Karthik Srinivasan on Nostr: npub1skvad…laky3 npub15swlx…zx855 Yeah... 1 is a red herring. What does low dim ...
npub1skvad2l2wrxgdmt6yxk9kt2rjhw5tucjzhf54pktfq2gg0qhgwyqdlaky3 (npub1skv…aky3) npub15swlxudlhx4ttcgsd4556zuqrl57qndxmt4n3dnzrkqn89nxv6lsjzx855 (npub15sw…x855)
Yeah... 1 is a red herring. What does low dim have to do with attractors in state space (especially given the review is concerned with mostly fixed points).
2 is absolutely crucial.
3 as Yohan said need not hold given anything can bifurcate the system (unless the system is linear, in which case, boring).
4 makes no sense. Why should the isometry of distance be equal to/proportional to the equal displacement dimension of the external variable? For example: We have six orders of magnitude change in the luminance of visual inputs. An integrator network performing lateral inhibition should exhibit isometry? That flies against the face of Weber-Fechne. And that is just the retina. Why should anything like this be adhered elsewhere in the brain. Nonlinear compression is inevitable.
Yeah... 1 is a red herring. What does low dim have to do with attractors in state space (especially given the review is concerned with mostly fixed points).
2 is absolutely crucial.
3 as Yohan said need not hold given anything can bifurcate the system (unless the system is linear, in which case, boring).
4 makes no sense. Why should the isometry of distance be equal to/proportional to the equal displacement dimension of the external variable? For example: We have six orders of magnitude change in the luminance of visual inputs. An integrator network performing lateral inhibition should exhibit isometry? That flies against the face of Weber-Fechne. And that is just the retina. Why should anything like this be adhered elsewhere in the brain. Nonlinear compression is inevitable.