Cicchini, G. M., Mikellidou, K. & Burr, D. C. Serial dependence in perception. Annu. Rev. Psychol. 75, 129–154 (2024).
Kiyonaga, A., Scimeca, J. M., Bliss, D. P. & Whitney, D. Serial dependence across perception, attention, and memory. Trends Cogn. Sci. 21, 493–497 (2017).
Manassi, M. & Whitney, D. Continuity fields enhance visual perception through positive serial dependence. Nat. Rev. Psychol. 3, 352–366 (2024).
Hattori, R., Danskin, B., Babic, Z., Mlynaryk, N. & Komiyama, T. Area-specificity and plasticity of history-dependent value coding during learning. Cell 177, 1858–1872.e15 (2019).
Hwang, E. J. et al. Corticostriatal flow of action selection bias. Neuron 104, 1126–1140.e6 (2019).
Akrami, A., Kopec, C. D., Diamond, M. E. & Brody, C. D. Posterior parietal cortex represents sensory history and mediates its effects on behaviour. Nature 554, 368–372 (2018).
Barbosa, J. et al. Interplay between persistent activity and activity-silent dynamics in the prefrontal cortex underlies serial biases in working memory. Nat. Neurosci. 23, 1016–1024 (2020).
Thompson, J. A., Costabile, J. D. & Felsen, G. Mesencephalic representations of recent experience influence decision making. eLife 5, e16572 (2016).
Urai, A. E. & Donner, T. H. Persistent activity in human parietal cortex mediates perceptual choice repetition bias. Nat. Commun. 13, 6015 (2022).
Findling, C. et al. Brain-wide representations of prior information in mouse decision-making. Nature 645, 192–200 (2025).
Morcos, A. S. & Harvey, C. D. History-dependent variability in population dynamics during evidence accumulation in cortex. Nat. Neurosci. 19, 1672–1681 (2016).
Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M. & Keller, P. J. Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10, 413–420 (2013).
Vladimirov, N. et al. Light-sheet functional imaging in fictively behaving zebrafish. Nat. Methods 11, 883–884 (2014).
Mu, Y. et al. Glia accumulate evidence that actions are futile and suppress unsuccessful behavior. Cell 178, 27–43.e19 (2019).
Du, X. et al. Nervous system-wide single-cell morphology atlas of excitatory and inhibitory neurons in larval zebrafish. Preprint at bioRXiv https://doi.org/10.1101/2025.06.06.658008 (2025).
Molano-Mazón, M. et al. Recurrent networks endowed with structural priors explain suboptimal animal behavior. Curr. Biol. 33, 622–638.e7 (2023).
Yu, A. J. & Cohen, J. D. Sequential effects: superstition or rational behavior? Adv. Neural Inf. Process. Syst. 21, 1873–1880 (2008).
Fischer, J. & Whitney, D. Serial dependence in visual perception. Nat. Neurosci. 17, 738–743 (2014).
Braun, A. & Donner, T. H. Adaptive biasing of action-selective cortical build-up activity by stimulus history. eLife 12, RP86740 (2023).
Gupta, D., DePasquale, B., Kopec, C. D. & Brody, C. D. Trial-history biases in evidence accumulation can give rise to apparent lapses in decision-making. Nat. Commun. 15, 662 (2024).
Shiozaki, H. M. & Kazama, H. Parallel encoding of recent visual experience and self-motion during navigation in Drosophila. Nat. Neurosci. 20, 1395–1403 (2017).
Fritsche, M., Spaak, E. & De Lange, F. P. A Bayesian and efficient observer model explains concurrent attractive and repulsive history biases in visual perception. eLife 9, e55389 (2020).
Zhang, H. & Luo, H. Feature-specific reactivations of past information shift current neural encoding thereby mediating serial bias behaviors. PLoS Biol. 21, e3002056 (2023).
Lak, A. et al. Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon. eLife 9, e49834 (2020).
St John-Saaltink, E., Kok, P., Lau, H. C. & De Lange, F. P. Serial dependence in perceptual decisions is reflected in activity patterns in primary visual cortex. J. Neurosci. 36, 6186–6192 (2016).
Ahrens, M. B. et al. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485, 471–477 (2012).
Zhang, Y. et al. Fast and sensitive GCaMP calcium indicators for imaging neural populations. Nature 615, 884–891 (2023).
Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).
Papadimitriou, C., White, R. L. III & Snyder, L. H. Ghosts in the machine II: neural correlates of memory interference from the previous trial. Cereb. Cortex 27, 2513–2527 (2017).
Kaas, J. H. in Evolution of Nervous Systems (ed. Kaas, J. H.) 499–516 (Elsevier, 2007).
Kappel, J. M. et al. Visual recognition of social signals by a tectothalamic neural circuit. Nature 608, 146–152 (2022).
Hageter, J., Starkey, J. & Horstick, E. J. Thalamic regulation of a visual critical period and motor behavior. Cell Rep. 42, 112287 (2023).
Heap, L. A. L., Vanwalleghem, G., Thompson, A. W., Favre-Bulle, I. A. & Scott, E. K. Luminance changes drive directional startle through a thalamic pathway. Neuron 99, 293–301.e4 (2018).
Govorunova, E. G., Sineshchekov, O. A., Janz, R., Liu, X. & Spudich, J. L. Natural light-gated anion channels: a family of microbial rhodopsins for advanced optogenetics. Science 349, 647–650 (2015).
Klapoetke, N. C. et al. Independent optical excitation of distinct neural populations. Nat. Methods 11, 338–346 (2014).
Chaudhuri, R. & Fiete, I. Computational principles of memory. Nat. Neurosci. 19, 394–403 (2016).
Lim, S. & Goldman, M. S. Balanced cortical microcircuitry for maintaining information in working memory. Nat. Neurosci. 16, 1306–1314 (2013).
Compte, A. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10, 910–923 (2000).
Murray, J. D. et al. A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17, 1661–1663 (2014).
Zylberberg, J. & Strowbridge, B. W. Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory. Annu. Rev. Neurosci. 40, 603–627 (2017).
Khona, M. & Fiete, I. R. Attractor and integrator networks in the brain. Nat. Rev. Neurosci. 23, 744–766 (2022).
Inagaki, H. K., Fontolan, L., Romani, S. & Svoboda, K. Discrete attractor dynamics underlies persistent activity in the frontal cortex. Nature 566, 212–217 (2019).
Petrucco, L. et al. Neural dynamics and architecture of the heading direction circuit in zebrafish. Nat. Neurosci. 26, 765–773 (2023).
Dragomir, E. I., Štih, V. & Portugues, R. Evidence accumulation during a sensorimotor decision task revealed by whole-brain imaging. Nat. Neurosci. 23, 85–93 (2020).
Bahl, A. & Engert, F. Neural circuits for evidence accumulation and decision making in larval zebrafish. Nat. Neurosci. 23, 94–102 (2020).
Dunn, T. W. et al. Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion. eLife 5, e12741 (2016).
Wang, X.-J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).
Stein, H. et al. Reduced serial dependence suggests deficits in synaptic potentiation in anti-NMDAR encephalitis and schizophrenia. Nat. Commun. 11, 4250 (2020).
Yang, E. et al. A brainstem integrator for self-location memory and positional homeostasis in zebrafish. Cell 185, 5011–5027.e20 (2022).
Toso, A. et al. History-dependent biases in perceptual decisions depend on NMDA receptors. Preprint at bioRxiv https://doi.org/10.64898/2026.01.12.699039 (2026).
Wang, X.-J. 50 Years of mnemonic persistent activity: quo vadis? Trends Neurosci. 44, 888–902 (2021).
Koulakov, A. A., Raghavachari, S., Kepecs, A. & Lisman, J. E. Model for a robust neural integrator. Nat. Neurosci. 5, 775–782 (2002).
Brody, C. D., Romo, R. & Kepecs, A. Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations. Curr. Opin. Neurobiol. 13, 204–211 (2003).
Boboeva, V., Pezzotta, A., Clopath, C. & Akrami, A. Unifying network model links recency and central tendency biases in working memory. eLife 12, RP86725 (2024).
Shang, C.-F. et al. Real-time analysis of large-scale neuronal imaging enables closed-loop investigation of neural dynamics. Nat. Neurosci. 27, 1014–1018 (2024).
Drieu, C. et al. Rapid emergence of latent knowledge in the sensory cortex drives learning. Nature 641, 960–970 (2025).
Fritsche, M. et al. Temporal regularities shape perceptual decisions and striatal dopamine signals. Nat. Commun. 15, 7093 (2024).
Pellicano, E. & Burr, D. When the world becomes ‘too real’: a Bayesian explanation of autistic perception. Trends Cogn. Sci. 16, 504–510 (2012).
Stringer, C. et al. Rastermap: a discovery method for neural population recordings. Nat. Neurosci. 28, 201–212 (2025).
Jiao, Z.-F. et al. All-optical imaging and manipulation of whole-brain neuronal activities in behaving larval zebrafish. Biomed. Opt. Express 9, 6154–6169 (2018).
Urasaki, A., Asakawa, K. & Kawakami, K. Efficient transposition of the Tol2 transposable element from a single-copy donor in zebrafish. Proc. Natl Acad. Sci. USA 105, 19827–19832 (2008).
Guilbeault, N. C., Guerguiev, J., Martin, M., Tate, I. & Thiele, T. R. BonZeb: open-source, modular software tools for high-resolution zebrafish tracking and analysis. Sci. Rep. 11, 8148 (2021).
Kawashima, T., Zwart, M. F., Yang, C.-T., Mensh, B. D. & Ahrens, M. B. The serotonergic system tracks the outcomes of actions to mediate short-term motor learning. Cell 167, 933–946.e20 (2016).
Li, N., Daie, K., Svoboda, K. & Druckmann, S. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532, 459–464 (2016).
Tubiana, J., Wolf, S., Panier, T. & Debregeas, G. Blind deconvolution for spike inference from fluorescence recordings. J. Neurosci. Methods 342, 108763 (2020).
Tuckwell, H. C. Introduction to Theoretical Neurobiology (Cambridge Univ. Press, 1988).
Wang, C. et al. BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming. eLife 12, e86365 (2023).
Zhao, S., Shan, H. & Mu, Y. A thalamus–brainstem attractor network drives history-biased decisions (2.0.0). Zenodo https://doi.org/10.5281/zenodo.18535967 (2026).

