Difference between revisions of "Cognitive Regime Shift II - When/why/how the Brain Breaks/JacopoGrilli"
(Created page with "{{Attendee note |Reference material notes=Podolsky et al find, In the context of regulatory networks and expression profiles, a connection between critical dynamics (the gene...") |
JacopoGrilli (talk | contribs) |
||
| Line 1: | Line 1: | ||
{{Attendee note | {{Attendee note | ||
| + | |Post-meeting summary=I am stuck with a picture of aging and collapse, motivated by catastrophic shifts in ecology, which simply takes the form of a saddle-node bifurcation. A functional and dysfunctional system are separated by some energy barrier. Aging (somewhat by definition) corresponds to decreasing energy barrier height (and therefore increasing probability of transition). This (at this level tautological) view comes with two interesting consequences: | ||
| + | |||
| + | - (critical) slowing down: the typical timescale at which fluctuations relax increases over time | ||
| + | |||
| + | - in multidimensional system there is an effective one dimensional trajectory describing collapse | ||
| + | |||
| + | The latter point, suggests high reproducibility in collapse trajectories. At what scale this framework is useful is unclear to me. At the coarser scale, when only two states exist (functional and not functional) the only thing that matters is transition probability (the when, and there is no how and why). At that scale bridges and brains fail in the same way (as lifetime distributions sort of match). I am very confused about the confusion around the scale(s) at which we want to study aging and breaking of brains. | ||
| + | |||
| + | I found extremely interesting the discussion of machine learning / neural networks as toy models of representation and/or learning in brains. | ||
|Reference material notes=Podolsky et al find, In the context of regulatory networks and expression profiles, a connection between critical dynamics (the gene regulatory network is at the edge of stability) and aging. This link between criticality (often associated to "functionality" and flexibility) and aging is particularly intriguing also if translated into the context of neural networks and brain diseases. | |Reference material notes=Podolsky et al find, In the context of regulatory networks and expression profiles, a connection between critical dynamics (the gene regulatory network is at the edge of stability) and aging. This link between criticality (often associated to "functionality" and flexibility) and aging is particularly intriguing also if translated into the context of neural networks and brain diseases. | ||
}} | }} | ||
Latest revision as of 00:50, November 14, 2019
Notes by user Jacopo Grilli (ICTP) for Cognitive Regime Shift II - When/why/how the Brain Breaks
Post-meeting Reflection
1+ paragraphs on any combination of the following:
- Presentation highlights
- Open questions that came up
- How your perspective changed
- Impact on your own work
- e.g. the discussion on [A] that we are having reminds me of [B] conference/[C] initiative/[D] funding call-for-proposal/[E] research group
I am stuck with a picture of aging and collapse, motivated by catastrophic shifts in ecology, which simply takes the form of a saddle-node bifurcation. A functional and dysfunctional system are separated by some energy barrier. Aging (somewhat by definition) corresponds to decreasing energy barrier height (and therefore increasing probability of transition). This (at this level tautological) view comes with two interesting consequences:
- (critical) slowing down: the typical timescale at which fluctuations relax increases over time
- in multidimensional system there is an effective one dimensional trajectory describing collapse
The latter point, suggests high reproducibility in collapse trajectories. At what scale this framework is useful is unclear to me. At the coarser scale, when only two states exist (functional and not functional) the only thing that matters is transition probability (the when, and there is no how and why). At that scale bridges and brains fail in the same way (as lifetime distributions sort of match). I am very confused about the confusion around the scale(s) at which we want to study aging and breaking of brains.
I found extremely interesting the discussion of machine learning / neural networks as toy models of representation and/or learning in brains.
Reference material notes
Some examples:
- Here is [A] database on [B] that I pull data from to do [C] analysis that might be of interest to this group (insert link).
- Here is a free tool for calculating [ABC] (insert link)
- This painting/sculpture/forms of artwork is emblematic to our discussion on [X]!
- Schwartz et al. 2017 offers a review on [ABC] migration as relate to climatic factors (add the reference as well).
Podolsky et al find, In the context of regulatory networks and expression profiles, a connection between critical dynamics (the gene regulatory network is at the edge of stability) and aging. This link between criticality (often associated to "functionality" and flexibility) and aging is particularly intriguing also if translated into the context of neural networks and brain diseases.
Reference Materials
| Title | Author name | Source name | Year | Citation count From Scopus. Refreshed every 5 days. | Page views | Related file |
|---|---|---|---|---|---|---|
| Critical dynamics of gene networks is a mechanism behind ageing and Gompertz law | Dmitriy Podolskiy, Ivan Molodtsov, Alexander Zenin, Valeria Kogan, Leonid I. Menshikov, Vadim N. Gladyshev, Robert J. Shmookler Reis, Peter O. Fedichev | q-bio.MN | 2016 | 0 | 2 | Download (Encrypted) |
