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MEMORY

Savin, C. Dayan, P. and Lengyel, M. Optimal neural circuit for memory recall unifies conflicting views on recognition memory.

Savin, C., Dayan, P. and Lengyel, M. Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3, PLoS Computational Biology, 10(2): e1003489. doi: 10.1371/journal.pcbi.1003489, 2014. 

Savin, C., Dayan, P. and Lengyel, M. Correlations strike back (again): the case of associative memory retrieval, Advances in Neural Information Processing Systems 26, 2014.

Savin, C., Dayan, P. and Lengyel, M. Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories, Advances in Neural Information Processing Systems 24, 1305-1313, 2011. 

LEARNING, PLASTICITY

Marschall, O. Savin, C. Probing learning through the lens of changes in circuit dynamics, bioRxiv, doi: https://doi.org/10.1101/2023.09.13.557585, 2023.

Bredenberg, E., Williams, E., Savin, C., Richards, B., and Lajoie, G. Formalizing and operationalizing locality for normative plasticity models, Advances In Neural Information Systems, 2023

Bredenberg, E. and Savin, C. Desiderata for normative models of synaptic plasticity, preprint on arXiv: https://arxiv.org/abs/2308.04988, 2023.

Hocker, D., Constantinopole, C.† and Savin, C.†  Curriculum learning inspired by behavioral shaping optimally trains neural networks on complex tasks, 2023 .

Zador et al, Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution, Nature Communications, 2023.

Marschall, O. Savin, C. Probing learning through the lens of changes in circuit dynamics, bioRxiv, doi: https://doi.org/10.1101/2023.09.13.557585, 2023.

Bredenberg, Savin, C*., and Kiani, R.* ,  Recurrent neural circuits overcome partial inactivation by compensation and re-learning, bioarxiv doi: https://doi.org/10.1101/2021.11.12.4682732021, *equal contribution. 

Bredenberg, C., Lyo, B.S.H. Simoncelli, E.P. and Savin, C. Impression learning: Online representation learning with synaptic plasticity, NeurIPS 2021.

Im, D., Savin, C., and Cho, K.,  Online hyperparameter optimization by real-time recurrent learning,  arXiv:2102.07813v1, 2021.

Bredenberg, C., Simoncelli, E*., and Savin, C.* Learning efficient, task-dependent representations with synaptic plasticity, NeurIPS 2020, *equal contribution. 

Marschall, O., Cho, K. and Savin, C. A unified framework of online learning algorithms for training recurrent neural networks, JMLR, 2020 .

Munk, T., Savin, C. and Luecke, J. Optimal Neural Inference of Stimulus Intensities. Scientific Reports, 8(1), 10038.

Munk, T., Savin, C. and Luecke, J. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics , Advances In Neural Information Processing Systems 29, 4278-4286, 2016.

Gilson, M.*, Savin, C.*, Zenke, F.* Emergent neural computation from the interaction of different forms of plasticity, Frontiers Comput. Neuroscience, 2016.

Savin, C., Triesch, J. Emergence of task-dependent representations in working memory circuits, Frontiers Comput. Neurosci. 8:57. doi: 10.3389/fncom.2014.00057, 2014.

Keck*, C., Savin*, C. and Lücke, J. Input normalization and synaptic scaling - two sides of the same coin?, PLoS Computational Biology, 8(3): e1002432, 2012. 

Savin, C., Joshi, P. and Triesch, J. Independent component analysis with spiking neurons. PLoS Computational Biology, 6(4) 2010:e1000757, 2010.

STOCHASTICITY FOR COMPUTATION

Lyo, B. and Savin, C. Complex priors and flexible inference in recurrent circuits with dendritic nonlinearities, bioRxiv, 2023.

Rullan, C, Savin, C. A sampling-based circuit for optimal decision making, NeurIPS 2021.

Bredenberg, C., Lyo, B.S.H. Simoncelli, E.P. and Savin, C. Impression learning: Online representation learning with synaptic plasticity, NeurIPS 2021.

