{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:31:09Z","timestamp":1772929869749,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100016789","name":"Pomona College","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100016789","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Biol Cybern"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The neocortex is composed of spiking neurons interconnected in a sparse, recurrent network. Spiking activity within these networks underlies the computations that transform sensory inputs into appropriate behavioral responses. In this study, we train recurrent spiking neural network (SNN) models constrained by neocortical connectivity statistics and investigate the architectural changes that enable task-relevant, spike-based computations. We employ a binary state change detection task\u2014an experimental paradigm used in animal behavioral studies. Our SNNs consist of interconnected excitatory and inhibitory units with connection probabilities and strengths modeled after the mouse neocortex and maintained throughout training and evaluation. Following training, we find that SNNs selectively modulate firing rates based on the binary input state, and that excitatory and inhibitory connectivity within and between input and recurrent layers adjusts accordingly. Notably, inhibitory neurons in the recurrent layer that positively modulate firing rates in response to one input state strengthen their connections to recurrent units with the opposite modulation. This push-pull connectivity\u2014where excitation and inhibition are dynamically balanced in an opponent fashion\u2014emerges as a key computational strategy and is reminiscent of connectivity observed in primary visual cortex. Using a one-hot output encoding yields identical firing rates to both input states, yet the push-pull inhibitory motif still arises. Importantly, this motif fails to emerge when Dale\u2019s principle is not enforced during training, and task performance also declines.Furthermore, disrupting spike timing by a few milliseconds significantly impairs task performance, highlighting the importance of precise spike time coordination for computation in sparse networks like neocortex. The emergence of push-pull inhibition through task training in spiking models underscores the crucial role of interneurons and structured inhibition in shaping neural dynamics and spike-based information processing.<\/jats:p>","DOI":"10.1007\/s00422-025-01030-4","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T04:24:51Z","timestamp":1767759891000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Task success in trained spiking neural network models coincides with emergence of cross-stimulus-modulated inhibition"],"prefix":"10.1007","volume":"120","author":[{"given":"Yuqing","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Chadbourne M. B.","family":"Smith","sequence":"additional","affiliation":[]},{"given":"Tarek","family":"Jabri","sequence":"additional","affiliation":[]},{"given":"Mufeng","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Franz","family":"Scherr","sequence":"additional","affiliation":[]},{"given":"Jason N.","family":"MacLean","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"issue":"3","key":"1030_CR1","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.conb.2010.02.012","volume":"20","author":"HJ Alitto","year":"2010","unstructured":"Alitto HJ, Dan Y (2010) Function of Inhibition in visual cortical processing. Curr Opin Neurobiol 20(3):340\u2013346. https:\/\/doi.org\/10.1016\/j.conb.2010.02.012","journal-title":"Curr Opin