Abbott LF, DePasquale B, Memmesheimer RM. (2016). Building functional networks of spiking model neurons. Nature neuroscience. 19 [PubMed]
Abeles M. (1991). Corticonics: Neural Circuits of the Cerebral Cortex..
Adler A, Zhao R, Shin ME, Yasuda R, Gan WB. (2019). Somatostatin-Expressing Interneurons Enable and Maintain Learning-Dependent Sequential Activation of Pyramidal Neurons. Neuron. 102 [PubMed]
Billeh YN, Schaub MT. (2018). Feedforward architectures driven by inhibitory interactions. Journal of computational neuroscience. 44 [PubMed]
Brea J, Senn W, Pfister JP. (2013). Matching recall and storage in sequence learning with spiking neural networks. The Journal of neuroscience : the official journal of the Society for Neuroscience. 33 [PubMed]
Brette R, Gerstner W. (2005). Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of neurophysiology. 94 [PubMed]
Brunel N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of computational neuroscience. 8 [PubMed]
Chenkov N, Sprekeler H, Kempter R. (2017). Memory replay in balanced recurrent networks. PLoS computational biology. 13 [PubMed]
Clopath C, Büsing L, Vasilaki E, Gerstner W. (2010). Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nature neuroscience. 13 [PubMed]
Debanne D, Gähwiler BH, Thompson SM. (1999). Heterogeneity of synaptic plasticity at unitary CA3-CA1 and CA3-CA3 connections in rat hippocampal slice cultures. The Journal of neuroscience : the official journal of the Society for Neuroscience. 19 [PubMed]
Fee MS, Scharff C. (2010). The songbird as a model for the generation and learning of complex sequential behaviors. ILAR journal. 51 [PubMed]
Fiete IR, Senn W, Wang CZ, Hahnloser RH. (2010). Spike-time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity. Neuron. 65 [PubMed]
Geddes CE, Li H, Jin X. (2018). Optogenetic Editing Reveals the Hierarchical Organization of Learned Action Sequences. Cell. 174 [PubMed]
Gibbon J. (1977). Scalar expectancy theory and Weber's law in animal timing Psychol Rev. 84
Gilra A, Gerstner W. (2017). Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network. eLife. 6 [PubMed]
Glaze CM, Troyer TW. (2006). Temporal structure in zebra finch song: implications for motor coding. The Journal of neuroscience : the official journal of the Society for Neuroscience. 26 [PubMed]
Gütig R, Sompolinsky H. (2006). The tempotron: a neuron that learns spike timing-based decisions. Nature neuroscience. 9 [PubMed]
Hahnloser RH, Kozhevnikov AA, Fee MS. (2002). An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature. 419 [PubMed]
Hardy NF, Buonomano DV. (2018). Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model. Neural computation. 30 [PubMed]
Harvey CD, Coen P, Tank DW. (2012). Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature. 484 [PubMed]
Hemberger M, Shein-Idelson M, Pammer L, Laurent G. (2019). Reliable Sequential Activation of Neural Assemblies by Single Pyramidal Cells in a Three-Layered Cortex. Neuron. 104 [PubMed]
Ikegaya Y et al. (2004). Synfire chains and cortical songs: temporal modules of cortical activity. Science (New York, N.Y.). 304 [PubMed]
Itskov V, Curto C, Pastalkova E, Buzsáki G. (2011). Cell assembly sequences arising from spike threshold adaptation keep track of time in the hippocampus. The Journal of neuroscience : the official journal of the Society for Neuroscience. 31 [PubMed]
Jin DZ, Fujii N, Graybiel AM. (2009). Neural representation of time in cortico-basal ganglia circuits. Proceedings of the National Academy of Sciences of the United States of America. 106 [PubMed]
Jin X, Costa RM. (2015). Shaping action sequences in basal ganglia circuits. Current opinion in neurobiology. 33 [PubMed]
Jun JJ et al. (2017). Fully integrated silicon probes for high-density recording of neural activity. Nature. 551 [PubMed]
Ko H et al. (2013). The emergence of functional microcircuits in visual cortex. Nature. 496 [PubMed]
Laje R, Buonomano DV. (2013). Robust timing and motor patterns by taming chaos in recurrent neural networks. Nature neuroscience. 16 [PubMed]
Lashley KS. (1951). The problem of serial order in behavior Cerebral Mechanismsin Behavior (the Hixon Symposium).
