Course or series
Neuromatch Academy: Computational Neuroscience
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 1: “What” Models
Introduces the descriptive-model section of a Neuromatch tutorial that explores spike recordings, visualisation and inter-spike-interval distributions.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 1: Linear Regression & Mean Squared Error
Introduces linear regression and mean squared error within a Neuromatch tutorial that asks learners to calculate error and fit a model parameter.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 1: Reduced Neuron Models
Introduces the reduction of a biological neuron to a leaky integrate-and-fire model, paired with a written explanation of its assumptions and a coding exercise.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 2: Response of the LIF neuron to different inputs
Introduces responses of a leaky integrate-and-fire neuron to different inputs, alongside demonstrations using constant and noisy currents.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 1: Input & output correlations
Connects correlated inputs with correlations in neuronal outputs, supported by exercises using pairs of simplified spiking neurons.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 2: Response of ensemble of neurons to time-varying input
A bonus lecture on how an ensemble of neurons responds to input that varies over time, following a tutorial about shared inputs and correlations.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 1: Static and dynamic synapses
Introduces static and dynamic synapses, with a notebook that contrasts fixed connection strengths with short-term facilitation and depression.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 1: STDP
Introduces a synaptic learning model in which weight changes depend on the relative timing of pre- and postsynaptic spikes.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 1: Dynamic networks
Introduces rate-based neural networks and an input-to-firing-rate relation for a single population of neurons.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 2: Fixed point
Introduces recurrent networks, fixed points and a one-dimensional phase description of changing activity.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 3: Stability of fixed points
Explains local stability by considering whether a small perturbation around a fixed point grows or decays.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 1: Phase analysis of the Wilson-Cowan E-I model
Introduces the Wilson–Cowan model of interacting excitatory and inhibitory populations and its two-dimensional phase plane.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 2: Nullclines and Vector Fields
Uses nullclines and vector fields to describe the directions in which excitatory and inhibitory population activity changes.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Video 1: Fixed points and their stability
Extends the two-population model to fixed points, local stability, oscillatory behaviour and inhibition-stabilised regimes.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Intro
Neuromatch’s Model Types introduction distinguishes descriptive, mechanistic and principle-seeking modelling goals using neuronal inter-spike intervals as a shared example.
Neuromatch Academy — Computational Neuroscience · Source-checked annotation
Intro
Introduces the biological complexity behind neuron and synapse models, including electrical signalling and the route into simplified computational descriptions.