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Barlow HB. Possible Principles Underlying the Transformations of Sensory Messages. In: Sensory Communication. The MIT Press; 1961. p. 217–234.
Attwell D, Laughlin SB. An Energy Budget for Signaling in the Grey Matter of the Brain. J Cereb Blood Flow Metab. 2001;21(10):1133–1145. doi: 10.1097/00004647-200110000-00001
PubMed
DOI
Harris JJ, Jolivet R, Attwell D. Synaptic energy use and supply. Neuron. 2012;75(5):762–777. doi: 10.1016/j.neuron.2012.08.019
PubMed
DOI
Levy WB, Baxter RA. Energy Efficient Neural Codes. Neural Comput. 1996;8(3):531–543. doi: 10.1162/neco.1996.8.3.531
PubMed
DOI
Balasubramanian V, Kimber D, Berry MJ II. Metabolically Efficient Information Processing. Neural Comput. 2001;13(4):799–815. doi: 10.1162/089976601300014358
PubMed
DOI
Laughlin S. Energy as a constraint on the coding and processing of sensory information. Curr Opin Neurobiol. 2001;11(4):475–480. doi: 10.1016/S0959-4388(00)00237-3
PubMed
DOI
Niven JE, Laughlin SB. Energy limitation as a selective pressure on the evolution of sensory systems. J Exp Biol. 2008;211(11):1792–1804. doi: 10.1242/jeb.017574
PubMed
DOI
Yu L, Yu Y. Energy-efficient neural information processing in individual neurons and neuronal networks. J Neurosci Res. 2017;95(11):2253–2266. doi: 10.1002/jnr.24131
PubMed
DOI
Sengupta B, Laughlin SB, Niven JE. Balanced Excitatory and Inhibitory Synaptic Currents Promote Efficient Coding and Metabolic Efficiency. PLoS Comput Biol. 2013;9(10):e1003263. doi: 10.1371/journal.pcbi.1003263
PubMed
DOI
PMC
Barta T, Kostal L. The effect of inhibition on rate code efficiency indicators. PLoS Comput Biol. 2019;15(12):e1007545. doi: 10.1371/journal.pcbi.1007545
PubMed
DOI
PMC
Monier C, Chavane F, Baudot P, Graham LJ, Frégnac Y. Orientation and Direction Selectivity of Synaptic Inputs in Visual Cortical Neurons. Neuron. 2003;37(4):663–680. doi: 10.1016/S0896-6273(03)00064-3
PubMed
DOI
Brunel N. Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons. J Comput Neurosci. 2000;8:183–208. doi: 10.1023/A:1008925309027
PubMed
DOI
Renart A, de la Rocha J, Bartho P, Hollender L, Parga N, Reyes A, et al.. The Asynchronous State in Cortical Circuits. Science. 2010;327(5965):587–590. doi: 10.1126/science.1179850
PubMed
DOI
PMC
Tetzlaff T, Helias M, Einevoll GT, Diesmann M. Decorrelation of Neural-Network Activity by Inhibitory Feedback. PLoS Comp Biol. 2012;8(8):e1002596. doi: 10.1371/journal.pcbi.1002596
PubMed
DOI
PMC
Bernacchia A, Wang XJ. Decorrelation by Recurrent Inhibition in Heterogeneous Neural Circuits. Neural Comput. 2013;25(7):1732–1767. doi: 10.1162/NECO_a_00451
PubMed
DOI
PMC
Abbott LF, Dayan P. The Effect of Correlated Variability on the Accuracy of a Population Code. Neural Comput. 1999;11(1):91–101. doi: 10.1162/089976699300016827
PubMed
DOI
Averbeck BB, Latham PE, Pouget A. Neural correlations, population coding and computation. Nat Rev Neurosci. 2006;7(5):358–366. doi: 10.1038/nrn1888
PubMed
DOI
Panzeri S, Moroni M, Safaai H, Harvey CD. The structures and functions of correlations in neural population codes. Nat Rev Neurosci. 2022;23(9):551–567. doi: 10.1038/s41583-022-00606-4
PubMed
DOI
Shadlen MN, Newsome WT. The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding. J Neurosci. 1998;18(10):3870–3896. doi: 10.1523/JNEUROSCI.18-10-03870.1998
PubMed
DOI
PMC
Moreno-Bote R, Beck J, Kanitscheider I, Pitkow X, Latham P, Pouget A. Information-limiting correlations. Nature Neuroscience. 2014;17(10):1410–1417. doi: 10.1038/nn.3807
PubMed
DOI
PMC
Blahut R. Computation of channel capacity and rate-distortion functions. IEEE Trans Inf Theory. 1972;18(4):460–473. doi: 10.1109/TIT.1972.1054855
DOI
Jimbo M, Kunisawa K. An iteration method for calculating the relative capacity. Information and Control. 1979;43(2):216–223. doi: 10.1016/S0019-9958(79)90719-8
DOI
Suksompong P, Berger T. Capacity Analysis for Integrate-and-Fire Neurons With Descending Action Potential Thresholds. IEEE Trans Inf Theory. 2010;56(2):838–851. doi: 10.1109/TIT.2009.2037042
DOI
Kostal L, Lansky P. Information capacity and its approximations under metabolic cost in a simple homogeneous population of neurons. Biosystems. 2013;112(3):265–275. doi: 10.1016/j.biosystems.2013.03.019
PubMed
DOI
Kostal L, Lansky P, McDonnell MD. Metabolic cost of neuronal information in an empirical stimulus-response model. Biol Cybern. 2013;107(3):355–365. doi: 10.1007/s00422-013-0554-6
PubMed
DOI
Stemmler M. A single spike suffices: the simplest form of stochastic resonance in model neurons. Network. 1996;7(4):687–716. doi: 10.1088/0954-898X_7_4_005
DOI
Greenwood PE, Lansky P. Optimum signal in a simple neuronal model with signal-dependent noise. Biol Cybern. 2005;92(3):199–205. doi: 10.1007/s00422-005-0545-3
PubMed
DOI
Meyer HS, Wimmer VC, Oberlaender M, de Kock CPJ, Sakmann B, Helmstaedter M. Number and Laminar Distribution of Neurons in a Thalamocortical Projection Column of Rat Vibrissal Cortex. Cereb Cortex. 2010;20(10):2277–2286. doi: 10.1093/cercor/bhq067
PubMed
DOI
PMC
Bernardi D, Doron G, Brecht M, Lindner B. A network model of the barrel cortex combined with a differentiator detector reproduces features of the behavioral response to single-neuron stimulation. PLOS Comput Biol. 2021;17(2). doi: 10.1371/journal.pcbi.1007831
PubMed
DOI
PMC
Hennequin G, Vogels T, Gerstner W. Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements. Neuron. 2014;82(6):1394–1406. doi: 10.1016/j.neuron.2014.04.045
PubMed
DOI
PMC
Potjans TC, Diesmann M. The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model. Cereb Cortex. 2014;24(3):785–806. doi: 10.1093/cercor/bhs358
PubMed
DOI
PMC
Kobayashi R, Kurita S, Kurth A, Kitano K, Mizuseki K, Diesmann M, et al.. Reconstructing neuronal circuitry from parallel spike trains. Nat Commun. 2019;10(1):4468. doi: 10.1038/s41467-019-12225-2
PubMed
DOI
PMC
Barta T, Kostal L. Regular spiking in high-conductance states: The essential role of inhibition. Phys Rev E. 2021;103(2):022408. doi: 10.1103/PhysRevE.103.022408
PubMed
DOI
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al.. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods. 2020;17(3):261–272. doi: 10.1038/s41592-019-0686-2
PubMed
DOI
PMC
Padamsey Z, Katsanevaki D, Dupuy N, Rochefort NL. Neocortex saves energy by reducing coding precision during food scarcity. Neuron. 2022;110(2):280–296. doi: 10.1016/j.neuron.2021.10.024
PubMed
DOI
PMC
Kobayashi R, Tsubo Y, Shinomoto S. Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Front Comput Neurosci. 2009;3:9. doi: 10.3389/neuro.10.009.2009
PubMed
DOI
PMC
Zerlaut Y, Chemla S, Chavane F, Destexhe A. Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons. J Comput Neurosci. 2017;44(1):45–61. doi: 10.1007/s10827-017-0668-2
