Summary#

What are the key ideas? How can I apply this knowledge that I learned? How do these ideas relate to what I already know?

💡Ideas#

Note: These should become Project Idea fleeting notes with a link back to this source.

Fleeting Notes#

Note: Conceptual notes that aren’t as tied to the source material. These are fodder for processing into evergreen notes.

Literature Notes#

Note: Commentary on the book or things you don’t want to forget. Maybe for use in a public mind garden. Should have a visible source reference. These are “weakly evergreen” because they’re tied heavily to a single piece. Must be in your own words. These should be processed into Evergreen notes, or used as fodder for them. ““Literature notes”, titled after a single work and meant primarily as linkages to other more durable notes, and as targets for backlinks. I write these roughly as “outline notes,” except for someone else’s ideas.” These should be regularly refactored into individual notes in Sources/! Literature Notes/

TABLE rows.file.link AS "Literature Note", rows.file.cday AS "Date"

FROM #note/literature AND \[\[title]]

GROUP BY file.link

sort date ASCENDING

Highlights/Summaries#

Note: Go through these to restructure, and bold and highlight important pieces. These should be under an outline with chapters

[!PDF|yellow] Backpropagation through space time and the brain, p.1

In machine learning, the answer is almost universally given by the error backpropagation algorithm, through both space and time. However, this algorithm is well-known to rely on biologically implausible assumptions, in particular with respect to spatiotemporal locality.

[!PDF|yellow] Backpropagation through space time and the brain, p.1 We introduce Generalized Latent Equilibrium (GLE), a computational framework for fully local spatio-temporal credit assignment in physical, dynamical networks of neurons. We start by defining an energy based on neuron-local mismatches, from which we derive both neuronal dynamics via stationarity and parameter dynamics via gradient descent. The resulting dynamics can be interpreted as a real-time, biologically plausible approximation of backpropagation through space and time in deep cortical networks with continuous-time neuronal dynamics and continuously active, local synaptic plasticity

Introduction#