to information retention, extending their traditional role as passive input-output units and potentially broadening our understanding of neural computation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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Zenodo 21775727

License: CC-BY-4.0
State: the link answers, verified on 31 October 2026
Evidence: files inventoried
Size: 13 files
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (21 files), Parallel Computing Toolbox (4 files), Signal Processing Toolbox (2 files), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 31 October 2026: the link answers (HTTP 200)
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61 files
At the source: doi.org/10.5281/zenodo.21775727

The paper's code and data availability statement is in the Data section.

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Data

No dataset and no data link were found in the paper.

Data, code, and materials availability

All data needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. All code generated for the data analysis in this study is available at https://doi.org/10.5281/zenodo.21775727.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 1 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 MeSH terms, 3 funders, 73 references.

Cite

This paper

Saber Marouf, B., Reboreda, A., Theissen, F., Kaushik, R., Sauvage, M., Dityatev, A., & Yoshida, M. (2026). Intrinsic cellular persistent firing sustains hippocampal spatial representations during working memory. Science advances, 12(44), eaef9907. https://doi.org/10.1126/sciadv.aef9907

BibTeX

@article{sabermarouf2026intrinsic,
author = {Saber Marouf, Babak and Reboreda, Antonio and Theissen, Frederik and Kaushik, Rahul and Sauvage, Magdalena and Dityatev, Alexander and Yoshida, Motoharu},
title = {{Intrinsic cellular persistent firing sustains hippocampal spatial representations during working memory}},
journal = {Science advances},
year = {2026},
month = oct,
volume = {12},
number = {44},
pages = {eaef9907},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aef9907},
url = {https://doi.org/10.1126/sciadv.aef9907},
pmid = {42962002},
pmcid = {PMC13721955}
}

RIS

TY - JOUR
AU - Saber Marouf, Babak
AU - Reboreda, Antonio
AU - Theissen, Frederik
AU - Kaushik, Rahul
AU - Sauvage, Magdalena
AU - Dityatev, Alexander
AU - Yoshida, Motoharu
TI - Intrinsic cellular persistent firing sustains hippocampal spatial representations during working memory
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/10/30
VL - 12
IS - 44
SP - eaef9907
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aef9907
UR - https://doi.org/10.1126/sciadv.aef9907
LA - en
ER -

CSL-JSON

{
"id": "10.1126/sciadv.aef9907",
"type": "article-journal",
"title": "Intrinsic cellular persistent firing sustains hippocampal spatial representations during working memory",
"container-title": "Science advances",
"author": [
{
"family": "Saber Marouf",
"given": "Babak"
},
{
"family": "Reboreda",
"given": "Antonio"
},
{
"family": "Theissen",
"given": "Frederik"
},
{
"family": "Kaushik",
"given": "Rahul"
},
{
"family": "Sauvage",
"given": "Magdalena"
},
{
"family": "Dityatev",
"given": "Alexander"
},
{
"family": "Yoshida",
"given": "Motoharu"
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "44",
"page": "eaef9907",
"DOI": "10.1126/sciadv.aef9907",
"PMID": "42962002",
"PMCID": "PMC13721955",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aef9907",
"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
30
]
]
}
}

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