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Dynamic sampling of non-stationary spontaneous activity in dissociated neuronal networks

T0 review · 2 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Adaptively reallocating which electrodes record captures 17.2 percentage points more neural activity than picking once and holding still.

desk verdict A solid, practical adaptive-recordings methods paper whose central quantitative claim is probably true but not yet out-of-sample, because the hyperparameters were selected on the same offline recordings used for the headline comparison. read the letter →

arxiv 2607.24269 v1 pith:DZM5EE7M submitted 2026-07-27 q-bio.NC

Kazushi Takehana , Dai Akita , Hirokazu Takahashi This is my paper · ORCID
classification q-bio.NC MSC 92C2062L0562C10
keywords high-densitymicroelectrodearrayadaptiveelectrodeselectionThompsonsamplingnon-stationaryspontaneousactivityPoisson-Gammamodelsynchronizedburstcenter-of-activitytrajectorychannelbudget
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

High-density microelectrode arrays can sense tens of thousands of sites but can record only about a thousand channels at once, so an experimenter must choose which electrodes stay connected. The standard approach — scan the culture, lock onto the most active sites, and keep them fixed — assumes the best electrodes stay the best. This paper shows that assumption fails: across nine 34-hour recordings of dissociated cortical networks, nearly half (47.8%) of the top-100 active electrodes at the end were not among the initially most active. The paper's method treats channel assignment as a sequential decision problem, modeling each electrode's spike count with a discounted Poisson-Gamma posterior and re-allocating the fixed budget every 30 minutes via Thompson sampling. In offline replay against a per-window oracle, this adaptive policy captures 17.2 percentage points more of the achievable spike yield than static selection at the final time point, suggesting that uncertainty-aware exploration can keep long-term recordings aligned with an evolving network.

What carries the argument

The carrying mechanism is the conjugate Poisson-Gamma model with a discounting step and Thompson sampling. Spike counts are modeled as $y_{c,t} \sim \mathrm{Poisson}(\lambda_c \Delta)$ with rate prior $\lambda_c \sim \mathrm{Gamma}(\alpha_c, \beta_c)$, so posterior updates reduce to $\alpha_c \leftarrow \alpha_c + y_{c,t}$ and $\beta_c \leftarrow \beta_c + \Delta$ for observed electrodes. Before each window all statistics are scaled by a discount factor $\rho = 0.7$ ($\alpha \leftarrow \rho\alpha$, $\beta \leftarrow \rho\beta$), which preserves each electrode's estimated mean $\alpha/\beta$ while inflating its uncertainty, so the policy can revisit electrodes whose activity may have drifted. Selection is Thompson sampling: draw one plausible rate $\tilde\lambda_c \sim \mathrm{Gamma}(\alpha_c/\tau, \beta_c/\tau)$ per electrode and route the top-$k$ draws, with temperature $\tau$ tuning exploration. A supporting identity links the spike-count objective to spatial readouts: if the selected electrodes capture fraction $p_s$ of events and the missed-event centroid is $m_c$, then $\|m_s - m\| = \frac{1-p_s}{p_s}\|m - m_c\|$, so raising the captured fraction directly bounds the bias of the center-of-activity trajectory. The evaluation apparatus — dense reference data that allow a per-window oracle subset and an attainment score $100 R_t / R_t^*$ — is what turns the comparison into a quantitative claim.

What would settle it

Re-run the adaptive policy online at full array scale on several cultures, interrupting every few hours for the 26-minute full-array scan needed to compute the true oracle subset: if the attainment gap over static selection disappears, or if the top-1,024 active set turns out to be far more stable than the top-100 set in the 23x23 patch, the downscaled result does not generalize. A cheaper offline check is to replay the same algorithms with candidate sets composed of several spatially separated patches, so that the correlation structure resembles the full array.

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Extended reading notes

Core claim

The paper's central claim is that adaptive electrode selection under a fixed readout budget can track non-stationary spontaneous activity better than fixed or heuristic policies, because the neural signal itself is restless. The evidence is a downscaled offline replay: from nine dense 34 h recordings of 529 electrodes, each algorithm picks 100 electrodes per 30 min window and is scored by the fraction of the oracle top-100 spike yield it captures. The discounted Poisson-Gamma Thompson sampling policy attains the highest oracle-relative score among static, random, and combinatorial epsilon-greedy baselines, and exceeds static selection by 17.2 percentage points at 34 h (two-sided exact paired sign-flip randomization test over the nine recordings, p = 0.004). The paper further documents the non-stationarity that motivates the method — top-100 active-electrode turnover reaches 47.8% by 34 h — and demonstrates in a single online recording that the policy can be executed in real time, capture the first synchronized burst, and support center-of-activity trajectory analysis of later recurrent bursts.