Neurobiol"},{"key":"1030_CR2","doi-asserted-by":"publisher","unstructured":"Ayyilmaz UI, Krishnan AG, Zhu Y (2024) Excitatory and inhibitory neurons exhibit distinct roles for task learning, temporal scaling, and working memory in recurrent spiking neural network models of neocortex [Conference presentation]. Bernstein Conference. Frankfurt, Germany. https:\/\/doi.org\/10.12751\/nncn.bc2024.313","DOI":"10.12751\/nncn.bc2024.313"},{"key":"1030_CR3","doi-asserted-by":"publisher","unstructured":"Bellec G, Kappel D, Maass W, Legenstein R (2018) Deep rewiring: Training very sparse deep networks. arXiv:1711.05136v5 [cs.NE] [Preprint]. https:\/\/doi.org\/10.48550\/arXiv.1711.05136","DOI":"10.48550\/arXiv.1711.05136"},{"issue":"1","key":"1030_CR4","doi-asserted-by":"publisher","first-page":"3625","DOI":"10.1038\/s41467-020-17236-y","volume":"11","author":"G Bellec","year":"2020","unstructured":"Bellec G, Scherr F, Subramoney A, Hajek E, Salaj D, Legenstein R, Maass W (2020) A solution to the learning dilemma for recurrent networks of spiking neurons. Nat Commun 11(1):3625. https:\/\/www.nature.com\/articles\/s41467-020-17236-y","journal-title":"Nat Commun"},{"issue":"3","key":"1030_CR5","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.neuron.2020.01.040","volume":"106","author":"YN Billeh","year":"2020","unstructured":"Billeh YN, Cai B, Gratiy SL, Dai K, Iyer R, Gouwens NW, Arkhipov A (2020) Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex. Neuron 106(3):388\u2013403. https:\/\/doi.org\/10.1016\/j.neuron.2020.01.040","journal-title":"Neuron"},{"issue":"9","key":"1030_CR6","doi-asserted-by":"publisher","first-page":"e1007409","DOI":"10.1371\/journal.pcbi.1007409","volume":"16","author":"K Bojanek","year":"2020","unstructured":"Bojanek K, Zhu Y, MacLean JN (2020) Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks. PLOS Comput Biol 16(9):e1007409. https:\/\/doi.org\/10.1371\/journal.pcbi.1007409.","journal-title":"PLOS Comput Biol"},{"key":"1030_CR7","doi-asserted-by":"publisher","DOI":"10.1101\/2020.06.15.148114v2.abstract","author":"H Bos","year":"2020","unstructured":"Bos H, Oswald AM, Doiron B (2020) Untangling stability and gain modulation in cortical circuits with multiple interneuron classes. BioRxiv. 2020-06 https:\/\/www.biorxiv.org\/content\/https:\/\/doi.org\/10.1101\/2020.06.15.148114v2.abstract","journal-title":"BioRxiv"},{"key":"1030_CR8","doi-asserted-by":"publisher","unstructured":"Brette R (2015) Philosophy of the spike: rate-based vs. spike-based theories of the brain. Front Syst Neurosci 151. https:\/\/doi.org\/10.3389\/fnsys.2015.00151","DOI":"10.3389\/fnsys.2015.00151"},{"issue":"5","key":"1030_CR9","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.1152\/jn.00686.2005","volume":"94","author":"R Brette","year":"2005","unstructured":"Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94(5):3637\u20133642. https:\/\/journals.physiology.org\/doi\/full\/https:\/\/doi.org\/10.1152\/jn.00686.2005","journal-title":"J Neurophysiol"},{"key":"1030_CR10","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1023\/A:1008925309027","volume":"8","author":"N Brunel","year":"2000","unstructured":"Brunel N (2000) Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8:183\u2013208. https:\/\/link.springer.com\/article\/10.1023\/A:1008925309027","journal-title":"J Comput Neurosci"},{"issue":"5","key":"1030_CR11","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nn.4286","volume":"19","author":"N Brunel","year":"2016","unstructured":"Brunel N (2016) Is cortical connectivity optimized for storing information? Nat Neurosci 