Lee H, Choi W, Park Y, Paik SB. (2020). Distinct role of flexible and stable encodings in sequential working memory. Neural networks : the official journal of the International Neural Network Society. 121 [PubMed]
Leonardo A, Fee MS. (2005). Ensemble coding of vocal control in birdsong. The Journal of neuroscience : the official journal of the Society for Neuroscience. 25 [PubMed]
Litwin-Kumar A, Doiron B. (2014). Formation and maintenance of neuronal assemblies through synaptic plasticity. Nature communications. 5 [PubMed]
Maass W. (2016). Searching for principles of brain computation Current Opinion In Behavioral Sciences. 11
Maass W, Markram H. (2002). Synapses as dynamic memory buffers. Neural networks : the official journal of the International Neural Network Society. 15 [PubMed]
Maass W, Principe JC, Jaeger H. (2007). Echo state networks and liquid state machines Neural Networks. 20(3)
Mackevicius EL et al. (2019). Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. eLife. 8 [PubMed]
Manohar SG, Zokaei N, Fallon SJ, Vogels TP, Husain M. (2019). Neural mechanisms of attending to items in working memory. Neuroscience and biobehavioral reviews. 101 [PubMed]
Mastrogiuseppe F, Ostojic S. (2018). Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks. Neuron. 99 [PubMed]
Morrison A, Aertsen A, Diesmann M. (2007). Spike-timing-dependent plasticity in balanced random networks. Neural computation. 19 [PubMed]
Murray JM, Escola GS. (2017). Learning multiple variable-speed sequences in striatum via cortical tutoring. eLife. 6 [PubMed]
Nicola W, Clopath C. (2017). Supervised learning in spiking neural networks with FORCE training. Nature communications. 8 [PubMed]
Nicola W, Clopath C. (2019). A diversity of interneurons and Hebbian plasticity facilitate rapid compressible learning in the hippocampus. Nature neuroscience. 22 [PubMed]
Okubo TS, Mackevicius EL, Payne HL, Lynch GF, Fee MS. (2015). Growth and splitting of neural sequences in songbird vocal development. Nature. 528 [PubMed]
Park Y, Choi W, Paik SB. (2017). Symmetry of learning rate in synaptic plasticity modulates formation of flexible and stable memories. Scientific reports. 7 [PubMed]
Pastalkova E, Itskov V, Amarasingham A, Buzsáki G. (2008). Internally generated cell assembly sequences in the rat hippocampus. Science (New York, N.Y.). 321 [PubMed]
Peters AJ, Chen SX, Komiyama T. (2014). Emergence of reproducible spatiotemporal activity during motor learning. Nature. 510 [PubMed]
Rajan K, Harvey CD, Tank DW. (2016). Recurrent Network Models of Sequence Generation and Memory. Neuron. 90 [PubMed]
Raman DV, Rotondo AP, O'Leary T. (2019). Fundamental bounds on learning performance in neural circuits. Proceedings of the National Academy of Sciences of the United States of America. 116 [PubMed]
Rhodes BJ, Bullock D, Verwey WB, Averbeck BB, Page MP. (2004). Learning and production of movement sequences: behavioral, neurophysiological, and modeling perspectives. Human movement science. 23 [PubMed]
Sakai K, Kitaguchi K, Hikosaka O. (2003). Chunking during human visuomotor sequence learning. Experimental brain research. 152 [PubMed]
Schaub MT, Billeh YN, Anastassiou CA, Koch C, Barahona M. (2015). Emergence of Slow-Switching Assemblies in Structured Neuronal Networks. PLoS computational biology. 11 [PubMed]
Setareh H, Deger M, Gerstner W. (2018). Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation. PLoS computational biology. 14 [PubMed]
Sjöström PJ, Turrigiano GG, Nelson SB. (2001). Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron. 32 [PubMed]
Spreizer S, Aertsen A, Kumar A. (2019). From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks. PLoS computational biology. 15 [PubMed]
Sussillo D, Abbott LF. (2009). Generating coherent patterns of activity from chaotic neural networks. Neuron. 63 [PubMed]
Tanji J. (2001). Sequential organization of multiple movements: involvement of cortical motor areas. Annual review of neuroscience. 24 [PubMed]
Tao T. (2013). Outliers in the spectrum of iid matrices with bounded rank perturbations Probability Theory And Related Fields. 155
Tully PJ, Lindén H, Hennig MH, Lansner A. (2016). Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. PLoS computational biology. 12 [PubMed]
Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W. (2011). Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science (New York, N.Y.). 334 [PubMed]
Waddington A, Appleby PA, De Kamps M, Cohen N. (2012). Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity. Frontiers in computational neuroscience. 6 [PubMed]
Werbos PJ. (1990). Backpropagation through time: what it does and how to do it. Proc IEEE. 78
Zenke F, Agnes EJ, Gerstner W. (2015). Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nature communications. 6 [PubMed]
Zheng P, Triesch J. (2014). Robust development of synfire chains from multiple plasticity mechanisms. Frontiers in computational neuroscience. 8 [PubMed]
Cone I, Shouval HZ. (2021). Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network. eLife. 10 [PubMed]