PubMed
DOI
Laughlin S. A simple coding procedure enhances a neuron’s information capacity. Z Naturforsch [C]. 1981;36(9-10):910–912. doi: 10.1515/znc-1981-9-1040
PubMed
DOI
Kostal L, Lansky P, Rospars JP. Efficient olfactory coding in the pheromone receptor neuron of a moth. PLoS Comput Biol. 2008;4:e1000053. doi: 10.1371/journal.pcbi.1000053
PubMed
DOI
PMC
Treves A, Panzeri S, Rolls ET, Booth M, Wakeman EA. Firing rate distributions and efficiency of information transmission of inferior temporal cortex neurons to natural visual stimuli. Neural Comput. 1999;11(3):601–632. doi: 10.1162/089976699300016593
PubMed
DOI
de Polavieja GG. Errors Drive the Evolution of Biological Signalling to Costly Codes. J Theor Biol. 2002;214(4):657–664. doi: 10.1006/jtbi.2001.2498
PubMed
DOI
de Polavieja GG. Reliable biological communication with realistic constraints. Phys Rev E. 2004;70(6). doi: 10.1103/PhysRevE.70.061910
PubMed
DOI
Kostal L, Kobayashi R. Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints. Biosystems. 2015;136:3–10. doi: 10.1016/j.biosystems.2015.06.008
PubMed
DOI
Kostal L, Kobayashi R. Critical size of neural population for reliable information transmission. Phys Rev E (Rapid Commun). 2019;100(1):050401(R).
PubMed
Gur M, Beylin A, Snodderly DM. Response Variability of Neurons in Primary Visual Cortex (V1) of Alert Monkeys. J Neurosci. 1997;17(8):2914–2920. doi: 10.1523/JNEUROSCI.17-08-02914.1997
PubMed
DOI
PMC
Geisler WS, Albrecht DG. Visual cortex neurons in monkeys and cats: Detection, discrimination, and identification. Vis Neurosci. 1997;14(5):897–919. doi: 10.1017/S0952523800011627
PubMed
DOI
Uhlenbeck GE, Ornstein LS. On the Theory of the Brownian Motion. Phys Rev. 1930;36(5):823–841. doi: 10.1103/PhysRev.36.823
DOI
Destexhe A, Rudolph M, Fellous JM, Sejnowski TJ. Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience. 2001;107(1):13–24. doi: 10.1016/s0306-4522(01)00344-x
PubMed
DOI
PMC
Rajdl K, Lansky P. Stein’s neuronal model with pooled renewal input. Biol Cybern. 2015;109(3):389–399. doi: 10.1007/s00422-015-0650-x
PubMed
DOI
Stimberg M, Brette R, Goodman DF. Brian 2, an intuitive and efficient neural simulator. eLife. 2019;8. doi: 10.7554/eLife.47314
PubMed
DOI
PMC
Vetter P, Roth A, Häusser M. Propagation of Action Potentials in Dendrites Depends on Dendritic Morphology. J Neurophysiol. 2001;85(2):926–937. doi: 10.1152/jn.2001.85.2.926
PubMed
DOI
Strong SP, Koberle R, de Ruyter van Steveninck RR, Bialek W. Entropy and Information in Neural Spike Trains. Phys Rev Lett. 1998;80(1):197–200. doi: 10.1103/PhysRevLett.80.197
DOI
Panzeri S, Senatore R, Montemurro MA, Petersen RS. Correcting for the Sampling Bias Problem in Spike Train Information Measures. J Neurophysiol. 2007;98(3):1064–1072. doi: 10.1152/jn.00559.2007
PubMed
DOI
Panzeri S, Treves A. Analytical estimates of limited sampling biases in different information measures. Network. 1996;7(1):87–107. doi: 10.1080/0954898X.1996.11978656
PubMed
DOI
Paninski L. Estimation of Entropy and Mutual Information. Neural Comput. 2003;15(6):1191–1253. doi: 10.1162/089976603321780272
DOI
Nemenman I, Bialek W, de Ruyter van Steveninck R. Entropy and information in neural spike trains: Progress on the sampling problem. Phys Rev E. 2004;69(5):056111. doi: 10.1103/PhysRevE.69.056111
PubMed
DOI