- A biophysical model of vestibular ganglion neurons (Hight & Kalluri 2016, Ventura & Kalluri 2018)
- A full-scale cortical microcircuit spiking network model (Shimoura et al 2018)
- A multiscale predictive digital twin for neurocardiac modulation (Yang et al., 2023)
- A sensorimotor-spinal cord model (Hoshino et al. 2022)
- A spiking neural network model of model-free reinforcement learning (Nakano et al 2015)
- A spiking NN for amplification of feature-selectivity with specific connectivity (Sadeh et al 2015)
- A state-space model to quantify common input to motor neurons (Feeney et al 2017)
- Active dendritic integration in robust and precise grid cell firing (Schmidt-Hieber et al 2017)
- Adaptive Generalized Leaky Integrate-and-Fire Model (AGLIF) (Marasco et al., 2023)
- Biophysical model for field potentials of networks of I&F neurons (beim Graben & Serafim 2013)
- Code to calc. spike-trig. ave (STA) conduct. from Vm (Pospischil et al. 2007, Rudolph et al. 2007)
- Complex dynamics: reproducing Golgi cell electroresponsiveness (Geminiani et al 2018, 2019ab)
- Dentate Gyrus model including Granule cells with dendritic compartments (Chavlis et al 2017)
- Diffusive homeostasis in a spiking network model (Sweeney et al. 2015)
- Disrupted information processing in Fmr1-KO mouse layer 4 barrel cortex (Domanski et al 2019)
- Double boundary value problem (A. Bose and J.E. Rubin, 2015)
- Dynamical patterns underlying response properties of cortical circuits (Keane et al 2018)
- E-I balance modulates formation and dynamics of neuronal assemblies (Sadeh and Clopath, 2021)
- Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010)
- eLIF and mAdExp: energy-based integrate-and-fire neurons (Fardet and Levina 2020)
- Emergence of spatiotemporal sequences in spiking neuronal networks (Spreizer et al 2019)
- Excitatory and inhibitory population activity (Bittner et al 2017) (Litwin-Kumar & Doiron 2017)
- Explainable AI for spatial navigation based on hippocampal circuitry (Coppolino + Migliore 2023)
- Feedforward network undergoing Up-state-mediated plasticity (Gonzalez-Rueda et al. 2018)
- Functional balanced networks with synaptic plasticity (Sadeh et al, 2015)
- Gap junction plasticity as a mechanism to regulate network-wide oscillations (Pernelle et al 2018)
- Generalized Carnevale-Hines algorithm (van Elburg and van Ooyen 2009)
- Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010)
- Grid cell spatial firing models (Zilli 2012)
- Hierarchical network model of perceptual decision making (Wimmer et al 2015)
- Hippocampal spiking model for context dependent behavior (Raudies & Hasselmo 2014)
- Hypothalamic CRH neurons represent physiological memory of positive and negative experience (Füzesi et al., 2023)
- I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014)
- Inhibition perturbations reveals dynamical structure of neural processing (Sadeh & Clopath 2020)
- Inhibitory cells enable sparse coding in V1 model (King et al. 2013)
- Inhibitory microcircuits for top-down plasticity of sensory representations (Wilmes & Clopath 2019)
- Inverse stochastic resonance of cerebellar Purkinje cell (Buchin et al. 2016)
- Leaky Integrate and Fire Neuron Model of Context Integration (Calvin, Redish 2021)
- Leaky integrate-and-fire model of spike frequency adaptation in the LGMD (Gabbiani and Krapp 2006)
- Learning spatiotemporal sequences using recurrent spiking NN that discretizes time (Maes et al 2020)
- MDD: the role of glutamate dysfunction on Cingulo-Frontal NN dynamics (Ramirez-Mahaluf et al 2017)
- Mean-field models of neural populations under electrical stimulation (Cakan & Obermayer 2020)
- Minimal model of interictal and ictal discharges “Epileptor-2” (Chizhov et al 2018)
- Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models (Marascoa et al., 2024)
- Modelling the effects of short and random proto-neural elongations (de Wiljes et al 2017)
- Models for cortical UP-DOWN states in a bistable inhibitory-stabilized network (Jercog et al 2017)
- Modulation of cortical Up-Down state switching by astrocytes (Moyse & Berry, 2022)
- Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)
- Neural transformations on spike timing information (Tripp and Eliasmith 2007)
- NeuroMatic: software for acquisition, analysis and simulation of e-phys data (Rothman & Silver 2018)
- Neuronify: An Educational Simulator for Neural Circuits (Dragly et al 2017)
- Norns - Neural Network Studio (Visser & Van Gils 2014)
- Olfactory Bulb mitral-granule network generates beta oscillations (Osinski & Kay 2016)
- On the structural connectivity of large-scale models of brain networks (Giacopelli et al 2021)
- Optimal Localist and Distributed Coding Through STDP (Masquelier & Kheradpisheh 2018)
- Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
- Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015)
- Oscillating neurons in the cochlear nucleus (Bahmer Langner 2006a, b, and 2007)
- Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
- Perfect Integrate and fire with noisy adaptation or fractional noise (Richard et al 2018)
- Population models of temporal differentiation (Tripp and Eliasmith 2010)
- Potjans-Diesmann cortical microcircuit model in NetPyNE (Romaro et al 2021)
- PyRhO: A multiscale optogenetics simulation platform (Evans et al 2016)
- Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
- Sequence learning via biophysically realistic learning rules (Cone and Shouval 2021)
- Short Term Depression, Presynaptic Inhib., Neuron Diversity Roles in Antennal Lobe (Wei & Lo 2020)
- SHOT-CA3, RO-CA1 Training, & Simulation CODE in models of hippocampal replay (Nicola & Clopath 2019)
- Single neuron models of four types of L1 mouse Interneurons: Canpy, NGFC, alpha7 and VIP cells
- Single Trial Sequence learning: a spiking neurons model based on hippocampus (Coppolino et al 2021)
- Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)
- Spiking neuron model of the basal ganglia (Humphries et al 2006)
- STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
- Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)
- Supervised learning in spiking neural networks with FORCE training (Nicola & Clopath 2017)
- Theoretical principles of DBS induced synaptic suppression (Farokhniaee & McIntyre 2019)
- Theory and simulation of integrate-and-fire neurons driven by shot noise (Droste & Lindner 2017)
- Time-dependent homeostatic mechanisms underlie Brain-Derived Neurotrophic Factor action on neural circuitry (O'Neill, 2023)
- Universal feature of developing networks (Tabak et al 2010)
- V1 and AL spiking neural network for visual contrast response in mouse (Meijer et al. 2020)