Load-bearing premise

The paper's quantitative evidence comes from a downscaled replay — choosing 100 of 529 electrodes in one local 23x23 patch — and the 17.2 percentage-point advantage is assumed to transfer to the real task of choosing 1,024 of 26,400 electrodes across the whole array, a transfer the single online demonstration does not establish.

Editorial extensions

If this is right

  • Long-term HD-MEA recordings can track evolving activity without repeated full-array scans: reconfiguring the routed subset every 30 minutes keeps the recorded channels near the current activity peak under the same channel budget.
  • For spatial summaries such as center-of-activity trajectories, maximizing captured spike count is aligned with trajectory fidelity: the centroid-error identity shows that a higher captured fraction $p_s$ directly shrinks the bound on centroid bias.
  • Uncertainty-directed exploration, not random exploration, is the load-bearing ingredient: a combinatorial $\varepsilon$-greedy baseline that explores at a fixed random rate does not match the Bayesian method's attainment.
  • Active-set turnover is a usable diagnostic: cultures with high top-100 turnover lose yield under scan-and-commit, and the 47.8% figure gives experimenters a quantitative threshold for deciding when adaptive routing is warranted.
  • The same formulation — temporal discounting plus Thompson sampling under a fixed channel budget — carries over to other high-density recording platforms with more sensing sites than readout channels, with the reward redefined to the relevant objective such as unit yield or information gain.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the 17.2 percentage-point advantage is real at full scale, the natural next comparison the paper does not run is adaptive policy versus periodic full-array rescanning: a 26-minute rescan cycle could itself serve as the exploration mechanism, and the trade-off between losing continuous recording during scans and the policy's partial observability would decide which is preferable.
  • The evaluation selects 100 from a 529-electrode local patch; because the patch is spatially contiguous, its correlation structure may make activity shifts easier or harder to track than across the full 26,400-electrode array — a re-run of the same replay with candidate sets drawn from multiple distant patches would test whether the advantage generalizes.
  • The discount factor $\rho = 0.7$ is fixed; an extension the paper leaves implicit is to tie $\rho$ to the measured turnover or the decorrelation time of the firing-rate maps so the algorithm's forgetting matches the culture's own rate of change, which could improve or stabilize attainment across cultures.
  • The centroid-bias identity predicts a quantitative scaling — centroid error should grow roughly as $(1-p_s)/p_s$; checking that predicted scaling on the dense reference data would validate spike-count maximization as a proxy for preserving spatial summaries, without any new hardware.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. This paper proposes a discounted Poisson–Gamma Thompson sampling algorithm for adaptive electrode selection in high-density microelectrode array (HD-MEA) recordings under a fixed channel budget. The method models per-electrode spike counts with a Poisson–Gamma conjugate model, discounts past observations to handle non-stationarity, and uses Thompson sampling with a temperature parameter to balance exploration and exploitation. The authors evaluate the method by offline replay of nine 34-hour dense recordings, in which 100 electrodes are selected from 529 candidates in a local 23×23 region, and compare it with static, random, and combinatorial ε-greedy baselines using an oracle-relative attainment metric. They report that the proposed method exceeds static selection by 17.2 percentage points at the final time point (two-sided exact paired sign-flip test, p=0.004, n=9). They also present a single representative online recording using 1,024 routed electrodes, in which the method captured the first synchronized burst, and they derive an algebraic relation (Eqs. 16–17) linking the fraction of captured spikes to the centroid bias of the estimated activity center.