19(5):749\u2013755. https:\/\/www.nature.com\/articles\/nn.4286","journal-title":"Nat Neurosci"},{"key":"1030_CR13","doi-asserted-by":"publisher","first-page":"e73276","DOI":"10.7554\/eLife.73276","volume":"11","author":"N Calaim","year":"2022","unstructured":"Calaim N, Dehmelt FA, Gon\u00e7alves PJ, Machens CK (2022) The geometry of robustness in spiking neural networks. Elife 11:e73276. https:\/\/doi.org\/10.7554\/eLife.73276","journal-title":"Elife"},{"issue":"21","key":"1030_CR14","doi-asserted-by":"publisher","first-page":"3061","DOI":"10.1016\/S0042-6989(97)00100-4","volume":"37","author":"M Carandini","year":"1997","unstructured":"Carandini M, Ringach DL (1997) Predictions of a recurrent model of orientation selectivity. Vision Res 37(21):3061\u20133071. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0042698997001004","journal-title":"Vision Res"},{"issue":"1","key":"1030_CR15","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1038\/s41467-020-14578-5","volume":"11","author":"U Cohen","year":"2020","unstructured":"Cohen U, Chung S, Lee DD, Sompolinsky H (2020) Separability and geometry of object manifolds in deep neural networks. Nat Commun 11(1):746. https:\/\/www.nature.com\/articles\/s41467-020-14578-5","journal-title":"Nat Commun"},{"key":"1030_CR16","doi-asserted-by":"crossref","unstructured":"Cone JJ, Scantlen MD, Histed MH, Maunsell JH (2019) Different inhibitory interneuron cell classes make distinct contributions to visual contrast perception. Eneuro, 6(1). https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC6414440\/","DOI":"10.1523\/ENEURO.0337-18.2019"},{"issue":"15","key":"1030_CR17","doi-asserted-by":"publisher","first-page":"3122","DOI":"10.1523\/JNEUROSCI.1736-21.2022","volume":"42","author":"J Day-Cooney","year":"2022","unstructured":"Day-Cooney J, Cone JJ, Maunsell JH (2022) Perceptual weighting of V1 spikes revealed by optogenetic white noise stimulation. J Neurosci 42(15):3122\u20133132. https:\/\/www.jneurosci.org\/content\/42\/15\/3122.abstract","journal-title":"J Neurosci"},{"issue":"1","key":"1030_CR18","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1152\/jn.1992.68.1.144","volume":"68","author":"GC DeAngelis","year":"1992","unstructured":"DeAngelis GC, Robson JG, Ohzawa I, Freeman RD (1992) Organization of suppression in receptive fields of neurons in Cat visual cortex. J Neurophysiol 68(1):144\u2013163. https:\/\/doi.org\/10.1152\/jn.1992.68.1.144","journal-title":"J Neurophysiol"},{"issue":"6583","key":"1030_CR19","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1038\/381610a0","volume":"381","author":"RC deCharms","year":"1996","unstructured":"deCharms RC, Merzenich MM (1996) Primary cortical representation of sounds by the coordination of action-potential timing. Nature 381(6583):610\u2013613. https:\/\/www.nature.com\/articles\/381610a0","journal-title":"Nature"},{"issue":"18","key":"1030_CR20","doi-asserted-by":"publisher","first-page":"2842","DOI":"10.1016\/j.visres.2006.02.025","volume":"46","author":"RM Douglas","year":"2006","unstructured":"Douglas RM, Neve A, Quittenbaum JP, Alam NM, Prusky GT (2006) Perception of visual motion coherence by rats and mice. Vision Res 46(18):2842\u20132847. https:\/\/doi.org\/10.1016\/j.visres.2006.02.025","journal-title":"Vision Res"},{"key":"1030_CR21","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/BF00228003","volume":"80","author":"UT Eysel","year":"1990","unstructured":"Eysel UT, Crook JM, Machemer HF (1990) GABA-induced remote inactivation reveals cross-orientation Inhibition in the Cat striate cortex. Exp Brain Res 80:626\u2013630. https:\/\/link.springer.com\/article\/10.1007\/BF00228003","journal-title":"Exp Brain Res"},{"issue":"1","key":"1030_CR22","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1146\/annurev.neuro.23.1.441","volume":"23","author":"D Ferster","year":"2000","unstructured":"Ferster D, Miller KD (2000) Neural mechanisms of orientation selectivity in the visual cortex. Annu Rev Neurosci 23(1):441\u2013471. https:\/\/doi.org\/10.1146\/annurev.neuro.23.1.441","journal-title":"Annu Rev Neurosci"},{"issue":"3","key":"1030_CR23","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1038\/nn.4483","volume":"20","author":"P Garcia-Junco-Clemente","year":"2017","unstructured":"Garcia-Junco-Clemente P, Ikrar T, Tring E, Xu X, Ringach DL, Trachtenberg JT (2017) An inhibitory pull\u2013push circuit in frontal cortex. Nat Neurosci 20(3):389\u2013392. https:\/\/doi.org\/10.1038\/nn.4483","journal-title":"Nat Neurosci"},{"key":"1030_CR24","unstructured":"Huh D, Sejnowski TJ (2018) Gradient descent for spiking neural networks. Adv Neural Inf Process Syst, 31. http:\/\/papers.nips.cc\/paper\/7417-gradient-descent-for-spiking-neural-networks"},{"issue":"12","key":"1030_CR25","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1162\/neco_a_01544","volume":"34","author":"T Jabri","year":"2022","unstructured":"Jabri T, MacLean JN (2022) Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring. Neural Comput 34(12):2347\u20132373. https:\/\/doi.org\/10.1162\/neco_a_01544","journal-title":"Neural Comput"},{"key":"#cr-split#-1030_CR26.1","unstructured":"Kandel ER (1957) Dale's principle and the functional specificity of neurons. Psychopharmacology"},{"key":"#cr-split#-1030_CR26.2","unstructured":"A Review of Progress, 1967, 385-398"},{"issue":"16","key":"1030_CR27","doi-asserted-by":"publisher","first-page":"5931","DOI":"10.1523\/JNEUROSCI.5753-10.2011","volume":"31","author":"S Katzner","year":"2011","unstructured":"Katzner S, Busse L, Carandini M (2011) GABAA Inhibition controls response gain in visual cortex. J Neurosci 31(16):5931\u20135941. https:\/\/www.jneurosci.org\/content\/31\/16\/5931.short","journal-title":"J Neurosci"},{"issue":"7483","key":"1030_CR28","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1038\/nature12983","volume":"505","author":"A Kepecs","year":"2014","unstructured":"Kepecs A, Fishell G (2014) Interneuron cell types are fit to function. Nature 505(7483):318\u2013326. https:\/\/www.nature.com\/articles\/nature12983","journal-title":"Nature"},{"key":"1030_CR29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1412.6980","author":"DP Kingma","year":"2014","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. ArXiv. https:\/\/doi.org\/10.48550\/ArXiv.1412.6980. :1412.6980 [cs.LG] [Preprint]","journal-title":"ArXiv"},{"issue":"1","key":"1030_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-25844-4","volume":"8","author":"LAMH Kirkels","year":"2018","unstructured":"Kirkels LAMH, Zhang W, Havenith MN, Tiesinga P, Glennon J, Van Wezel RJA, Duijnhouwer J (2018) The opto-locomotor reflex as a tool to measure sensitivity to moving random Dot patterns in mice. Sci Rep 8(1):1\u20139. https:\/\/www.nature.com\/articles\/s41598-018-25844-4","journal-title":"Sci Rep"},{"key":"1030_CR31","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1146\/annurev-neuro-070815-013851","volume":"39","author":"A Kohn","year":"2016","unstructured":"Kohn A, Coen-Cagli R, Kanitscheider I, Pouget A (2016) Correlations and neuronal population information. Annu Rev Neurosci 39:237\u2013256. https:\/\/doi.org\/10.1146\/annurev-neuro-070815-013851","journal-title":"Annu Rev Neurosci"},{"key":"1030_CR32","doi-asserted-by":"publisher","unstructured":"Koren V, Malerba SB, Schwalger T, Panzeri S (2024) Structure, dynamics, coding and optimal biophysical parameters of efficient excitatory-inhibitory spiking networks. BioRxiv. 