Publications - Salk Institute for Biological Studies

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Alvarez-Kuglen, M., Ninomiya, K., Qin, H., Rodriguez, D., Fiengo, L., Farhy, C., Hsu, W.M., Kirk, B., Havas, A., Feng, G.S., Roberts, A.J., Anderson, R.M., Serrano, M., Adams, P.D., Sharpee, T.O., Terskikh, A.V. ImAge quantitates aging and rejuvenation. (2024) Nat Aging. DOI: 10.1038/s43587-024-00685-1

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Alvarez-Kuglen, M., Rodriguez, D., Qin, H., Ninomiya, K., Fiengo, L., Farhy, C., Hsu, W.M., Havas, A., Feng, G.S., Roberts, A.J., Anderson, R.M., Serrano, M., Adams, P.D., Sharpee, T.O., Terskikh, A.V. Imaging-based chromatin and epigenetic age, ImAge, quantitates aging and rejuvenation. (2023) Res Sq. DOI: 10.21203/rs.3.rs-3479973/v1

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Little, J.C., Kaaronen, R.O., Hukkinen, J.I., Xiao, S., Sharpee, T., Farid, A.M., Nilchiani, R., Barton, C.M. Earth Systems to Anthropocene Systems: An Evolutionary, System-of-Systems, Convergence Paradigm for Interdependent Societal Challenges. (2023) Environ Sci Technol. DOI: 10.1021/acs.est.2c06203

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Yang, J.Y., O'Connell, T.F., Hsu, W.M., Bauer, M.S., Dylla, K.V., Sharpee, T.O., Hong, E.J. Restructuring of olfactory representations in the fly brain around odor relationships in natural sources. (2023) bioRxiv. DOI: 10.1101/2023.02.15.528627

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Zhang, H., Rich, P.D., Lee, A.K., Sharpee, T.O. Hippocampal spatial representations exhibit a hyperbolic geometry that expands with experience. (2022) Nature Neuroscience. DOI: 10.1038/s41593-022-01212-4

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Ghaninia, M., Zhou, Y., Knauer, A.C., Schiestl, F.P., Sharpee, T.O., Smith, B.H. Hyperbolic odorant mixtures as a basis for more efficient signaling between flowering plants and bees. (2022) PLOS One. 17(7):e0270358. DOI: 10.1371/journal.pone.0270358

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Zhou, Y., Sharpee, T.O. Using Global t-SNE to Preserve Intercluster Data Structure. (2022) Neural Computation.:1-15 DOI: 10.1162/neco_a_01504

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Nowotny, T., van Albada, S.J., Fellous, J.M., Haas, J.S., Jolivet, R.B., Metzner, C., Sharpee, T. Editorial: Advances in Computational Neuroscience. (2022) Frontiers in Computational Neuroscience. 15:824899. DOI: 10.3389/fncom.2021.824899

Shadel, G.S., Adams, P.D., Berggren, W.T., Diedrich, J.K., Diffenderfer, K.E., Gage, F.H., Hah, N., Hansen, M., Hetzer, M.W., Molina, A.J.A., Manor, U., Marek, K., O'Keefe, D.D., Pinto, A.F.M., Sacco, A., Sharpee, T.O., Shokriev, M.N., Zambetti, S. The San Diego Nathan Shock Center: tackling the heterogeneity of aging. (2021) Geroscience. DOI: 10.1007/s11357-021-00426-x

