This lesson provides an introduction to biologically detailed computational modelling of neural dynamics, including neuron membrane potential simulation and F-I curves.
In this lesson, users learn how to use MATLAB to build an adaptive exponential integrate and fire (AdEx) neuron model.
In this lesson, users learn about the practical differences between MATLAB scripts and functions, as well as how to embed their neuronal simulation into a callable function.
This lesson teaches users how to generate a frequency-current (F-I) curve, which describes the function that relates the net synaptic current (I) flowing into a neuron to its firing rate (F).
This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course.
This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.
This tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.
This lesson provides a brief introduction to the Computational Modeling of Neuronal Plasticity.
In this lesson, you will be introducted to a type of neuronal model known as the leaky integrate-and-fire (LIF) model.
This lesson goes over various potential inputs to neuronal synapses, loci of neural communication.
This lesson describes the how and why behind implementing integration time steps as part of a neuronal model.
In this lesson, you will learn about neural spike trains which can be characterized as having a Poisson distribution.
This lesson covers spike-rate adaptation, the process by which a neuron's firing pattern decays to a low, steady-state frequency during the sustained encoding of a stimulus.
This lesson provides a brief explanation of how to implement a neuron's refractory period in a computational model.
In this lesson, you will learn a computational description of the process which tunes neuronal connectivity strength, spike-timing-dependent plasticity (STDP).
This lesson reviews theoretical and mathematical descriptions of correlated spike trains.
This lesson investigates the effect of correlated spike trains on spike-timing dependent plasticity (STDP).
This lesson goes over synaptic normalisation, the homeostatic process by which groups of weighted inputs scale up or down their biases.
In this lesson, you will learn about the intrinsic plasticity of single neurons.
This lesson covers short-term facilitation, a process whereby a neuron's synaptic transmission is enhanced for a short (sub-second) period.