2024.04.24.590955 https:\/\/doi.org\/10.1101\/2024.04.24.590955","DOI":"10.1101\/2024.04.24.590955"},{"issue":"12","key":"1030_CR33","doi-asserted-by":"publisher","first-page":"3685","DOI":"10.1523\/JNEUROSCI.4500-08.2009","volume":"29","author":"AA Koulakov","year":"2009","unstructured":"Koulakov AA, Hrom\u00e1dka T, Zador AM (2009) Correlated connectivity and the distribution of firing rates in the neocortex. J Neurosci 29(12):3685\u20133694. https:\/\/doi.org\/10.1523\/JNEUROSCI.4500-08.2009","journal-title":"J Neurosci"},{"key":"1030_CR34","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3389\/fncir.2016.00037","volume":"10","author":"J Kremkow","year":"2016","unstructured":"Kremkow J, Perrinet LU, Monier C, Alonso JM, Aertsen A, Fr\u00e9gnac Y, Masson GS (2016) Push-pull receptive field organization and synaptic depression: mechanisms for reliably encoding naturalistic stimuli in V1. Front Neural Circuits 10:37. https:\/\/doi.org\/10.3389\/fncir.2016.00037","journal-title":"Front Neural Circuits"},{"issue":"12","key":"1030_CR36","doi-asserted-by":"publisher","first-page":"eadi4350","DOI":"10.1126\/sciadv.adi4350","volume":"10","author":"F Lagzi","year":"2024","unstructured":"Lagzi F, Fairhall AL (2024) Emergence of co-tuning in inhibitory neurons as a network phenomenon mediated by randomness, correlations, and homeostatic plasticity. Sci Adv 10(12):eadi4350. https:\/\/doi.org\/10.1126\/sciadv.adi4350","journal-title":"Sci Adv"},{"key":"1030_CR35","doi-asserted-by":"publisher","DOI":"10.1101\/2021.09.06.459211v1.abstract","author":"F Lagzi","year":"2021","unstructured":"Lagzi F, Bustos MC, Oswald AM, Doiron B (2021) Assembly formation is stabilized by parvalbumin neurons and accelerated by somatostatin neurons. BioRxiv. 2021-09 https:\/\/www.biorxiv.org\/content\/https:\/\/doi.org\/10.1101\/2021.09.06.459211v1.abstract","journal-title":"BioRxiv"},{"issue":"1","key":"1030_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2202-16-S1-P198","volume":"16","author":"M Lankarany","year":"2015","unstructured":"Lankarany M, Prescott SA (2015) Multiplexed coding through synchronous and asynchronous spiking. BMC Neurosci 16(1):1\u20132. https:\/\/doi.org\/10.1186\/1471-2202-16-S1-P198","journal-title":"BMC Neurosci"},{"key":"1030_CR38","doi-asserted-by":"publisher","first-page":"508","DOI":"10.3389\/fnins.2016.00508\/full","volume":"10","author":"JH Lee","year":"2016","unstructured":"Lee JH, Delbruck T, Pfeiffer M (2016) Training deep spiking neural networks using backpropagation. Front NeuroSci 10:508. https:\/\/www.frontiersin.org\/articles\/https:\/\/doi.org\/10.3389\/fnins.2016.00508\/full","journal-title":"Front NeuroSci"},{"issue":"11","key":"1030_CR39","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1038\/nn.3220","volume":"15","author":"A Litwin-Kumar","year":"2012","unstructured":"Litwin-Kumar A, Doiron B (2012) Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat Neurosci 15(11):1498\u20131505. https:\/\/www.nature.com\/articles\/nn.3220","journal-title":"Nat Neurosci"},{"key":"1030_CR40","doi-asserted-by":"crossref","unstructured":"Maheswaranathan N, McIntosh LT, Tanaka H, Grant S, Kastner DB, Melander JB, Baccus SA (2023) Interpreting the retinal neural code for natural scenes: from computations to neurons. Neuron","DOI":"10.1016\/j.neuron.2023.06.007"},{"issue":"11","key":"1030_CR41","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1016\/j.cub.2018.04.012","volume":"28","author":"T Marques","year":"2018","unstructured":"Marques T, Summers MT, Fioreze G, Fridman M, Dias RF, Feller MB, Petreanu L (2018) A role for mouse primary visual cortex in motion perception. Curr Biol 28(11):1703\u20131713. https:\/\/doi.org\/10.1016\/j.cub.2018.04.012","journal-title":"Curr Biol"},{"issue":"22","key":"1030_CR42","doi-asserted-by":"publisher","first-page":"228102","DOI":"10.1103\/PhysRevLett.108.228102","volume":"108","author":"JF Mejias","year":"2012","unstructured":"Mejias JF, Longtin A (2012) Optimal heterogeneity for coding in spiking neural networks. Phys Rev Lett 108(22):228102. https:\/\/journals.aps.org\/prl\/abstract\/https:\/\/doi.org\/10.1103\/PhysRevLett.108.228102","journal-title":"Phys Rev Lett"},{"key":"1030_CR43","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.nlm.2016.07.007","volume":"135","author":"DJ Morrison","year":"2016","unstructured":"Morrison DJ, Rashid AJ, Yiu AP, Yan C, Frankland PW, Josselyn SA (2016) Parvalbumin interneurons constrain the size of the lateral amygdala engram. Neurobiol Learn Mem 135:91\u201399. https:\/\/doi.org\/10.1016\/j.nlm.2016.07.007","journal-title":"Neurobiol Learn Mem"},{"issue":"1204","key":"1030_CR44","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1098\/rspb.1982.0078","volume":"216","author":"MC Morrone","year":"1982","unstructured":"Morrone MC, Burr DC, Maffei L (1982) Functional implications of cross-orientation Inhibition of cortical visual cells. I. Neurophysiological evidence. Proc Royal Soc Lond Ser B Biol Sci 216(1204):335\u2013354. https:\/\/doi.org\/10.1098\/rspb.1982.0078","journal-title":"Proc Royal Soc Lond Ser B Biol Sci"},{"key":"1030_CR46","doi-asserted-by":"publisher","unstructured":"Qu IM, Liu H, Li JD, Zhu Y (2024) Evolutionary algorithms support recurrent plasticity in spiking neural network models of neocortical task learning [Conference presentation]. Bernstein Conference. Frankfurt, Germany. https:\/\/doi.org\/10.12751\/nncn.bc2024.128","DOI":"10.12751\/nncn.bc2024.128"},{"issue":"5965","key":"1030_CR47","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1126\/science.1179850","volume":"327","author":"A Renart","year":"2010","unstructured":"Renart A, De La Rocha J, Bartho P, Hollender L, Parga N, Reyes A, Harris KD (2010) The asynchronous state in cortical circuits. Science 327(5965):587\u2013590. https:\/\/www.science.org\/doi\/full\/https:\/\/doi.org\/10.1126\/science.1179850","journal-title":"Science"},{"key":"1030_CR48","doi-asserted-by":"publisher","first-page":"e34044","DOI":"10.7554\/eLife.34044","volume":"7","author":"A Resulaj","year":"2018","unstructured":"Resulaj A, Ruediger S, Olsen SR, Scanziani M (2018) First spikes in visual cortex enable perceptual discrimination. Elife 7:e34044. https:\/\/elifesciences.org\/articles\/34044","journal-title":"Elife"},{"issue":"45","key":"1030_CR49","doi-asserted-by":"publisher","first-page":"16217","DOI":"10.1523\/JNEUROSCI.1677-11.2011","volume":"31","author":"A Roxin","year":"2011","unstructured":"Roxin A, Brunel N, Hansel D, Mongillo G, van Vreeswijk C (2011) On the distribution of firing rates in networks of cortical neurons. J Neurosci 31(45):16217\u201316226. https:\/\/www.jneurosci.org\/content\/31\/45\/16217.short","journal-title":"J Neurosci"},{"key":"1030_CR50","doi-asserted-by":"publisher","unstructured":"Sharmin S, Rathi N, Panda P, Roy K (2020) Inherent adversarial robustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXIX 16 (pp. 399\u2013414). Springer International