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Understanding basic mechanisms of aging holds great promise for developing interventions that prevent or delay many age-related declines and diseases simultaneously to increase human healthspan. However, a major confounding factor in aging research is the heterogeneity of the aging process itself. At the organismal level, it is clear that chronological age does not always predict biological age or susceptibility to frailty or pathology. While genetics and environment are major factors driving variable rates of aging, additional complexity arises because different organs, tissues, and cell types are intrinsically heterogeneous and exhibit different aging trajectories normally or in response to the stresses of the aging process (e.g., damage accumulation). Tackling the heterogeneity of aging requires new and specialized tools (e.g., single-cell analyses, mass spectrometry-based approaches, and advanced imaging) to identify novel signatures of aging across scales. Cutting-edge computational approaches are then needed to integrate these disparate datasets and elucidate network interactions between known aging hallmarks. There is also a need for improved, human cell-based models of aging to ensure that basic research findings are relevant to human aging and healthspan interventions. The San Diego Nathan Shock Center (SD-NSC) provides access to cutting-edge scientific resources to facilitate the study of the heterogeneity of aging in general and to promote the use of novel human cell models of aging. The center also has a robust Research Development Core that funds pilot projects on the heterogeneity of aging and organizes innovative training activities, including workshops and a personalized mentoring program, to help investigators new to the aging field succeed. Finally, the SD-NSC participates in outreach activities to educate the general community about the importance of aging research and promote the need for basic biology of aging research in particular.

Hsu, W.M., Kastner, D.B., Baccus, S.A., Sharpee, T.O. How inhibitory neurons increase information transmission under threshold modulation. (2021) Cell Reports. 35(8):109158. DOI: 10.1016/j.celrep.2021.109158

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Zhou, Y., Sharpee, T.O. Hyperbolic geometry of gene expression. (2021) iScience. 24(3):102225. DOI: 10.1016/j.isci.2021.102225

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Sharpee, T.O., Berkowitz, J.A. Linking neural responses to behavior with information-preserving population vectors (2019) Current Opinion in Behavioral Sciences. 29:37-44. DOI: https://doi.org/10.1016/j.cobeha.2019.03.004

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Amatya, D.N., Linker, S.B., Mendes, A.P.D., Santos, R., Erikson, G., Shokhirev, M.N., Zhou, Y., Sharpee, T., Gage, F.H., Marchetto, M.C., Kim, Y. Dynamical Electrical Complexity Is Reduced during Neuronal Differentiation in Autism Spectrum Disorder. (2019) Stem Cell Reports. DOI: 10.1016/j.stemcr.2019.08.001

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Sharpee, T.O. An argument for hyperbolic geometry in neural circuits. (2019) Current Opinion in Neurobiology. 58:101-104. DOI: 10.1016/j.conb.2019.07.008

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Berkowitz, J.A., Sharpee, T.O. Quantifying Information Conveyed by Large Neuronal Populations. (2019) Neural Computation.:1-33 DOI: 10.1162/neco_a_01193

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Zhou, Y., Smith, B.H., Sharpee, T.O. Hyperbolic geometry of the olfactory space. (2018) Science Advances. 4(8):eaaq1458. DOI: 10.1126/sciadv.aaq1458

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Zhang, Y., Kastner, D.B., Baccus, S.A., Sharpee, T.O. Optimal Information Transmission by Overlapping Retinal Cell Mosaics. (2018) Proc Conf Inf Sci Syst. 2018. DOI: 10.1109/ciss.2018.8362310

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Sharpee, T.O. (2018) Oxford Research Encyclopedias, Neuroscience.:1-23 DOI: doi.org/10.1093/acrefore/9780190264086.013.110

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Sensory systems exist to provide an organism with information about the state of the environment that can be used to guide future actions and decisions. Remarkably, two conceptually simple yet general theorems from information theory can be used to evaluate the performance of any sensory system. One theorem states that there is a minimal amount of energy that an organism has to spend in order to capture a given amount of information about the environment. The second theorem states that the maximum rate with which the organism can acquire resources from the environment, relative to its competitors, is limited by the information this organism collects about the environment, also relative to its competitors. These two theorems provide a scaffold for formulating and testing general principles of sensory coding but leave unanswered many important practical questions of implementation in neural circuits. These implementation questions have guided thinking in entire subfields of sensory neuroscience, and include: What features in the sensory environment should be measured? Given that we make decisions on a variety of time scales, how should one solve trade-offs between making simpler measurements to guide minimal decisions vs. more elaborate sensory systems that have to overcome multiple delays between sensation and action. Once we agree on the types of features that are important to represent, how should they be represented? How should resources be allocated between different stages of processing, and where is the impact of noise most damaging? Finally, one should consider trade-offs between implementing a fixed strategy vs. an adaptive scheme that readjusts resources based on current needs. Where adaptation is considered, under what conditions does it become optimal to switch strategies? Research over the past 60 years has provided answers to almost all of these questions but primarily in early sensory systems. Joining these answers into a comprehensive framework is a challenge that will help us understand who we are and how we can make better use of limited natural resources.

Atencio, C.A., Sharpee, T.O. Multidimensional receptive field processing by cat primary auditory cortical neurons. (2017) Neuroscience. 359:130-141. DOI: 10.1016/j.neuroscience.2017.07.003

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Sharpee, T.O. On texture, form, and fixational eye movements. (2017) Current Opinion in Neurobiology. 46:228-233. DOI: 10.1016/j.conb.2017.09.002

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Kaardal, J.T., Theunissen, F.E., Sharpee, T.O. A Low-Rank Method for Characterizing High-Level Neural Computations. (2017) Frontiers in Computational Neuroscience. 11:68. DOI: 10.3389/fncom.2017.00068

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Sharpee, T.O. Optimizing Neural Information Capacity through Discretization. (2017) Neuron. 94(5):954-960. DOI: 10.1016/j.neuron.2017.04.044

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Rowekamp, R.J., Sharpee, T.O. Cross-orientation suppression in visual area V2. (2017) Nature Communications. 8:15739. DOI: 10.1038/ncomms15739

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Sharpee, T.O. Plasticity to the Rescue. (2016) Neuron. 92(5):935-936. DOI: 10.1016/j.neuron.2016.11.042

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Kuczala, A., Sharpee, T.O. Eigenvalue spectra of large correlated random matrices. (2016) Phys Rev E. 94(5-1):050101. DOI: 10.1103/PhysRevE.94.050101

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Sharpee, T.O. How Invariant Feature Selectivity Is Achieved in Cortex. (2016) Front Synaptic Neurosci. 8:26. DOI: 10.3389/fnsyn.2016.00026

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Sharpee, T.O. Taking a close look at electrosensing. (2016) Elife. 5. DOI: 10.7554/eLife.16209

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Zhang, Y., Sharpee, T.O. A Robust Feedforward Model of the Olfactory System. (2016) PLOS Computational Biology. 12(4):e1004850. DOI: 10.1371/journal.pcbi.1004850

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Aljadeff, J., Renfrew, D., Vegué, M., Sharpee, T.O. Low-dimensional dynamics of structured random networks. (2016) Phys Rev E. 93(2):022302. DOI: 10.1103/PhysRevE.93.022302

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Hinckley, C.A., Alaynick, W.A., Gallarda, B.W., Hayashi, M., Hilde, K.L., Driscoll, S.P., Dekker, J.D., Tucker, H.O., Sharpee, T.O., Pfaff, S.L. Spinal Locomotor Circuits Develop Using Hierarchical Rules Based on Motorneuron Position and Identity. (2015) Neuron. 87(5):1008-21. DOI: 10.1016/j.neuron.2015.08.005

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Calhoun, A.J., Tong, A., Pokala, N., Fitzpatrick, J.A., Sharpee, T.O., Chalasani, S.H. Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans. (2015) Neuron. 86(2):428-41. DOI: 10.1016/j.neuron.2015.03.026

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Aljadeff, J., Stern, M., Sharpee, T. Transition to chaos in random networks with cell-type-specific connectivity. (2015) Phys. Rev. Lett. 114(8):088101. DOI: 10.1103/PhysRevLett.114.088101

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Kastner, D.B., Baccus, S.A., Sharpee, T.O. Critical and maximally informative encoding between neural populations in the retina. (2015) Proceedings of the National Academy of Sciences of the United States of America. 112(8):2533-8. DOI: 10.1073/pnas.1418092112

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Calhoun, A.J., Chalasani, S.H., Sharpee, T.O. Maximally informative foraging by Caenorhabditis elegans. (2014) Elife. 3. DOI: 10.7554/eLife.04220

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Sharpee, T.O. Toward functional classification of neuronal types. (2014) Neuron. 83(6):1329-34. DOI: 10.1016/j.neuron.2014.08.040

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Sharpee, T.O. Function determines structure in complex neural networks. (2014) Proceedings of the National Academy of Sciences of the United States of America. 111(23):8327-8. DOI: 10.1073/pnas.1407198111

Sharpee, T.O., Calhoun, A.J., Chalasani, S.H. Information theory of adaptation in neurons, behavior, and mood. (2014) Current Opinion in Neurobiology. 25:47-53. DOI: 10.1016/j.conb.2013.11.007

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Morris, R., Sancho-Martinez, I., Sharpee, T.O., Izpisua Belmonte, J.C. Mathematical approaches to modeling development and reprogramming. (2014) Proceedings of the National Academy of Sciences of the United States of America. 111(14):5076-82. DOI: 10.1073/pnas.1317150111

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Aljadeff, J., Segev, R., Berry, M.J., Sharpee, T.O. Spike triggered covariance in strongly correlated gaussian stimuli. (2013) PLOS Computational Biology. 9(9):e1003206. DOI: 10.1371/journal.pcbi.1003206

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Sharpee, T.O. Computational identification of receptive fields. (2013) Annual Review of Neuroscience. 36:103-20. DOI: 10.1146/annurev-neuro-062012-170253

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Sharpee, T.O., Kouh, M., Reynolds, J.H. Trade-off between curvature tuning and position invariance in visual area V4. (2013) Proceedings of the National Academy of Sciences of the United States of America. 110(28):11618-23. DOI: 10.1073/pnas.1217479110

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Kaardal, J., Fitzgerald, J.D., Berry, M.J., Sharpee, T.O. Identifying functional bases for multidimensional neural computations. (2013) Neural Computation. 25(7):1870-90. DOI: 10.1162/NECO_a_00465

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Current dimensionality-reduction methods can identify relevant subspaces for neural computations but do not favor one basis over the other within the relevant subspace. Finding the appropriate basis can simplify the description of the nonlinear computation with respect to the relevant variables, making it easier to elucidate the underlying neural computation and make hypotheses about the neural circuitry, giving rise to the observed responses. Part of the problem is that although some of the dimensionality reduction methods can identify many of the relevant dimensions, it is usually difficult to map out or interpret the nonlinear transformation with respect to more than a few relevant dimensions simultaneously without some simplifying assumptions. While recent approaches make it possible to create predictive models based on many relevant dimensions simultaneously, there still remains the need to relate such predictive models to the mechanistic descriptions of the operation of underlying neural circuitry. Here we demonstrate that transforming to a basis within the relevant subspace where the neural computation is best described by a given nonlinear function often makes it easier to interpret the computation and describe it with a small number of parameters. We refer to the corresponding basis as the functional basis, and illustrate the utility of such transformation in the context of logical OR and logical AND functions. We show that although dimensionality-reduction methods such as spike-triggered covariance are able to find a relevant subspace, they often produce dimensions that are difficult to interpret and do not correspond to a functional basis. The functional features can be found using a maximum likelihood approach. The results are illustrated using simulated neurons and recordings from retinal ganglion cells. The resulting features are uniquely defined and nonorthogonal, and they make it easier to relate computational and mechanistic models to each other.

Nandy, A.S., Sharpee, T.O., Reynolds, J.H., Mitchell, J.F. The fine structure of shape tuning in area V4. (2013) Neuron. 78(6):1102-15. DOI: 10.1016/j.neuron.2013.04.016

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Saremi, S., Sejnowski, T.J., Sharpee, T.O. Double-gabor filters are independent components of small translation-invariant image patches. (2013) Neural Computation. 25(4):922-39. DOI: 10.1162/NECO_a_00418

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Jeanne, J.M., Sharpee, T.O., Gentner, T.Q. Associative learning enhances population coding by inverting interneuronal correlation patterns. (2013) Neuron. 78(2):352-63. DOI: 10.1016/j.neuron.2013.02.023

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Eickenberg, M., Rowekamp, R.J., Kouh, M., Sharpee, T.O. Characterizing responses of translation-invariant neurons to natural stimuli: maximally informative invariant dimensions. (2012) Neural Computation. 24(9):2384-421. DOI: 10.1162/NECO_a_00330

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The human visual system is capable of recognizing complex objects even when their appearances change drastically under various viewing conditions. Especially in the higher cortical areas, the sensory neurons reflect such functional capacity in their selectivity for complex visual features and invariance to certain object transformations, such as image translation. Due to the strong nonlinearities necessary to achieve both the selectivity and invariance, characterizing and predicting the response patterns of these neurons represents a formidable computational challenge. A related problem is that such neurons are poorly driven by randomized inputs, such as white noise, and respond strongly only to stimuli with complex high-order correlations, such as natural stimuli. Here we describe a novel two-step optimization technique that can characterize both the shape selectivity and the range and coarseness of position invariance from neural responses to natural stimuli. One step in the optimization is finding the template as the maximally informative dimension given the estimated spatial location where the response could have been triggered within each image. The estimates of the locations that triggered the response are updated in the next step. Under the assumption of a monotonic relationship between the firing rate and stimulus projections on the template at a given position, the most likely location is the one that has the largest projection on the estimate of the template. The algorithm shows quick convergence during optimization, and the estimation results are reliable even in the regime of small signal-to-noise ratios. When we apply the algorithm to responses of complex cells in the primary visual cortex (V1) to natural movies, we find that responses of the majority of cells were significantly better described by translation-invariant models based on one template compared with position-specific models with several relevant features.

Atencio, C.A., Sharpee, T.O., Schreiner, C.E. Receptive field dimensionality increases from the auditory midbrain to cortex. (2012) Journal of Neurophysiology. 107(10):2594-603. DOI: 10.1152/jn.01025.2011

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Sharpee, T.O. Adaptive switches in midbrain circuits. (2012) Neuron. 73(1):6-7. DOI: 10.1016/j.neuron.2011.12.017

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Fitzgerald, J.D., Rowekamp, R.J., Sincich, L.C., Sharpee, T.O. Second order dimensionality reduction using minimum and maximum mutual information models. (2011) PLOS Computational Biology. 7(10):e1002249. DOI: 10.1371/journal.pcbi.1002249

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Sharpee, T.O., Nagel, K.I., Doupe, A.J. Two-dimensional adaptation in the auditory forebrain. (2011) Journal of Neurophysiology. 106(4):1841-61. DOI: 10.1152/jn.00905.2010

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Sharpee, T.O., Atencio, C.A., Schreiner, C.E. Hierarchical representations in the auditory cortex. (2011) Current Opinion in Neurobiology. 21(5):761-7. DOI: 10.1016/j.conb.2011.05.027

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Rowekamp, R.J., Sharpee, T.O. Analyzing multicomponent receptive fields from neural responses to natural stimuli. (2011) Network. 22(1-4):45-73. DOI: 10.3109/0954898X.2011.566303

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Fitzgerald, J.D., Sincich, L.C., Sharpee, T.O. Minimal models of multidimensional computations. (2011) PLOS Computational Biology. 7(3):e1001111. DOI: 10.1371/journal.pcbi.1001111

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Jeanne, J.M., Thompson, J.V., Sharpee, T.O., Gentner, T.Q. Emergence of learned categorical representations within an auditory forebrain circuit. (2011) Journal of Neuroscience. 31(7):2595-606. DOI: 10.1523/JNEUROSCI.3930-10.2011

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Imaizumi, K., Priebe, N.J., Sharpee, T.O., Cheung, S.W., Schreiner, C.E. Encoding of temporal information by timing, rate, and place in cat auditory cortex. (2010) PLOS One. 5(7):e11531. DOI: 10.1371/journal.pone.0011531

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Gallarda, B.W., Sharpee, T.O., Pfaff, S.L., Alaynick, W.A. Defining rhythmic locomotor burst patterns using a continuous wavelet transform. (2010) Ann. N. Y. Acad. Sci. 1198:133-9. DOI: 10.1111/j.1749-6632.2010.05437.x

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Atencio, C.A., Sharpee, T.O., Schreiner, C.E. Hierarchical computation in the canonical auditory cortical circuit. (2009) Proceedings of the National Academy of Sciences of the United States of America. 106(51):21894-9. DOI: 10.1073/pnas.0908383106

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Fitzgerald, J.D., Sharpee, T.O. Maximally informative pairwise interactions in networks. (2009) Phys Rev E Stat Nonlin Soft Matter Phys. 80(3 Pt 1):031914. DOI: 10.1103/PhysRevE.80.031914

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Liu, Y.S., Stevens, C.F., Sharpee, T.O. Predictable irregularities in retinal receptive fields. (2009) Proceedings of the National Academy of Sciences of the United States of America. 106(38):16499-504. DOI: 10.1073/pnas.0908926106

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Sincich, L.C., Horton, J.C., Sharpee, T.O. Preserving information in neural transmission. (2009) Journal of Neuroscience. 29(19):6207-16. DOI: 10.1523/JNEUROSCI.3701-08.2009

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Sharpee, T.O., Victor, J.D. Contextual modulation of V1 receptive fields depends on their spatial symmetry. (2009) Journal of Computational Neuroscience. 26(2):203-18. DOI: 10.1007/s10827-008-0107-5

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Kouh, M., Sharpee, T.O. Estimating linear-nonlinear models using Renyi divergences. (2009) Network. 20(2):49-68. DOI: 10.1080/09548980902950891

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Atencio, C.A., Sharpee, T.O., Schreiner, C.E. Cooperative nonlinearities in auditory cortical neurons. (2008) Neuron. 58(6):956-66. DOI: 10.1016/j.neuron.2008.04.026

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Sharpee, T.O., Miller, K.D., Stryker, M.P. On the importance of static nonlinearity in estimating spatiotemporal neural filters with natural stimuli. (2008) Journal of Neurophysiology. 99(5):2496-509. DOI: 10.1152/jn.01397.2007

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Clifford, C.W., Webster, M.A., Stanley, G.B., Stocker, A.A., Kohn, A., Sharpee, T.O., Schwartz, O. Visual adaptation: neural, psychological and computational aspects. (2007) Vision Research. 47(25):3125-31. DOI: 10.1016/j.visres.2007.08.023

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Sharpee, T.O. Comparison of information and variance maximization strategies for characterizing neural feature selectivity. (2007) Stat Med. 26(21):4009-31. DOI: 10.1002/sim.2931

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Sharpee, T., Bialek, W. Neural decision boundaries for maximal information transmission. (2007) PLOS One. 2(7):e646. DOI: 10.1371/journal.pone.0000646

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Sharpee, T.O., Sugihara, H., Kurgansky, A.V., Rebrik, S.P., Stryker, M.P., Miller, K.D. Adaptive filtering enhances information transmission in visual cortex. (2006) Nature. 439(7079):936-42. DOI: 10.1038/nature04519

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Victor, J.D., Mechler, F., Repucci, M.A., Purpura, K.P., Sharpee, T. Responses of V1 neurons to two-dimensional hermite functions. (2006) Journal of Neurophysiology. 95(1):379-400. DOI: 10.1152/jn.00498.2005

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Sharpee, T., Sugihara, H., Kurgansky, A.V., Rebrik, S., Stryker, M.P., Miller, K.D. Probing feature selectivity of neurons in primary visual cortex with natural stimuli. (2004) Proc SPIE Int Soc Opt Eng.(5467):212-222

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Sharpee, T., Rust, N.C., Bialek, W. Analyzing neural responses to natural signals: maximally informative dimensions. (2004) Neural Computation. 16(2):223-50. DOI: 10.1162/089976604322742010

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Dykman, M.I., Sharpee, T., Platzman, P.M. Enhancement of tunneling from a correlated 2D electron system by a many-electron Mössbauer-type recoil in a magnetic field. (2001) Phys. Rev. Lett. 86(11):2408-11. DOI: 10.1103/PhysRevLett.86.2408

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Barabash-Sharpee, T., Dykman, M.I., Platzman, P.M. Tunneling transverse to a magnetic field and its occurrence in correlated 2D electron systems. (2000) Phys. Rev. Lett. 84(10):2227-30. DOI: 10.1103/PhysRevLett.84.2227

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Tatyana Sharpee, PhD

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