Significance. If the central quantitative claim holds, the paper provides a practical and principled solution to a genuine bottleneck in switch-matrix HD-MEA recordings: the readout channel limit. The evaluation has several strengths: it uses an external oracle computed from dense reference data, so the main comparison is not circular by construction; it aggregates nine independent recordings; stochastic policies are averaged over 50 seeds; and the paired comparison uses an exact sign-flip randomization test. The centroid-bias identity (Eqs. 16–17) is algebraically correct and usefully connects spike-capture fraction to a downstream spatial summary. The paper also makes core analysis code publicly available. However, as detailed in the major comments, the out-of-sample validity of the headline improvement is compromised by the apparent selection of hyperparameters on the same offline recordings used for the comparison, and the downscaled evaluation region may not represent the full-array problem. These issues matter because the 17.2 pp advantage and its p-value are the primary evidence for the method's practical value.

major comments (2)
  1. [§3.3 and Table 1] Section 3.3 states that the online recording used 'the Bayesian hyperparameters that performed best in the offline replay,' and Table 1 fixes the same hyperparameter values (ρ=0.7, τ=1.0, α0=5.0, β0=1.0) for the offline replay that produces the headline 17.2 pp improvement. If these values were selected by searching over the same nine recordings used for the sign-flip test, the offline evaluation is not out-of-sample: the tuning process can exploit recording-specific idiosyncrasies, and the p=0.004 result (the minimum attainable for n=9, corresponding to the proposed method winning in all nine recordings) is exactly the pattern a tuned policy would tend to produce even without a true advantage. The manuscript does not disclose a validation split or a hyperparameter-selection protocol, and the supplementary sensitivity analyses are not shown in the main text. This concern is load-bearing because the 17.2 pp gain and its p-value are the paper's primary evidence for adaptive selection; a held-out validation (e.g., tuning on a subset of recordings and testing on the rest, or a documented a priori choice of hyperparameters) is needed to support the central claim.
  2. [§2.3 and Figure 1(F)] The offline evaluation is conducted exclusively in a downscaled setting: 100 electrodes are selected from 529 candidates within a local 23×23 region, whereas the real hardware problem is to select 1,024 from 26,400 electrodes. The paper does not provide evidence that the non-stationarity and spatial correlation structure of this local region are representative of the full array. The single online demonstration (§3.3, n=1) shows feasibility but cannot quantitatively validate the 17.2 pp advantage in the full-array setting. The Discussion (Section 4.3) acknowledges this limitation, but the abstract and conclusion present the 17.2 pp result without this caveat. I recommend either providing supporting analyses (e.g., comparing multiple local regions or coarser full-array maps) or explicitly qualifying the headline claim as a demonstration in a downscaled proxy.
minor comments (4)
  1. [Throughout] There are multiple unresolved placeholder references to 'supplementary table??', 'supplementary figure??', and 'supplementary note??' throughout the text (Sections 2.2, 2.3, 3.1, 3.2, 3.4); these need to be filled in before publication.
  2. [§3.2 and Figure 3(D)] The statistical test is reported only at the final time point (34 h). Since the method's advantage develops over time, consider also reporting a test at an intermediate time point or a summary of the full temporal curve, to avoid relying on a single (potentially most favorable) time point.
  3. [§2.3] The statement that turnover is '0% when the same top-k electrodes are retained' should clarify that the measure uses the top-k set, not the ordering within that set.
  4. [Data Availability] The code availability statement says the repository 'will be updated with the final analysis scripts upon publication'; if the analysis code is not yet fully available, this should be stated clearly in the main text.

Circularity Check

1 steps flagged · score 4.0 of 10

Hyperparameters selected on the offline replay are reused for the same offline replay's headline comparison, so the reported 17.2 pp advantage is partly a fitted outcome.

  1. fitted input called prediction [Section 3.3 (Representative online recording), Table 1, and Section 3.2 / Figure 3(D)]
    "Parameters used for offline replay and the representative online recording. ... the same Bayesian update interval and hyperparameters were used. ... the routed electrode configuration was updated every 30 min using the Bayesian hyperparameters that performed best in the offline replay; the online channel budget was 1,024 electrodes (table 1)."

    The headline offline result (17.2 pp over static, p=0.004, Fig. 3D) is produced with the same hyperparameters (ρ=0.7, τ=1.0, α0=5.0, β0=1.0, Table 1) that Section 3.3 says 'performed best in the offline replay.' As written, the parameter vector is selected by performance on the same offline recordings that are then used for the reported comparison, so the offline replay is not an out-of-sample test. The exact sign-flip p=0.004 is the minimum attainable for n=9 (2/512), meaning the method beat static in all nine recordings, which is exactly the pattern a tuned policy can produce even without a true advantage. Because no validation split or parameter-selection protocol is disclosed, the central quantitative claim is partially a fitted outcome rather than an independent prediction.

