# From neurons and spike data to network models

Begin with physical and circuit models, connect them to synapses and spike data, then extend to receptive fields and recurrent memory models. Each step asks what the representation includes and what observation would challenge it. This is a selected route, with the full course available for omitted derivations.

1. [1. Overview and Ionic Currents](https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/resources/overview-and-ionic-currents/)
   Start with the physical ingredients of neuronal currents before replacing them with a simplified mathematical representation.
   Task: List the ingredients in the opening model and one observation each is intended to help explain.

2. [2. RC Circuit and Nernst Potential](https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/resources/2/)
   Translate those ingredients into the resistance-and-capacitance representation used by subsequent lectures.
   Task: Draw the correspondence between a component of the model and the feature it represents. Mark one simplification explicitly.

3. [3. Nernst Potential and Integrate and Fire Models](https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/resources/3/)
   Use the circuit preparation to examine an integrate-and-fire model rather than treating it as an unexplained equation.
   Task: State what event counts as a model spike and one biological feature the representation does not attempt to reproduce.

4. [7. Synapses](https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/resources/7/)
   Add interactions through synapses before proceeding from single-neuron representations to populations and networks.
   Task: Compare two model situations that differ in synaptic input. Predict a difference and state the assumption behind it.

5. [8. Spike Trains](https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/resources/8/)
   Connect the models to the spike signals and summaries available in recorded data.
   Task: Specify a data summary and a question it could answer. Describe information that the summary might discard.

6. [9. Receptive Fields](https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/resources/9/)
   Now use explicit response models to connect sensory inputs with neuronal activity.
   Task: Identify the stimulus and response variables in a receptive-field model. Propose a new stimulus that would test its limits.

7. [18. Recurrent Networks](https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/resources/18/)
   Extend from individual responses to recurrent networks that the course relates to memory and decision-making.
   Task: Write separately what the network is designed to do and what evidence would be required to treat it as an explanation of a biological system.

8. [20. Hopfield Networks](https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/resources/20/)
   Finish with attractor memory models and capacity, turning a network’s limits into an explicit object of study.
   Task: State what the model counts as successful storage or retrieval. Identify a failure condition and explain why it is informative rather than merely inconvenient.

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