Bayati M et al. (2018). Storage fidelity for sequence memory in the hippocampal circuit. PloS one. 13 [PubMed]

Bertschinger N, Natschläger T. (2004). Real-time computation at the edge of chaos in recurrent neural networks. Neural computation. 16 [PubMed]

Brette R. (2012). Computing with neural synchrony. PLoS computational biology. 8 [PubMed]

• Computing with neural synchrony (Brette 2012) [Model]

Cone I, Shouval HZ. (2021). Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network. eLife. 10 [PubMed]

• Sequence learning via biophysically realistic learning rules (Cone and Shouval 2021) [Model]

Haeusler S, Maass W. (2007). A statistical analysis of information-processing properties of lamina-specific cortical microcircuit models. Cerebral cortex (New York, N.Y. : 1991). 17 [PubMed]

• Information-processing in lamina-specific cortical microcircuits (Haeusler and Maass 2006) [Model]

Ibañez S, Luebke JI, Chang W, Draguljić D, Weaver CM. (2019). Network Models Predict That Pyramidal Neuron Hyperexcitability and Synapse Loss in the dlPFC Lead to Age-Related Spatial Working Memory Impairment in Rhesus Monkeys. Frontiers in computational neuroscience. 13 [PubMed]

• Bump Attractor Models: Delayed Response & Recognition Span - spatial condition (Ibanez et al 2019) [Model]

Izhikevich EM. (2006). Polychronization: computation with spikes. Neural computation. 18 [PubMed]

• Polychronization: Computation With Spikes (Izhikevich 2005) [Model]

Joshi P, Maass W. (2005). Movement generation with circuits of spiking neurons. Neural computation. 17 [PubMed]

Karmarkar UR, Buonomano DV. (2007). Timing in the absence of clocks: encoding time in neural network states. Neuron. 53 [PubMed]

Kitano K, Fukai T. (2007). Variability v.s. synchronicity of neuronal activity in local cortical network models with different wiring topologies. Journal of computational neuroscience. 23 [PubMed]

Knüsel P, Wyss R, König P, Verschure PF. (2004). Decoding a temporal population code. Neural computation. 16 [PubMed]

Legenstein R, Pecevski D, Maass W. (2008). A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS computational biology. 4 [PubMed]

• Reward modulated STDP (Legenstein et al. 2008) [Model]

Lindsay GW, Rigotti M, Warden MR, Miller EK, Fusi S. (2017). Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex. The Journal of neuroscience : the official journal of the Society for Neuroscience. 37 [PubMed]

• Hebbian learning in a random network for PFC modeling (Lindsay, et al. 2017) [Model]

Maass W, Joshi P, Sontag ED. (2007). Computational aspects of feedback in neural circuits. PLoS computational biology. 3 [PubMed]

• Computational aspects of feedback in neural circuits (Maass et al 2006) [Model]

Maass W, Sontag ED, Joshi P. (2006). Principles of real-time computing with feedback applied to cortical microcircuit models. Advances in Neural Information Processing Systems. 18

• Computational aspects of feedback in neural circuits (Maass et al 2006) [Model]

Markram H, Maass W, Haeusler S. (2003). Perspectives of the high dimensional dynamics of neural microcircuits from the point of view of low dimensional readouts. Complexity (special issue on Complex Adaptive Systems). 8(4)

• High dimensional dynamics and low dimensional readouts in neural microcircuits (Haeusler et al 2006) [Model]

Matsubara T, Torikai H. (2016). An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities. IEEE transactions on neural networks and learning systems. 27 [PubMed]

Muresan RC, Savin C. (2007). Resonance or integration? Self-sustained dynamics and excitability of neural microcircuits. Journal of neurophysiology. 97 [PubMed]

Muscinelli SP, Gerstner W, Schwalger T. (2019). How single neuron properties shape chaotic dynamics and signal transmission in random neural networks. PLoS computational biology. 15 [PubMed]

• Single neuron properties shape chaos and signal transmission in random NNs (Muscinelli et al 2019) [Model]

Ozturk MC, Xu D, Príncipe JC. (2007). Analysis and design of echo state networks. Neural computation. 19 [PubMed]

Rössert C, Dean P, Porrill J. (2015). At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters. PLoS computational biology. 11 [PubMed]

• Basis for temporal filters in the cerebellar granular layer (Roessert et al. 2015) [Model]

Sanders H, Berends M, Major G, Goldman MS, Lisman JE. (2013). NMDA and GABAB (KIR) conductances: the "perfect couple" for bistability. The Journal of neuroscience : the official journal of the Society for Neuroscience. 33 [PubMed]

• NMDAR & GABAB/KIR Give Bistable Dendrites: Working Memory & Sequence Readout (Sanders et al., 2013) [Model]

Sanders H et al. (2014). A network that performs brute-force conversion of a temporal sequence to a spatial pattern: relevance to odor recognition. Frontiers in computational neuroscience. 8 [PubMed]

• NMDAR & GABAB/KIR Give Bistable Dendrites: Working Memory & Sequence Readout (Sanders et al., 2013) [Model]

Sussillo D, Abbott LF. (2009). Generating coherent patterns of activity from chaotic neural networks. Neuron. 63 [PubMed]

• Generating coherent patterns of activity from chaotic neural networks (Sussillo and Abbott 2009) [Model]

Talathi SS, Abarbanel HD, Ditto WL. (2008). Temporal spike pattern learning. Physical review. E, Statistical, nonlinear, and soft matter physics. 78 [PubMed]

Tino P, Mills AJ. (2006). Learning beyond finite memory in recurrent networks of spiking neurons. Neural computation. 18 [PubMed]

Tripp B, Eliasmith C. (2007). Neural populations can induce reliable postsynaptic currents without observable spike rate changes or precise spike timing. Cerebral cortex (New York, N.Y. : 1991). 17 [PubMed]

• Neural transformations on spike timing information (Tripp and Eliasmith 2007) [Model]

Versace M, Ames H, Léveillé J, Fortenberry B, Gorchetchnikov A. (2008). KInNeSS: a modular framework for computational neuroscience. Neuroinformatics. 6 [PubMed]

• KInNeSS : a modular framework for computational neuroscience (Versace et al. 2008) [Model]

Vertechi P, Brendel W, Machens CK. (2014). Unsupervised learning of an efficient short-term memory network Advances in Neural Information Processing Systems. 27

• Unsupervised learning of an efficient short-term memory network (Vertechi, Brendel & Machens 2014) [Model]

Wilson CJ, Beverlin B, Netoff T. (2011). Chaotic desynchronization as the therapeutic mechanism of deep brain stimulation. Frontiers in systems neuroscience. 5 [PubMed]

Yamazaki T, Tanaka S. (2007). A spiking network model for passage-of-time representation in the cerebellum. The European journal of neuroscience. 26 [PubMed]

• Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012) [Model]

Yamazaki T, Tanaka S. (2009). Robust Reservoir Generation by Correlation-Based Learning Advances in Artificial Neural Systems. 2009

• Robust Reservoir Generation by Correlation-Based Learning (Yamazaki & Tanaka 2008) [Model]

van Ooyen A et al. (2014). Independently outgrowing neurons and geometry-based synapse formation produce networks with realistic synaptic connectivity. PloS one. 9 [PubMed]

• NETMORPH: creates NNs with realistic neuron morphologies (Koene et al. 2009, van Ooyen et al. 2014) [Model]