- A CORF computational model of a simple cell that relies on LGN input (Azzopardi & Petkov 2012)
- A cardiac cell simulator (Puglisi and Bers 2001), applied to the QT interval (Busjahn et al 2004)
- A comparison of mathematical models of mood in bipolar disorder (Cochran et al. 2017)
- A detailed and fast model of extracellular recordings (Camunas-Mesa & Qurioga 2013)
- A dynamic model of the canine ventricular myocyte (Hund, Rudy 2004)
- A gap junction network of Amacrine Cells controls Nitric Oxide release (Jacoby et al 2018)
- A model for focal seizure onset, propagation, evolution, and progression (Liou et al 2020)
- A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)
- A neural mass model of cross frequency coupling (Chehelcheraghi et al 2017)
- A neurocomputational model of classical conditioning phenomena (Moustafa et al. 2009)
- A reinforcement learning example (Sutton and Barto 1998)
- A simple integrative electrophysiological model of bursting GnRH neurons (Csercsik et al. 2011)
- Activity constraints on stable neuronal or network parameters (Olypher and Calabrese 2007)
- Analyzing neural time series data theory and practice (Cohen 2014)
- Auditory nerve spontaneous rate histograms (Jackson and Carney 2005)
- Binocular energy model set for binocular neurons in optic lobe of praying mantis (Rosner et al 2019)
- Brain Dynamics Toolbox (Heitmann & Breakspear 2016, 2017, 2018)
- Cat auditory nerve model (Zilany and Bruce 2006, 2007)
- Cerebellar stellate cells: changes in threshold, latency and frequency of firing (Mitry et al 2020)
- Cochlear implant models (Bruce et al. 1999a, b, c, 2000)
- Continuous lateral oscillations as a mechanism for taxis in Drosophila larvae (Wystrach et al 2016)
- Disrupted information processing in Fmr1-KO mouse layer 4 barrel cortex (Domanski et al 2019)
- DynaSim: a MATLAB toolbox for neural modeling and simulation (Sherfey et al 2018)
- Dynamics of sleep oscillations coupled to brain temperature on multiple scales (Csernai et al 2019)
- Evaluation of stochastic diff. eq. approximation of ion channel gating models (Bruce 2009)
- Fixed point attractor (Hasselmo et al 1995)
- Gamma and theta rythms in biophysical models of hippocampus circuits (Kopell et al. 2011)
- Generating coherent patterns of activity from chaotic neural networks (Sussillo and Abbott 2009)
- High-Res. Recordings Using a Real-Time Computational Model of the Electrode (Brette et al. 2008)
- Hippocampal context-dependent retrieval (Hasselmo and Eichenbaum 2005)
- Hodgkinâ€“Huxley model with fractional gating (Teka et al. 2016)
- Human seizures couple across spatial scales through travelling wave dynamics (Martinet et al 2017)
- Implementation issues in approximate methods for stochastic Hodgkin-Huxley models (Bruce 2007)
- Inhibitory cells enable sparse coding in V1 model (King et al. 2013)
- Integrate and fire model code for spike-based coincidence-detection (Heinz et al. 2001, others)
- Levodopa-Induced Toxicity in Parkinson's Disease (Muddapu et al, 2022)
- Logarithmic distributions prove that intrinsic learning is Hebbian (Scheler 2017)
- Long-term adaptation with power-law dynamics (Zilany et al. 2009)
- Loss of phase-locking in non-weakly coupled inhib. networks of type-I neurons (Oh and Matveev 2009)
- MATLAB for brain and cognitive scientists (Cohen 2017)
- Mathematics for Neuroscientists (Gabbiani and Cox 2010)
- Mature and young adult-born dentate granule cell models (T2N interface) (Beining et al. 2017)
- Method for counting motor units in mice (Major et al 2007)
- Method of probabilistic principle surfaces (PPS) (Chang and Ghosh 2001)
- Microglial cytokine network (Anderson et al., 2015)
- Model of generalized periodic discharges in acute hepatic encephalopathy (Song et al 2019)
- Model of neural responses to amplitude-modulated tones (Nelson and Carney 2004)
- Modeling conductivity profiles in the deep neocortical pyramidal neuron (Wang K et al. 2013)
- Models analysis for auditory-nerve synapse (Zhang and Carney 2005)
- Models for diotic and dichotic detection (Davidson et al. 2009)
- Multi-timescale adaptive threshold model (Kobayashi et al 2009)
- Multiscale model of excitotoxicity in PD (Muddapu and Chakravarthy 2020)
- Multistability of clustered states in a globally inhibitory network (Chandrasekaran et al. 2009)
- NN for proto-object based contour integration and figure-ground segregation (Hu & Niebur 2017)
- Network topologies for producing limited sustained activation (Kaiser and Hilgetag 2010)
- Neural Mass Model for relationship between Brain Rhythms + Functional Connectivity (Ricci et al '21)
- Neural field model to reconcile structure with function in V1 (Rankin & Chavane 2017)
- Neural mass model of spindle generation in the isolated thalamus (Schellenberger Costa et al. 2016)
- Neural mass model of the neocortex under sleep regulation (Costa et al 2016)
- Neural mass model of the sleeping thalamocortical system (Schellenberger Costa et al 2016)
- Neural model of two-interval discrimination (Machens et al 2005)
- Neural recruitment during synchronous multichannel microstimulation (Hokanson et al 2018)
- NeuroManager: a workflow analysis based simulation management engine (Stockton & Santamaria 2015)
- Nodose sensory neuron (Schild et al. 1994, Schild and Kunze 1997)
- Nonlinear neuronal computation based on physiologically plausible inputs (McFarland et al. 2013)
- Phase-locking analysis with transcranial magneto-acoustical stimulation (Yuan et al 2017)
- Polychronization: Computation With Spikes (Izhikevich 2005)
- Prefrontal cortical mechanisms for goal-directed behavior (Hasselmo 2005)
- Prefrontalâ€“striatal Parkinsons comp. model of multicue category learning (Moustafa and Gluck 2011)
- Quantitative assessment of computational models for retinotopic map formation (Hjorth et al. 2015)
- Reduction of nonlinear ODE systems possessing multiple scales (Clewley et al. 2005)
- Response properties of an integrate and fire model (Zhang and Carney 2005)
- Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005)
- Single-cell comprehensive biophysical model of SN pars compacta (Muddapu & Chakravarthy 2021)
- Squid axon (Hodgkin, Huxley 1952) (LabAXON)
- Stimulated and physiologically induced APs: frequency and fiber diameter (Sadashivaiah et al 2018)
- Sympathetic neuron (Wheeler et al 2004)
- Synaptic damage underlies EEG abnormalities in postanoxic encephalopathy (Ruijter et al 2017)
- Synaptic strengths are critical in creating the proper output phasing in a CPG (Gunay et al 2019)
- The dynamics underlying pseudo-plateau bursting in a pituitary cell model (Teka et al. 2011)
- Two-cell inhibitory network bursting dynamics captured in a one-dimensional map (Matveev et al 2007)
- Voltage and light-sensitive Channelrhodopsin-2 model (ChR2) (Williams et al. 2013)