Top authors for Abstract integrate-and-fire leaky neuron:

- Clopath C (11)
- Sadeh S (5)
- Migliore M (5)
- Nicola W (5)
- Masquelier T (5)
- Bahmer A (5)
- Langner G (5)
- Rotter S (3)
- Rudolph M (3)
- Destexhe A (3)

Top concepts studied with Abstract integrate-and-fire leaky neuron:

- Activity Patterns (20)
- Oscillations (15)
- Simplified Models (12)
- Synaptic Plasticity (10)
- Action Potentials (8)
- Long-term Synaptic Plasticity (7)
- Methods (6)
- Spike Frequency Adaptation (6)
- STDP (6)
- Short-term Synaptic Plasticity (6)

Top neurons studied with Abstract integrate-and-fire leaky neuron:

- Abstract integrate-and-fire adaptive exponential (AdEx) neuron (5)
- Abstract Izhikevich neuron (3)
- Vestibular neuron (2)
- Entorhinal cortex stellate cell (2)
- Hippocampus CA1 pyramidal GLU cell (2)
- Abstract integrate-and-fire neuron (2)
- Neocortex L2/3 pyramidal GLU cell (2)
- Cerebellum Purkinje GABA cell (2)
- Hodgkin-Huxley neuron (2)
- Abstract theta neuron (2)

Top currents studied with Abstract integrate-and-fire leaky neuron:

- I Na,t (6)
- I K (5)
- I Potassium (3)
- I Sodium (3)
- I h (3)
- I A (2)
- I_AHP (2)
- I Na,p (2)
- I K,leak (2)
- I K,Ca (2)

Top references cited by these models:

- Brunel N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of computational neuroscience. 8 (14)
- Izhikevich EM. (2003). Simple model of spiking neurons. IEEE transactions on neural networks. 14 (11)
- Brette R, Gerstner W. (2005). Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of neurophysiology. 94 (9)
- Song S, Miller KD, Abbott LF. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature neuroscience. 3 (9)
- Goodman DF, Brette R. (2009). The brian simulator. Frontiers in neuroscience. 3 (8)

This database was founded as part of the SenseLab project which was supported by the Human Brain Project (NIDCD, NIMH, NIA, NICD, NINDS), by MURI (Multidisciplinary University Research Initiative), and by R01 DC 009977 from the National Institute for Deafness and other Communication Disorders.

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