Schedule

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

Suggested reading:

• Schwartz et. al. 2006 (a review of spike-triggered analysis)
• Fairhall 2006 (applies spike-triggered covariance analysis to retinal data)
• Mease et. al. 2013 (investigates adaptation in single neuron models)

Advanced reading:

• Aljadeff, Landsdell, Fairhall, Kleinfeld 2016. Analysis of Neuronal Spike Trains, Deconstructed
• Yamins and DiCarlo 2016

Advanced reading:

• Duda, Hart, Stork, Pattern Classification, chapters 2-5

Advanced reading:

• Duda, Hart, Stork, Pattern Classification, chapters 2-5

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:

• Atick and Redlich 1992 (What does the retina know about natural scenes?)
• Abbott and Nelson 2000 (Synaptic Plasticity: Taming the Beast)
• Mongillo, Barak, and Tsodyks 2008 (Synaptic theory of working memory)

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:

• Detailed lecture notes on Hopfield network