MATLAB integrates mathematical computing, visualization, and a powerful language to provide a flexible environment for technical computing. The open architecture makes it easy to use MATLAB and its companion products to explore data, create algorithms, and create custom tools that provide early insights and competitive advantages.

Top authors for MATLAB (web link to model):

- Carney LH (9)
- Bruce IC (9)
- Heitmann S (4)
- Breakspear M (4)
- Irlicht LS (4)
- White MW (4)
- O'Leary SJ (4)
- Clark GM (4)
- Colburn HS (4)
- Zilany MS (3)

Top concepts studied with MATLAB (web link to model):

- Methods (17)
- Activity Patterns (16)
- Simplified Models (15)
- Action Potentials (13)
- Oscillations (11)
- Audition (10)
- Temporal Pattern Generation (8)
- Bursting (7)
- Ion Channel Kinetics (6)
- Extracellular Fields (6)

Top neurons studied with MATLAB (web link to model):

- Auditory nerve (7)
- Hodgkin-Huxley neuron (5)
- Cardiac ventricular cell (3)
- Neocortex L5/6 pyramidal GLU cell (3)
- Thalamus reticular nucleus GABA cell (3)
- Substantia nigra pars compacta DA cell (3)
- Heart cell (2)
- Hippocampus CA1 pyramidal GLU cell (2)
- Myelinated neuron (2)
- Neocortex layer 5 interneuron (2)

Top currents studied with MATLAB (web link to model):

- I K (14)
- I Na,t (8)
- I Calcium (7)
- I h (6)
- I K,Ca (6)
- Na/K pump (5)
- Na/Ca exchanger (4)
- I T low threshold (3)
- Ca pump (3)
- I A (3)

Top references cited by these models:

- HODGKIN AL, HUXLEY AF. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology. 117 (12)
- Deco G, Jirsa VK, Robinson PA, Breakspear M, Friston K. (2008). The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS computational biology. 4 (9)
- Ermentrout GB. (2002). Simulating, Analyzing, and Animating Dynamical System: A Guide to XPPAUT for Researchers and Students Society for Industrial and Applied Mathematics (SIAM). (9)
- Hines ML, Carnevale NT. (1997). The NEURON simulation environment. Neural computation. 9 (7)
- Coombes S. (2005). Waves, bumps, and patterns in neural field theories. Biological cybernetics. 93 (7)

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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