Haimerl, C. , Ruff, D.A., Cohen, M.R., Savin, C.*, and Simoncelli, E.P.* Targeted comodulation supports flexible and accurate decoding in V1, biorxiv, doi: https://doi.org/10.1101/2021.02.23.432351, Nature Communications, 2023. 

Savin, C. Neural representations of uncertainty: the right tool for the right job.

Boeshertz, G., Haimerl, C. and Savin, C. Task adaptation by biologically inspired stochastic comodulation, 2023.

Savin, C.*, Stanciu, O.*, Chiu, C.Q., Lengyel, M** and Fiser, J.** Visual experience fine-tunes the matching of spontaneous and evoked activity during development.

Savin, C. Deneve, S. Distributed spatio-temporal representations of uncertainty in networks of spiking neurons, Advances in Neural Information Processing Systems 27, 2015.

Fiser, J., Lengyel, M. Savin, C., Orban, G. and Berkes, P. How (not) to assess the importance of correlations for the matching of spontaneous and stimulus evoked activity, arXiv 1301.6554 and associated Letter to the Editor in J. Neurosci., 2013.

DATA ANALYSIS

Balzani, E., Noel, J.P., Angelaki, D. and Savin, C. A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation, ICLR 2023.

Noel, JP.*, Balzani, E.*, Savin, C.†, and Angelaki, D.†  Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex, doi: https://doi.org/10.1101/2023.07.30.551169, bioRxiv, 2023 .

Yao, J.D.*, Zemlianova, K.O.*, Hocker, D.L., Savin, C., Constantinople, C.M., Chung, S.Y., Sanes, D. Transformation of acoustic information to sensory decisions in parietal cortex,  PNAS, 2022.

Hocker, D., Brody, C., Savin, C.* and Constantinopole, C.* Subpopulations of neurons in lOFC encode previous and current rewards at time of choice, Elife, 2021, also on bioarxiv, doi: https://doi.org/10.1101/2021.05.06.442972.

Nardini, M, Tkacik, G, Csicsvari J, and Savin, C. The structure of noise correlations in CA1 and its importance for spatial coding across experience, doi: https://doi.org/10.1101/2021.09.28.460602,  J. Neuroscience, 2023.

Herrero-Vidal, P. , Rinberg, D., and Savin, C., Across-animal odor decoding by probabilistic manifold alignment, NeurIPS 2021 (In Press; spotlight, top 3%).

Noel, JP.*, Balzani, E.*, Avila E.*, Lakshminarasimhan, K.J., Bruni, S., Alefantis, P., Savin, C.†, and Angelaki, D.† Coding of latent variables in sensory, parietal, and frontal cortices during closed-loop virtual navigation eLife 11:e80280, 2022. doi: https://doi.org/10.7554/eLife.80280 .

Shor E.*, Herrero-Vidal, P.*, Maliaras G., Savin, C., Bozza, T. and Rinberg, D. Sensitive  and  robust  chemical detection using an olfactory brain-computer interface,  Biosensors and Bioelectronics, Sept. 2021 (*joint first authors).

Balzani, E., Lakshminarasimhan, K.J., Angelaki, D., and Savin, C.  Generalized Additive Models as a tool for quantifying neural tuning during complex behavior, NeurIPS 2020.

Park EH, Keeley S, Savin C, Ranck Jr JB, Fenton AA. How the Internally Organized Direction Sense Is Used to Navigate. Neuron. 2018 Dec 3.

Savin, C. and Tkacik , G. Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology, 2017, 46:120–126.

Savin, C. and Tkacik , G. Estimating nonlinear neural response functions using GP priors and Kronecker methods, Advances In Neural Information Processing Systems 29, 3603-3611, 2016. 

The organization of the material is tentative, as some publications may not fit neatly in one of the main categories. Markings distinguish between mainly computational/theoretical neuroscience work           and data analysis          . Working papers are marked by           Github code links         Suppl. Info. 

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. Do not redistribute without permission.

For a complete list of publication check google Scholar page.

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