Topic guide · Cells, circuits and brains / Computational approaches and AI
How do specialised brain areas work as systems?
Connect anatomy, activity relationships and circuit models without confusing one for another.
Topic guide · Computational approaches and AI / Cells, circuits and brains / Methods, evidence and reproducibility
How can a model help explain neural activity?
Connect modelling goals, simplified neuronal mechanisms, spike data and fitting exercises, with an optional route into network models.
Topic guide · Cells, circuits and brains / Computational approaches and AI
How does a neuron generate an electrical signal?
Follow a membrane signal from moving ions to a deliberately simplified model of a spike.
Topic guide · Methods, evidence and reproducibility / Computational approaches and AI
How do transparent choices make a result more reproducible?
Connect explicit analysis decisions and readable computation with the separate tasks of reproducing a result and testing a claim again.
Topic guide · Cells, circuits and brains / Learning, memory and education / Computational approaches and AI
How do synapses change a circuit’s response?
Separate transmission, short-term changes and longer-term learning rules before moving to networks.
Topic guide · Consciousness and philosophy / Methods, evidence and reproducibility / Computational approaches and AI
How can competing theories of consciousness be tested?
Distinguish a named theory, a particular prediction, a measurement and the interpretation of a result.
Lectures and explanations
Selections below prioritise source-checked annotations. Inclusion does not assess scientific correctness.
MIT OpenCourseWare · Source-checked annotation
2. RC Circuit and Nernst Potential
Models neuronal responses using membrane resistance, capacitance and an electrical-circuit analogy, introducing the source of neuronal potentials.
MIT OpenCourseWare · Source-checked annotation
11. Spectral Analysis Part 1
Introduces complex Fourier series, continuous and discrete transforms, and power spectra in a neural-computation course.
MIT OpenCourseWare · Source-checked annotation
14. Rate Models and Perceptrons
Introduces tractable network models through rate descriptions, receptive fields, vector algebra and perceptrons.
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.
MIT OpenCourseWare · Source-checked annotation
4. Hodgkin-Huxley Model Part 1
Introduces the Hodgkin–Huxley circuit model through voltage-clamp measurements and the time and voltage dependence of potassium current.
MIT OpenCourseWare · Source-checked annotation
6. Dendrites
Extends circuit modelling to dendrites, spatial dimensions and a two-compartment representation.
MIT OpenCourseWare · Source-checked annotation
18. Recurrent Networks
Introduces mathematical descriptions of recurrent networks in relation to memory storage and decision-making.
MIT OpenCourseWare · Source-checked annotation
9. Receptive Fields
Introduces mathematical descriptions of responses to stimuli, including receptive fields, separability and spike-triggered averaging.
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.
MIT OpenCourseWare · Source-checked annotation
20. Hopfield Networks
Examines attractor and winner-take-all networks, long-term memory models and Hopfield-network capacity.
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.
MIT OpenCourseWare · Source-checked annotation
12. Spectral Analysis Part 2
Moves from Fourier transforms to estimating spectra, filtering signals in the frequency domain and removing line noise.
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.
Brain Inspired · Source-checked annotation
BI 249 Michael Cole: Brain Flows are the Units of Cognition
Michael Cole discusses a view of cognition centred on flows of activity between brain regions and their network organisation.
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.
MIT OpenCourseWare · Source-checked annotation
5. Hodgkin-Huxley Model Part 2
Continues the Hodgkin–Huxley model, voltage-clamp analysis and the role of conductance in describing current.