{"page":"14419 - 14424","issue":"32","day":"10","year":"2010","_id":"3736","quality_controlled":0,"doi":"10.1073/pnas.1004906107","intvolume":"       107","author":[{"full_name":"Gasper Tkacik","first_name":"Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Jason","last_name":"Prentice","full_name":"Prentice, Jason S"},{"last_name":"Balasubramanian","first_name":"Vijay","full_name":"Balasubramanian, Vijay"},{"full_name":"Schneidman, Elad","last_name":"Schneidman","first_name":"Elad"}],"date_created":"2018-12-11T12:04:53Z","date_updated":"2021-01-12T07:51:50Z","title":"Optimal population coding by noisy spiking neurons","status":"public","abstract":[{"text":"In retina and in cortical slice the collective response of spiking neural populations is well described by &quot;maximum-entropy&quot; models in which only pairs of neurons interact. We asked, how should such interactions be organized to maximize the amount of information represented in population responses? To this end, we extended the linear-nonlinear-Poisson model of single neural response to include pairwise interactions, yielding a stimulus-dependent, pairwise maximum-entropy model. We found that as we varied the noise level in single neurons and the distribution of network inputs, the optimal pairwise interactions smoothly interpolated to achieve network functions that are usually regarded as discrete–stimulus decorrelation, error correction, and independent encoding. These functions reflected a trade-off between efficient consumption of finite neural bandwidth and the use of redundancy to mitigate noise. Spontaneous activity in the optimal network reflected stimulus-induced activity patterns, and single-neuron response variability overestimated network noise. Our analysis suggests that rather than having a single coding principle hardwired in their architecture, networks in the brain should adapt their function to changing noise and stimulus correlations.","lang":"eng"}],"acknowledgement":"R01 EY08124/EY/NEI NIH HHS/United States; T32-07035/PHS HHS/United States","date_published":"2010-08-10T00:00:00Z","type":"journal_article","publist_id":"2492","publication_status":"published","extern":1,"volume":107,"main_file_link":[{"open_access":"0","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2922524/"}],"publication":"PNAS","month":"08","citation":{"mla":"Tkačik, Gašper, et al. “Optimal Population Coding by Noisy Spiking Neurons.” <i>PNAS</i>, vol. 107, no. 32, National Academy of Sciences, 2010, pp. 14419–24, doi:<a href=\"https://doi.org/10.1073/pnas.1004906107\">10.1073/pnas.1004906107</a>.","ista":"Tkačik G, Prentice J, Balasubramanian V, Schneidman E. 2010. Optimal population coding by noisy spiking neurons. PNAS. 107(32), 14419–14424.","ieee":"G. Tkačik, J. Prentice, V. Balasubramanian, and E. Schneidman, “Optimal population coding by noisy spiking neurons,” <i>PNAS</i>, vol. 107, no. 32. National Academy of Sciences, pp. 14419–14424, 2010.","short":"G. Tkačik, J. Prentice, V. Balasubramanian, E. Schneidman, PNAS 107 (2010) 14419–14424.","ama":"Tkačik G, Prentice J, Balasubramanian V, Schneidman E. Optimal population coding by noisy spiking neurons. <i>PNAS</i>. 2010;107(32):14419-14424. doi:<a href=\"https://doi.org/10.1073/pnas.1004906107\">10.1073/pnas.1004906107</a>","chicago":"Tkačik, Gašper, Jason Prentice, Vijay Balasubramanian, and Elad Schneidman. “Optimal Population Coding by Noisy Spiking Neurons.” <i>PNAS</i>. National Academy of Sciences, 2010. <a href=\"https://doi.org/10.1073/pnas.1004906107\">https://doi.org/10.1073/pnas.1004906107</a>.","apa":"Tkačik, G., Prentice, J., Balasubramanian, V., &#38; Schneidman, E. (2010). Optimal population coding by noisy spiking neurons. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1004906107\">https://doi.org/10.1073/pnas.1004906107</a>"},"publisher":"National Academy of Sciences"}