Lectures
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Introduction and perceptrons (4/20/2026)
Course goals and logistics. Introduction to perceptrons, the basic model of synaptic learning.
[slides] [notes]
Pre-lesson reading:
- Vectors (10 minute video)
- Dot product (the relevant part is the first 2 minutes 10 seconds, but feel free to watch the whole thing if you like)
Optional Material:
- Hertz, Krogh, Palmer Introduction to the theory of neural computation, chapters 5 and 6
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Neural Encoding (4/22/2026)
Review of theory for describing neural responses.
[slides]
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Neural Population Analysis 1 (4/27/2026)
Introduction to decoding. Linear Discriminant Analysis. Factor Analysis.
Advanced reading:
- Duda, Hart, Stork, Pattern Classification, chapters 2-5
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Neural Population Analysis 2 (4/29/2026)
Introduction to decoding. Linear Discriminant Analysis. Factor Analysis.
[slides]
Advanced reading:
- Duda, Hart, Stork, Pattern Classification, chapters 2-5
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Adaptation and plasticity (5/4/2026)
Neural adaptation. Maximizing information in a noisy neural system. Discussion of biophysical constraints and mechanisms of neural adaptation. Review of the Hodgkin Huxley model.
[slides]
Suggested reading:
- Laughlin 1981 (Maximizing a neuron’s information capacity)
- Van Hateren 1992 (Real and optimal neural images in early vision)
- Hennig 2013 (Theoretical models of synaptic short term plasticity)
- Ozuysal and Baccus 2012 (Linking the computational structure of variance adaptation to biophysical mechanisms)
Advanced reading:
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Hopfield Networks (5/6/2026)
Hopfield networks as models of content-addressable memory.
[slides]
Suggested reading:
- Hopfield 1982 (A classic paper)
- Review article by Chaudhuri and Fiete 2016 (Computational principles of memory)
Advanced reading:
- Hertz, Krogh, Palmer Introduction to the theory of neural computation, chapters 1-3
- Amit, Gutfreund, Sompolinsky Storing infinite numbers of patterns in a spin-glass model of neural networks, PRL 1985
Additional resources: