Most methods of sparse coding or ICA assume the 'pre-whitening' of inputs. @cstein06 shows that this is not necessary with a smart local Hebbian learning rule and ReLU neurons!
Paper just out in @PLOSCompBiol:
https://t.co/d3cFN4oxOm
Last spotlight of Cosyne Day 2 🔦
Meet @cstein06 our Audio-Video Chair (thank him for all the video recordings 🙏)
This is his 11th 🤯 Cosyne in a row, and it holds a special place in his heart bc it is around his birthday!! His first Cosyne talk was in Salt Lake 10 years ago!
Chairs lunch!!
If you are enjoying your time at #Cosyne24 you should thank these folks when you see them around!
From the program, to the workshops, to the video recordings, to the grants, to so so so much more we don’t see 👏👏👏
@TonyZador Initial turning points: you can use a=b as the initial point, to get c as the 2nd turning point. But I don't see how to get first turning point, would need to numerically sweep. Starting from 0. gets: (0., 2.5, -26.). From -1: (-1, 3.1, -45).
@TonyZador Given it's zig zag, for a (a,b,c) sequence of turning points, you can optimize the cost for b, take derivative, equal to zero, and get something like: pdf(b)*abs(b-c) - (1 - abs(cdf(b) - cdf(a))). From this, given a,b first turning points, you optimize c, and get a sequence.
Tremendously excited to be giving an RL tutorial at #cosyne2023!
Materials for the tutorial can be found here.
slides
https://t.co/2Iv4njlGNr
coding colab (File > Make a copy; Runtime > Change runtime type > GPU)
https://t.co/QZjb4NPq1w
@Samuel_Eckmann@DrYohanJohn@PessoaBrain@NoahGuzman14 Thanks for the mention. Might also be relevant here, in this recent paper we explain the functional form of Hebb rules (including BCM, triplet-STDP) as learning higher-order components (i.e. ICA, sparse coding) while ignoring second-order information: https://t.co/u7pPYy2yIs