Publishing. https:\/\/link.springer.com\/chapter\/https:\/\/doi.org\/10.1007\/978-3-030-58526-6_24","DOI":"10.1007\/978-3-030-58526-6_24"},{"issue":"49","key":"1030_CR51","doi-asserted-by":"publisher","first-page":"15595","DOI":"10.1523\/JNEUROSCI.3864-09.2009","volume":"29","author":"WL Shew","year":"2009","unstructured":"Shew WL, Yang H, Petermann T, Roy R, Plenz D (2009) Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J Neurosci 29(49):15595\u201315600. https:\/\/doi.org\/10.1523\/JNEUROSCI.3864-09.2009","journal-title":"J Neurosci"},{"issue":"3","key":"1030_CR52","doi-asserted-by":"publisher","first-page":"e68","DOI":"10.1371\/journal.pbio.0030068","volume":"3","author":"S Song","year":"2005","unstructured":"Song S, Sj\u00f6str\u00f6m PJ, Reigl M, Nelson S, Chklovskii DB (2005) Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol 3(3):e68. https:\/\/doi.org\/10.1371\/journal.pbio.0030068","journal-title":"PLoS Biol"},{"key":"1030_CR53","doi-asserted-by":"publisher","first-page":"2660","DOI":"10.1162\/neco_a_01113","volume":"30","author":"SJ Verzi","year":"2018","unstructured":"Verzi SJ, Rothganger F, Parekh OD, Quach T, Miner NE, Vineyard CM, James CD, Aimone JB (2018) Computing with spikes: the advantage of fine-grained timing. Neural Comput 30:2660\u20132690. https:\/\/doi.org\/10.1162\/neco_a_01113","journal-title":"Neural Comput"},{"key":"1030_CR54","doi-asserted-by":"publisher","unstructured":"Yu Z, Sun P, Goodman DF (2025) Beyond rate coding: surrogate gradients enable Spike timing learning in spiking neural networks. arXiv:2507.16043 [cs.NE] [Preprint] https:\/\/doi.org\/10.48550\/arXiv.2507.16043","DOI":"10.48550\/arXiv.2507.16043"},{"issue":"4","key":"1030_CR55","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1162\/neco_a_01367","volume":"33","author":"F Zenke","year":"2021","unstructured":"Zenke F, Vogels TP (2021) The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural Comput 33(4):899\u2013925. https:\/\/doi.org\/10.1162\/neco_a_01367","journal-title":"Neural Comput"},{"issue":"4","key":"1030_CR56","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1016\/j.celrep.2019.03.102","volume":"27","author":"Y Zerlaut","year":"2019","unstructured":"Zerlaut Y, Zucca S, Panzeri S, Fellin T (2019) The spectrum of asynchronous dynamics in spiking networks as a model for the diversity of non-rhythmic waking States in the neocortex. Cell Rep 27(4):1119\u20131132. https:\/\/doi.org\/10.1016\/j.celrep.2019.03.102","journal-title":"Cell Rep"},{"key":"1030_CR57","unstructured":"Zhu Y, Scherr F, Maass W, MacLean J (2020) November 9\u201312). Addition of neocortical features permits successful training of spiking neuronal network models [Conference presentation]. From Neuroscience to Artificially Intelligent Systems, Cold Spring Harbor Laboratory, NY, United States. https:\/\/meetings.cshl.edu\/meetings.aspx?meet=naisys&year=20"}],"container-title":["Biological Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00422-025-01030-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00422-025-01030-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00422-025-01030-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T05:11:38Z","timestamp":1772860298000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00422-025-01030-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,7]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["1030"],"URL":"https:\/\/doi.org\/10.1007\/s00422-025-01030-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4999644\/v1","asserted-by":"object"}]},"ISSN":["1432-0770"],"issn-type":[{"value":"1432-0770","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,7]]},"assertion":[{"value":"29 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2"}}