full rationale

The paper's core derivation is self-contained and not circular by construction: the proposed method is compared against an external oracle computed from dense reference data, and the centroid bound in Eq. 17 is an algebraic identity rather than a load-bearing circular step. I found no load-bearing self-citation chain; citations to the authors' prior work support background and burst-detection settings, not the adaptive-selection advantage. The one substantial circular element is in the evaluation protocol: the hyperparameters used for the offline replay that produces the headline 17.2 pp / p=0.004 result are described as 'the Bayesian hyperparameters that performed best in the offline replay,' and the same values are then used for the offline evaluation. Unless a distinct validation split or an a priori parameter choice is disclosed, the main quantitative comparison is partly fitted to the test data. This lowers confidence in the headline effect but does not make the method's derivation itself circular, so the score is moderate rather than high.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

No new entities are postulated. The model relies on standard bandit machinery (Poisson-Gamma conjugacy, Thompson sampling) and four tuning parameters whose values are given in Table 1. The discount factor is the most consequential free parameter, and the paper's language suggests it may have been tuned on the same offline data.

free parameters (5)
  • Discount factor rho = 0.7
    Controls how quickly old spike-count observations are forgotten (Eq. 10). The paper says the online run used hyperparameters that performed best in the offline replay, suggesting rho was selected on these recordings.
  • Prior shape alpha_0 = 5.0
    Initial Gamma shape for all electrodes (Table 1), affects the exploration-exploitation balance early in the recording.
  • Prior rate beta_0 = 1.0 s
    Initial Gamma rate (Table 1), sets the prior mean firing rate of 5 spikes/s before any observation.
  • Temperature tau = 1.0
    Scales the Gamma distribution in Thompson sampling (Eq. 12); larger tau increases exploration. Selected as part of the offline setting.
  • Epsilon (epsilon-greedy baseline) = 0.2
    Exploration probability for the combinatorial epsilon-greedy baseline; not part of the proposed method but affects the comparison.
assumptions (5)
  • domain assumption Spike counts on each electrode follow a Poisson distribution with a single rate parameter lambda_c.
    Used in Eq. 7 to define the likelihood; the paper does not verify this against refractory or burst statistics.
  • domain assumption Electrodes are independent; the activity model ignores spatial correlations.
    Stated in Section 4.3 as a limitation; the prior and updates in Eqs. 8-11 treat each electrode separately.
  • domain assumption Exponential discounting of sufficient statistics with a fixed rho correctly captures the non-stationarity of spontaneous activity.
    Eq. 10 applies the same discount to all electrodes; the choice of rho=0.7 is not independently justified.
  • domain assumption The oracle subset, the top-k electrodes by realized spike count in the same window, is the correct benchmark for recording usefulness.
    Eq. 3 defines the oracle using the full recording; the paper adopts total spike count as the reward in Section 2.3.
  • standard math Standard conjugacy of the Gamma prior with the Poisson likelihood.
    Used implicitly in the posterior update, Eq. 11.

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Cite this review

Pith. "Pith review of Dynamic sampling of non-stationary spontaneous activity in dissociated neuronal networks." pith.science (2026). https://pith.science/paper/DZM5EE7M

@misc{pith2026260724269,
  author       = {Pith},
  title        = {Pith review of: Dynamic sampling of non-stationary spontaneous activity in dissociated neuronal networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DZM5EE7M}},
  note         = {Machine review of arXiv:2607.24269}
}
read the original abstract

Objective. To develop and evaluate an adaptive electrode-selection method for tracking non-stationary spontaneous activity during long-term high-density microelectrode array (HD-MEA) recordings under a fixed channel budget. Approach. We formulated electrode allocation as a sequential subset-selection problem and used a discounted Poisson-Gamma model with Thompson sampling. The method updated electrode-specific activity estimates from observed spike counts and reallocated a fixed channel budget over time. We evaluated it by offline replay of nine 34 h HD-MEA recordings, selecting 100 electrodes from 529 densely routed candidates, and in a representative online recording using 1,024 routed electrodes. Main results. Across offline recordings, the top 100 active-electrode set changed substantially, reaching 47.8% turnover at 34 h. The Bayesian method captured the largest fraction of the spikes available to an oracle selector among the tested strategies and exceeded static selection by 17.2 percentage points at the final time point. In the online recording, adaptive selection captured the first synchronized burst and supported center-of-activity trajectory analysis. Significance. Uncertainty-aware exploration and temporal discounting can improve HD-MEA recording efficiency under fixed readout constraints, providing a basis for adaptive sensing of evolving neural activity.

