Lectures
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Introduction and perceptrons (4/21/2025)
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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Oscillations (4/23/2025)
Network oscillations via coupled inhibitory cells (Wang and Buzsaki, 1996). Oscillations between up and down states (Wilson, 2005) in striatum, and their dependence on KIR channels. Thalamic relay neurons as intrinsic oscillators dependent on interactions between H current and T current, and how synaptic inhibitory responses engage this intrinsic oscillatory activity (McCormick and Pape, 1990).
[slides]
Suggested Reading:
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Neural Encoding (4/28/2025)
Review of theory for describing neural responses.
[slides]
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Neural population analysis (4/30/2025)
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 (5/5/2025)
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 (5/7/2025)
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: