Models that contain the Model Concept : Tutorial/Teaching

(This model is designed to help educate investigators or students.)
Re-display model names without descriptions
    Models   Description
1.  A cardiac cell simulator (Puglisi and Bers 2001), applied to the QT interval (Busjahn et al 2004)
"LabHEART is an easy to use program that simulates the cardiac action potential, calcium transient and ionic currents. Key parameters such as ionic concentration, stimulus waveform and channel conductance can easily be changed by a click on an icon or dragging a slider. It is a powerfull tool for teaching and researching cardiac electrophysiology."
2.  Activity dependent changes in motoneurones (Dai Y et al 2002, Gardiner et al 2002)
These two papers review various experimental papers and examine the effects of activity on motoneurons in a similar 5 compartment model with 10 active conductances. Included are slow (S) and fast (F) type and fast fatigue resistant (FR) and fast fatigable (FF) models corresponding to the types of motoneurons. See papers for more and details.
3.  Analyzing neural time series data theory and practice (Cohen 2014)
"This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals."
4.  Artificial neuron model (Izhikevich 2003, 2004, 2007)
A set of models is presented based on 2 related parameterizations to reproduce spiking and bursting behavior of multiple types of cortical neurons and thalamic neurons. These models combine the biologically plausibility of Hodgkin Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using these model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.
5.  Auditory cortex layer IV network model (Beeman 2013)
"... The primary objective of this modeling study was to determine the effects of axonal conduction velocity (often neglected, but significant), as well as synaptic time constants, on the ability of such a network to create and propagate cortical waves. ... The model is also being used to study the interaction between single and two-tone input and normal background activity, and the effects of synaptic depression from thalamic inputs. The simulation scripts have the additional purpose of serving as tutorial examples for the construction of cortical networks with GENESIS. The present model has fostered the development of the G-3 Python network analysis and visualization tools used in this study... It is my hope that this short tutorial and the example simulation scripts can provide a head start for a graduate student or postdoc who is beginning a cortical modeling project. "
6.  Calculating the consequences of left-shifted Nav channel activity in sick cells (Joos et al 2018)
"Two features common to diverse sick excitable cells are “leaky” Nav channels and bleb damage-damaged membranes. The bleb damage, we have argued, causes a channel kinetics based “leakiness.” Recombinant (node of Ranvier type) Nav1.6 channels voltage-clamped in mechanically-blebbed cell-attached patches undergo a damage intensity dependent kinetic change. Specifically, they experience a coupled hyperpolarizing (left) shift of the activation and inactivation processes. The biophysical observations on Nav1.6 currents formed the basis of Nav-Coupled Left Shift (Nav-CLS) theory. Node of Ranvier excitability can be modeled with Nav-CLS imposed at varying LS intensities and with varying fractions of total nodal membrane affected. Mild damage from which sick excitable cells might recover is of most interest pathologically. Accordingly, Na+/K+ ATPase (pump) activity was included in the modeling. As we described more fully in our other recent reviews, Nav-CLS in nodes with pumps proves sufficient to predict many of the pathological excitability phenomena reported for sick excitable cells. ..."
7.  Cerebellar purkinje cell (De Schutter and Bower 1994)
Tutorial simulation of a cerebellar Purkinje cell. This tutorial is based upon a GENESIS simulation of a cerebellar Purkinje cell, modeled and fine-tuned by Erik de Schutter. The tutorial assumes that you have a basic knowledge of the Purkinje cell and its synaptic inputs. It gives visual insight in how different properties as concentrations and channel conductances vary and interact within a real Purkinje cell.
8.  Discrete event simulation in the NEURON environment (Hines and Carnevale 2004)
A short introduction to how "integrate and fire" cells are implemented in NEURON. Network simulations that use only artificial spiking cells are extremely efficient, with runtimes proportional to the total number of synaptic inputs received and independent of the number of cells or problem time.
9.  Effect of trp-like current on APs during exposure to sinusoidal voltage (Chen et al. 2010)
"... Previous work showed that magnetic electrical field-induced antinoceptive action is mediated by activation of capsaicin-sensitive sensory afferents. In this study, a modified Hodgkin-Huxley model, in which TRP-like current (I-TRP) was incorporated, was implemented to predict the firing behavior of action potentials (APs), as the model neuron was exposed to sinusoidal changes in externally-applied voltage. ... Our simulation results suggest that modulation of TRP-like channels functionally expressed in small-diameter peripheral sensory neurons should be an important mechanism through which it can contribute to the firing pattern of APs."
10.  Leech heart interneuron network model (Hill et al 2001, 2002)
We have created a computational model of the timing network that paces the heartbeat of the medicinal leech, Hirudo medicinalis. In the intact nerve cord, segmental oscillators are mutually entrained to the same cycle period. Although experiments have shown that the segmental oscillators are coupled by inhibitory coordinating interneurons, the underlying mechanisms of intersegmental coordination have not yet been elucidated. To help understand this coordination, we have created a simple computational model with two variants: symmetric and asymmetric. See references for more details. Biologically realistic network models with two, six, and eight cells and a tutorial are available at the links to Calabrese's web site below.
11.  LGNcircuit: Minimal LGN network model of temporal processing of visual input (Norheim et al. 2012)
The responses of relay cells in the lateral geniculate nucleus (LGN) are shaped by their diverse set of impinging inputs: feedforward synaptic inputs stemming from retina, and feedback inputs stemming from the visual cortex and the thalamic reticular nucleus. This MATLAB model, with an easy-to-use graphical user interface (GUI), explores possible roles of these feedforward and feedback inputs in shaping the temporal part of the receptive fields of LGN relay cells with, so called, ON symmetry. A minimal mechanistic firing-rate model tailored to elucidate salient feedforward and feedback effects is considered including, in particular, feedforward excitation and inhibition (via interneurons) from retinal ON cells and excitatory and inhibitory (via thalamic reticular nucleus cells and interneurons) feedback from cortical ON and OFF cells. Various types of visual stimuli can be explored: flashing spots, impulses, sinusoidal gratings.
12.  Mathematical Foundations of Neuroscience (Ermentrout and Terman 2010)
Ermentrout and Terman's book on dynamical systems and computational methods in neuroscience has associated computer code available at The main emphasis in the book is on single-neuron biophysics and there is also systems neuroscience theory and applications to networks. The electronic text of the book is freely available!
13.  Mathematics for Neuroscientists (Gabbiani and Cox 2010)
This textbook provides a good source for learning the mathematics relevant to computational neuroscience and also the neuroscience itself. There are 232 computer code examples from the book available through the code link here and in the below page copied from the books companion web site.
14.  MATLAB for brain and cognitive scientists (Cohen 2017)
" ... MATLAB for Brain and Cognitive Scientists takes readers from beginning to intermediate and advanced levels of MATLAB programming, helping them gain real expertise in applications that they will use in their work. The book offers a mix of instructive text and rigorous explanations of MATLAB code along with programming tips and tricks. The goal is to teach the reader how to program data analyses in neuroscience and psychology. Readers will learn not only how to but also how not to program, with examples of bad code that they are invited to correct or improve. Chapters end with exercises that test and develop the skills taught in each chapter. Interviews with neuroscientists and cognitive scientists who have made significant contributions to their field using MATLAB appear throughout the book. ..."
15.  MyFirstNEURON (Houweling, Sejnowski 1997)
MyFirstNEURON is a NEURON demo by Arthur Houweling and Terry Sejnowski. Perform experiments from the book 'Electrophysiology of the Neuron, A Companion to Shepherd's Neurobiology, An Interactive Tutorial' by John Huguenard & David McCormick, Oxford University Press 1997, or design your own one or two cell simulation.
16.  Neuronal morphology goes digital ... (Parekh & Ascoli 2013)
An illustration of a NEURON model and why reconstructing morphologies is useful in this regard (i.e. investigating spatial/temporal aspect of how different currents and voltage propagate in dendrites).
17.  Neuronify: An Educational Simulator for Neural Circuits (Dragly et al 2017)
"Neuronify, a new educational software application (app) providing an interactive way of learning about neural networks, is described. Neuronify allows students with no programming experience to easily build and explore networks in a plug-and-play manner picking network elements (neurons, stimulators, recording devices) from a menu. The app is based on the commonly used integrate-and-fire type model neuron and has adjustable neuronal and synaptic parameters. ..."
18.  Norns - Neural Network Studio (Visser & Van Gils 2014)
The Norns - Neural Network Studio is a software package for designing, simulation and analyzing networks of spiking neurons. It consists of three parts: 1. "Urd": a Matlab frontend with high-level functions for quickly defining networks 2. "Verdandi": an optimized C++ simulation environment which runs the simulation defined by Urd 3. "Skuld": an advanced Matlab graphical user interface (GUI) for visual inspection of simulated data.
19.  Prediction for the presence of voltage-gated Ca2+ channels in myelinated central axons (Brown 2003)
"The objective of this current study was to investigate whether voltage gated Ca(2+) channels are present on axons of the adult rat optic nerve (RON). Simulations of axonal excitability using a Hodgkin-Huxley based one-compartment model incorporating I(Na), I(K) and leak currents were used to predict conditions under which the potential contribution of a Ca(2+) current to an evoked action potential could be measured. ... , as predicted by the simulation, reducing the repolarizing effect of I(K) by adding the K(+) channel blocker 4-AP revealed a Ca(2+) component on the repolarizing phase of the action potential that was blocked by the Ca(2+) channel inhibitor nifedipine."
20.  Principles of Computational Modelling in Neuroscience (Book) (Sterratt et al. 2011)
"... This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience."
21.  Reconstrucing sleep dynamics with data assimilation (Sedigh-Sarvestani et al., 2012)
We have developed a framework, based on the unscented Kalman filter, for estimating hidden states and parameters of a network model of sleep. The network model includes firing rates and neurotransmitter output of 5 cell-groups in the rat brain.
22.  Software for teaching neurophysiology of neuronal circuits (Grisham et al. 2008)
"To circumvent the many problems in teaching neurophysiology as a “wet lab,” we developed SWIMMY, a virtual fish that swims by moving its virtual tail by means of a virtual neural circuit. ... Using SWIMMY, students (1) review the basics of neurophysiology, (2) identify the neurons in the circuit, (3) ascertain the neurons’ synaptic interconnections, (4) discover which cells generate the motor pattern of swimming, (5) discover how the rhythm is generated, and finally (6) use an animation that corresponds to the activity of the motoneurons to discover the behavioral effects produced by various lesions and explain them in terms of their neural underpinnings. ..."
23.  Software for teaching the Hodgkin-Huxley model (Hernandez & Zurek 2013) (SENB written in NEURON hoc)
" ... The SENB software offers several advantages for teaching and learning electrophysiology. First, SENB offers ease and flexibility in determining the number of stimuli. Second, SENB allows immediate and simultaneous visualization, in the same window and time frame, of the evolution of the electrophysiological variables. Third, SENB calculates parameters such as time and space constants, stimuli frequency, cellular area and volume, sodium and potassium equilibrium potentials, and propagation velocity of the action potentials. ..."
24.  Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001)
A heuristic for compartmentalization based on the space constant at 100 Hz is proposed. The paper also discusses spatio/temporal accuracy and the use of CVODE.
25.  Spinal Motor Neuron (Dodge, Cooley 1973)
"The excitability of various regions of the spinal motorneuron can be specified by solving the partial differential equation of a nerve fiber whose diameter and membrane properties vary with distance. For our model geometrical factors for the myelinated axon, initial segment and cell body were derived from anatomical measurements, the dendritic tree was represented by its equivalent cylinder, and the current-voltage relations of the membrane were described by a modification of the Hodgkin-Huxley model that fits voltage-clamp data from the motorneuron. ..."
26.  Thalamic neuron: Modeling rhythmic neuronal activity (Meuth et al. 2005)
The authors use an in vitro cell model of a single acutely isolated thalamic neuron in the NEURON simulation environment to address and discuss questions in an undergraduate course. Topics covered include passive electrical properties, composition of action potentials, trains of action potentials, multicompartment modeling, and research topics. The paper includes detailed instructions on how to run the simulations in the appendix.
27.  Thalamic Reticular Network (Destexhe et al 1994)
Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994)
28.  The cannula artifact (Chandler & Hodgkin 1965)
Chandler and Hodgkin 1965 describes how using a high impedance electrode can lead to squid axon recordings that appear to overshoot the sodium reversal potential, thus resolving controversial recordings at the time.

Re-display model names without descriptions