Figures

Figures reproduced from arXiv: 2607.24269 by the authors.

Figure 1
Figure 1. Experimental setup and offline evaluation framework. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of the adaptive electrode selection framework. Red electrodes indicate the subset currently [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Non-stationarity of active electrodes and offline evaluation of adaptive electrode selection. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Representative online adaptive recording during network-burst emergence. Recording began 30 h after cell [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

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Works this paper leans on

42 extracted references · 30 canonical work pages

  1. [1]

    C. A. Thomas, P. A. Springer, G. E. Loeb, Y . Berwald-Netter, and L. M. Okun. A miniature microelectrode array to monitor the bioelectric activity of cultured cells.Experimental Cell Research, 74(1):61–66, 1972. doi:10.1016/0014-4827(72)90481-8

    work page doi:10.1016/0014-4827(72)90481-8 1972

  2. [2]

    Electrical stimulation and recording from cultured neurons using a planar electrode array.Bioelectrochemistry and Bioenergetics, 29(2):193–204, 1992

    Yasuhiko Jimbo and Akio Kawana. Electrical stimulation and recording from cultured neurons using a planar electrode array.Bioelectrochemistry and Bioenergetics, 29(2):193–204, 1992. doi:10.1016/0302-4598(92)80067- Q

    work page doi:10.1016/0302-4598(92)80067- 1992

  3. [3]

    Re- vealing neuronal function through microelectrode array recordings.Frontiers in Neuroscience, 8:423, 2015

    Marie Engelene J Obien, Kosmas Deligkaris, Torsten Bullmann, Douglas J Bakkum, and Urs Frey. Re- vealing neuronal function through microelectrode array recordings.Frontiers in Neuroscience, 8:423, 2015. doi:10.3389/fnins.2014.00423

    arXiv 2015

  4. [4]

    Long-term characteri- zation of firing dynamics of spontaneous bursts in cultured neural networks.IEEE Transactions on Biomedical Engineering, 51(11):2051–2062, 2004

    Jaap Van Pelt, Pieter S Wolters, Michael A Corner, Wim LC Rutten, and Ger JA Ramakers. Long-term characteri- zation of firing dynamics of spontaneous bursts in cultured neural networks.IEEE Transactions on Biomedical Engineering, 51(11):2051–2062, 2004. doi:10.1109/TBME.2004.827936

    work page arXiv 2004

  5. [5]

    Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development.Brain research, 1093(1):41–53, 2006

    Michela Chiappalone, Marco Bove, Alessandro Vato, Mariateresa Tedesco, and Sergio Martinoia. Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development.Brain research, 1093(1):41–53, 2006. doi:10.1016/j.brainres.2006.03.049

    work page doi:10.1016/j.brainres.2006.03.049 2006

  6. [6]

    An extremely rich repertoire of bursting patterns during the development of cortical cultures.BMC neuroscience, 7(1):11, 2006

    Daniel A Wagenaar, Jerome Pine, and Steve M Potter. An extremely rich repertoire of bursting patterns during the development of cortical cultures.BMC neuroscience, 7(1):11, 2006. doi:10.1186/1471-2202-7-11

    doi:10.1186/1471-2202-7-11 2006

  7. [7]

    Self- organized criticality in developing neuronal networks.PLoS computational biology, 6(12):e1001013, 2010

    Christian Tetzlaff, Samora Okujeni, Ulrich Egert, Florentin Wörgötter, and Markus Butz. Self- organized criticality in developing neuronal networks.PLoS computational biology, 6(12):e1001013, 2010. doi:10.1371/journal.pcbi.1001013

    doi:10.1371/journal.pcbi.1001013 2010

  8. [8]

    Early prediction of developing spontaneous activity in cultured neuronal networks

    David Cabrera-Garcia, Davide Warm, Pablo de la Fuente, M Teresa Fernández-Sánchez, Antonello Novelli, and Joaquín M Villanueva-Balsera. Early prediction of developing spontaneous activity in cultured neuronal networks. Scientific reports, 11(1):20407, 2021. doi:10.1038/s41598-021-99538-9

    work page doi:10.1038/s41598-021-99538-9 2021

Show all 42 references
  1. [9]

    Development of neural population activity toward self-organized criticality

    Yuichiro Yada, Takeshi Mita, Akihiro Sanada, Ryuichi Yano, Ryohei Kanzaki, Douglas J Bakkum, Andreas Hierlemann, and Hirokazu Takahashi. Development of neural population activity toward self-organized criticality. Neuroscience, 343:55–65, 2017. doi:10.1016/j.neuroscience.2016.11.031

    2017 doi

  2. [10]

    Persistent dynamic attractors in activity patterns of cultured neuronal networks.Physical Review E, 73(5):051907, 2006

    Daniel A Wagenaar, Zoltan Nadasdy, and Steve M Potter. Persistent dynamic attractors in activity patterns of cultured neuronal networks.Physical Review E, 73(5):051907, 2006. doi:10.1103/PhysRevE.73.051907

    2006 doi

  3. [11]

    Network bursting dynamics in excitatory cortical neuron cul- tures results from the combination of different adaptive mechanisms.PLOS ONE, 8(10):e75824, 2013

    Timothée Masquelier and Gustavo Deco. Network bursting dynamics in excitatory cortical neuron cul- tures results from the combination of different adaptive mechanisms.PLOS ONE, 8(10):e75824, 2013. doi:10.1371/journal.pone.0075824

    2013 doi

  4. [12]

    Dissociated neuronal cultures as model systems for self-organized prediction.Frontiers in Neural Circuits, 19: 1568652, 2025

    Amit Yaron, Zhuo Zhang, Dai Akita, Tomoyo Isoguchi Shiramatsu, Zenas C Chao, and Hirokazu Takahashi. Dissociated neuronal cultures as model systems for self-organized prediction.Frontiers in Neural Circuits, 19: 1568652, 2025. doi:10.3389/fncir.2025.1568652

    2025

  5. [13]

    Deviance detection and regularity sensitivity in dissociated neuronal cultures.Frontiers in Neural Circuits, 19: 1584322, 2025

    Zhuo Zhang, Amit Yaron, Dai Akita, Tomoyo Isoguchi Shiramatsu, Zenas C Chao, and Hirokazu Takahashi. Deviance detection and regularity sensitivity in dissociated neuronal cultures.Frontiers in Neural Circuits, 19: 1584322, 2025. doi:10.3389/fncir.2025.1584322. 13 Dynamic sampl...

    2025

  6. [14]

    Emergent functions of noise-driven spontaneous activity: homeostatic maintenance of criticality and memory consolidation.Frontiers in Neural Circuits, 19:1585087, 2025

    Narumitsu Ikeda, Dai Akita, and Hirokazu Takahashi. Emergent functions of noise-driven spontaneous activity: homeostatic maintenance of criticality and memory consolidation.Frontiers in Neural Circuits, 19:1585087, 2025. doi:10.3389/fncir.2025.1585087

    2025

  7. [15]

    Switch-matrix-based high-density microelectrode array in cmos technology.IEEE Journal of Solid-State Circuits, 45(2):467–482, 2010

    Urs Frey, Jan Sedivy, Flavio Heer, Rene Pedron, Marco Ballini, Jan Mueller, Douglas Bakkum, Sadik Hafizovic, Francesca D Faraci, Frauke Greve, et al. Switch-matrix-based high-density microelectrode array in cmos technology.IEEE Journal of Solid-State Circuits, 45(2):467–482, 2...

    2010

  8. [16]

    Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites.Nature Communications, 4:2181, 2013

    Douglas J Bakkum, Urs Frey, Milos Radivojevic, Thomas L Russell, Jan Müller, Michele Fiscella, Hirokazu Taka- hashi, and Andreas Hierlemann. Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites.Nature Communications, 4:21...

    2013 doi

  9. [17]

    A 1024-channel cmos microelectrode array with 26,400 electrodes for recording and stimulation of electrogenic cells in vitro.IEEE journal of solid-state circuits, 49(11):2705–2719,

    Marco Ballini, Jan Müller, Paolo Livi, Yihui Chen, Urs Frey, Alexander Stettler, Amir Shadmani, Vijay Viswam, Ian Lloyd Jones, David Jäckel, et al. A 1024-channel cmos microelectrode array with 26,400 electrodes for recording and stimulation of electrogenic cells in vitro.IEEE...

  10. [18]

    High-resolution cmos mea platform to study neurons at subcellular, cellular, and network levels.Lab on a Chip, 15(13):2767–2780, 2015

    Jan Müller, Marco Ballini, Paolo Livi, Yihui Chen, Milos Radivojevic, Amir Shadmani, Vijay Viswam, Ian L Jones, Michele Fiscella, Roland Diggelmann, et al. High-resolution cmos mea platform to study neurons at subcellular, cellular, and network levels.Lab on a Chip, 15(13):276...

    2015 doi

  11. [19]

    Obien and Urs Frey

    Marie Engelene J. Obien and Urs Frey. Large-scale, high-resolution microelectrode arrays for interrogation of neurons and networks. In Michela Chiappalone, Valentina Pasquale, and Monica Frega, editors,In Vitro Neuronal Networks, volume 22 ofAdvances in Neurobiology, pages 83–...

    2019 doi

  12. [20]

    State-dependent propagation of neuronal sub-population in spontaneous synchronized bursts.Frontiers in Systems Neuroscience, 10:28, 2016

    Yuichiro Yada, Ryohei Kanzaki, and Hirokazu Takahashi. State-dependent propagation of neuronal sub-population in spontaneous synchronized bursts.Frontiers in Systems Neuroscience, 10:28, 2016. doi:10.3389/fnsys.2016.00028

    2016

  13. [21]

    Locally embedded presages of global network bursts.Proceedings of the National Academy of Sciences, 114(36):9517–9522, 2017

    Satohiro Tajima, Takeshi Mita, Douglas J Bakkum, Hirokazu Takahashi, and Taro Toyoizumi. Locally embedded presages of global network bursts.Proceedings of the National Academy of Sciences, 114(36):9517–9522, 2017. doi:10.1073/pnas.1705981114

    2017 doi

  14. [22]

    Recording strategies for high channel count, densely spaced microelectrode arrays.Frontiers in Neuroscience, 15:681085, 2021

    Norberto Pérez-Prieto and Manuel Delgado-Restituto. Recording strategies for high channel count, densely spaced microelectrode arrays.Frontiers in Neuroscience, 15:681085, 2021. doi:10.3389/fnins.2021.681085

    2021

  15. [23]

    Luca Berdondini, Kilian Imfeld, Alessandro Maccione, Mariateresa Tedesco, Simon Neukom, Milena Koudelka- Hep, and Sergio Martinoia. Active pixel sensor array for high spatio-temporal resolution electrophysiolog- ical recordings from single cell to large scale neuronal networks...

    2009 doi

  16. [24]

    David Tsai, Daniel Sawyer, Adrian Bradd, Rafael Yuste, and Kenneth L. Shepard. A very large-scale microelectrode array for cellular-resolution electrophysiology.Nature Communications, 8(1):1802, 2017. doi:10.1038/s41467-017-02009-x

    2017 doi

  17. [25]

    Large-area field potential imaging having single neuron resolution using 236 880 electrodes cmos-mea technology

    Ikuro Suzuki, Naoki Matsuda, Xiaobo Han, Shuhei Noji, Mikako Shibata, Nami Nagafuku, and Yuto Ishibashi. Large-area field potential imaging having single neuron resolution using 236 880 electrodes cmos-mea technology. Advanced Science, 10(20):2207732, 2023. doi:10.1002/advs.202207732

    2023 doi

  18. [26]

    Chronic co-variation of neural network configuration and activity in mature dissociated cultures

    Satoru Okawa, Takeshi Mita, Douglas Bakkum, Urs Frey, Andreas Hierlemann, Ryohei Kanzaki, and Hirokazu Takahashi. Chronic co-variation of neural network configuration and activity in mature dissociated cultures. Electronics and Communications in Japan, 98(5):34–42, 2015. doi:1...

    2015 doi

  19. [27]

    Long-term morphological and functional dynamics of human stem cell-derived neuronal networks on high-density micro- electrode arrays.Frontiers in Neuroscience, 16:951964, 2022

    Rouhollah Habibey, Johannes Striebel, Felix Schmieder, Jürgen Czarske, and V olker Busskamp. Long-term morphological and functional dynamics of human stem cell-derived neuronal networks on high-density micro- electrode arrays.Frontiers in Neuroscience, 16:951964, 2022. doi:10....

    2022

  20. [28]

    Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures.Neuroscience, 148(1):294–303, 2007

    John D Rolston, Daniel A Wagenaar, and Steve M Potter. Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures.Neuroscience, 148(1):294–303, 2007. doi:10.1016/j.neuroscience.2007.05.025

    2007 doi

  21. [29]

    Zenas C Chao, Douglas J Bakkum, and Steve M Potter. Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (cat) with other statistics.Journal of Neural Engineering, 4(3):294–308, 2007. doi:10.1088/1741-256...

    2007 doi

  22. [30]

    Charles, and Bijan Pesaran

    John Choi, Krishan Kumar, Mohammad Khazali, Katie Wingel, Mahdi Choudhury, Adam S. Charles, and Bijan Pesaran. Optimal adaptive electrode selection to maximize simultaneously recorded neuron yield. InAdvances in Neural Information Processing Systems, volume 33, pages 6160–6171...

    2020

  23. [31]

    Controlling bursting in corti- cal cultures with closed-loop multi-electrode stimulation.Journal of Neuroscience, 25(3):680–688, 2005

    Daniel A Wagenaar, Radhika Madhavan, Jerome Pine, and Steve M Potter. Controlling bursting in corti- cal cultures with closed-loop multi-electrode stimulation.Journal of Neuroscience, 25(3):680–688, 2005. doi:10.1523/JNEUROSCI.4209-04.2005

    2005 doi

  24. [32]

    Schweiger, Sebastian Hernandez, Alex Spaeth, Kateryna V oitiuk, David F

    Ash Robbins, Hunter E. Schweiger, Sebastian Hernandez, Alex Spaeth, Kateryna V oitiuk, David F. Parks, Tjitse van der Molen, Jinghui Geng, Isabel Cline, Kenneth S. Kosik, Sofie R. Salama, Tal Sharf, Mohammed A. Mostajo- Radji, David Haussler, and Mircea Teodorescu. Goal-direct...

    2026

  25. [33]

    Finite-time analysis of the multiarmed bandit problem

    Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2):235–256, 2002. doi:10.1023/A:1013689704352

    2002 doi

  26. [34]

    An empirical evaluation of thompson sampling

    Olivier Chapelle and Lihong Li. An empirical evaluation of thompson sampling. InAdvances in Neural Information Processing Systems, volume 24, pages 2249–2257, 2011

    2011

  27. [35]

    Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen

    Daniel J. Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen. A tutorial on thompson sampling.Foundations and Trends in Machine Learning, 11(1):1–96, 2018. doi:10.1561/2200000070

    2018 doi

  28. [36]

    On upper-confidence bound policies for switching bandit problems

    Aurélien Garivier and Eric Moulines. On upper-confidence bound policies for switching bandit problems. In Algorithmic Learning Theory, volume 6925 ofLecture Notes in Computer Science, pages 174–188. Springer,

  29. [37]

    Thompson sampling for non-stationary bandit problems.Entropy, 27(1):51, 2025

    Han Qi, Fei Guo, and Li Zhu. Thompson sampling for non-stationary bandit problems.Entropy, 27(1):51, 2025. doi:10.3390/e27010051

    2025 doi

  30. [38]

    Cambridge University Press, 2020

    Tor Lattimore and Csaba Szepesvári.Bandit Algorithms. Cambridge University Press, 2020. doi:10.1017/9781108571401

    2020 doi

  31. [39]

    Parameters for burst detection.Frontiers in Computational Neuroscience, 7:193, 2014

    Douglas J Bakkum, Milos Radivojevic, Urs Frey, Felix Franke, Andreas Hierlemann, and Hirokazu Takahashi. Parameters for burst detection.Frontiers in Computational Neuroscience, 7:193, 2014. doi:10.3389/fncom.2013.00193

    2014

  32. [40]

    Jun, Nicholas A

    James J. Jun, Nicholas A. Steinmetz, Joshua H. Siegle, Daniel J. Denman, Marius Bauza, Brian Barbarits, Albert K. Lee, Costas A. Anastassiou, Alexandru Andrei, Cagatay Aydin, Mladen Barbic, Timothy J. Blanche, Vincent Bonin, Joao Couto, Barundeb Dutta, Sergey L. Gratiy, Diego ...

    2017

  33. [2011]

    doi:10.1007/978-3-642-24412-4_16

    doi

  34. [2014]

    doi:10.1109/JSSC.2014.2359219

    2014

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.