From membranes to interacting circuits

For: A learner who wants a physical and biological route from cells to small circuit models.

What you will work towards

• Explain why concentration and voltage both matter for ionic current.
• Distinguish an observed signal from a reduced model of it.
• Separate transmission, short-term synaptic dynamics and a plasticity rule.
• Describe what a population model adds to a single-cell explanation.

Known viewing time: 4 hr 43 min across 4 steps; 4 step durations unknown. Lengths are metadata, not evidence of review.

Download the ordered reading list

1. 01

   MIT OpenCourseWare · Source-checked annotation

   Lecture 3: Brain I

   MIT OpenCourseWare · Speaker not established · foundation · 54 min

   Establish the neuron and nervous-system vocabulary before the physical models.

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   Draw a neuron with input regions, cell body and output. Label this as a simplified diagram, not a complete biological inventory.

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2. 02

   MIT OpenCourseWare · Source-checked annotation

   1. Overview and Ionic Currents

   MIT OpenCourseWare · Michale Fee · advanced · 1 hr 10 min

   Move from a labelled cell to the physical sources of current.

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   Explain in separate sentences how a concentration difference and an electrical difference can influence movement of charge.

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3. 03

   MIT OpenCourseWare · Source-checked annotation

   2. RC Circuit and Nernst Potential

   MIT OpenCourseWare · Michale Fee · advanced · 1 hr 20 min

   Use the physical account to understand a membrane’s time-dependent response.

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   Sketch a response to a sustained current and mark which features a resistance–capacitance model is intended to represent.

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4. 04

   Neuromatch Academy — Computational Neuroscience · Source-checked annotation

   Video 1: Reduced Neuron Models

   Neuromatch Academy — Computational Neuroscience · Speaker not established · intermediate · Duration unknown

   Introduce a deliberately reduced spiking model after the circuit foundation.

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   List the threshold/reset rule and two biological details it leaves out. Distinguish a convenient rule from a measured mechanism.

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5. 05

   MIT OpenCourseWare · Source-checked annotation

   7. Synapses

   MIT OpenCourseWare · Michale Fee · advanced · 1 hr 18 min

   Connect cells through transmission rather than treating them as isolated units.

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   Explain why an arrow labelled with a fixed weight may omit saturation, time and input location.

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6. 06

   Neuromatch Academy — Computational Neuroscience · Source-checked annotation

   Video 1: Static and dynamic synapses

   Neuromatch Academy — Computational Neuroscience · Speaker not established · intermediate · Duration unknown

   Now let a connection’s immediate influence change during activity.

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   Compare a fixed connection with a temporarily changing one under the same hypothetical input train.

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7. 07

   Neuromatch Academy — Computational Neuroscience · Source-checked annotation

   Video 1: STDP

   Neuromatch Academy — Computational Neuroscience · Speaker not established · intermediate · Duration unknown

   Distinguish a learning rule from the short-term effects in the preceding step.

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   Write the observation that would be needed to decide whether a change persists beyond the input sequence.

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8. 08

   Neuromatch Academy — Computational Neuroscience · Source-checked annotation

   Video 1: Phase analysis of the Wilson-Cowan E-I model

   Neuromatch Academy — Computational Neuroscience · Speaker not established · advanced · Duration unknown

   Optional advanced branch: combine excitation and inhibition using coupled differential equations after the cell and synapse assumptions. Prepare calculus and phase-plane reasoning, plus linear algebra for stability extensions.

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   Name what the population variables stand for and one single-cell feature that the model does not resolve. Attempt equations/notebooks only with the stated mathematical/Python preparation.

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A short starting route

Steps 1, 2, 3, 4. These steps establish the cell-to-membrane-model chain before adding synaptic and population complexity.

Continue more deeply

Continue through the full MIT neural-computation sequence for dendrites, spike trains and recurrent networks. Continue at source ↗

Limits of this route

The route combines a 2011 introductory transcript opening, 2018 MIT descriptions and inspected Neuromatch tutorial sections. No notebook was executed and no whole-video viewing is claimed. The model sequence is an editorial teaching route, not independent validation of the biological completeness of any model.