https://centre.santafe.edu/complextime/w/api.php?action=feedcontributions&user=DavidKrakauer&feedformat=atomComplex Time - User contributions [en]2024-03-28T16:32:22ZUser contributionsMediaWiki 1.35.6https://centre.santafe.edu/complextime/w/index.php?title=Cognitive_Regime_Shift_II_-_When/why/how_the_Brain_Breaks/DavidKrakauer&diff=5109Cognitive Regime Shift II - When/why/how the Brain Breaks/DavidKrakauer2019-11-14T00:41:36Z<p>DavidKrakauer: </p>
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<div>{{Attendee note<br />
|Post-meeting summary=Much of the emphasis was placed on describing the necessary basic principles, models or data, for describing brain functions. <br />
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These included:<br />
# Resting state correlations from imaging data<br />
# Behavioral psychological experiments<br />
# Local field potentials<br />
# Deep neural networks<br />
# Information theoretic formalisms.<br />
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Much emphasis was placed on either justifying or discovering appropriate levels for prediction and explanation. On this topic;<br />
# Is there a preferred level based on fundamental principles?<br />
# How to reconcile computational models (with strong time separation) with dynamical systems models (with a spectrum of time scales)<br />
# How to present and justify theoretical frameworks with many free parameters - theory for complex systems (in contrast to mere complication as in physics).<br />
# How to triangulate among levels of description<br />
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My own question dealt with the general problem: does the fact of the brain as a computational organ imply distinct regularities in the way in which it breaks?<br />
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One approach to this would be to ask about:<br />
# Robustness and adaptability<br />
# Critical transitions: order disorder regimes<br />
# Cascading failure and percolation.<br />
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This triplet provides a possible informal coordinate system in which to situate a system to include the brain. The rather unique scale and connectivity and general function of brain might suggest that it sit near a critical point, balanced between robust and adaptive regimes.<br />
|Reference material notes=Flack et al. 2012 summarizes our understanding of mechanisms that generate robustness (invariance of function to non-trivial perturbations) in biological and social systems. It provides a classification of these mechanisms in pursuit of more general principles that confer robustness at different time and space scales. <br />
}}</div>DavidKrakauerhttps://centre.santafe.edu/complextime/w/index.php?title=Cognitive_Regime_Shift_I_-_When_the_Brain_Breaks/%22Prion_dynamics_and_latency%22&diff=1156Cognitive Regime Shift I - When the Brain Breaks/"Prion dynamics and latency"2018-07-25T18:02:49Z<p>DavidKrakauer: </p>
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<div>{{Agenda item<br />
|Start time=July 23, 2018 01:30:00 PM<br />
|End time=July 23, 2018 02:20:00 PM<br />
|Presenter=DavidKrakauer<br />
|Pre-meeting notes=A large number of neurodegenerative diseases feature the accumulation of mis-folded proteins. These include prion diseases, Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. In all of these cases several different scales of organization are associated with disease progression or onset to include genetic, epigenetic, neural circuits, brain modules, and behavior. How should we best integrate data from each of these levels and what models and theories allow us to span levels? I shall discuss a few dynamical models of polymerization, protein accumulation, and protein diffusion through neural connections, that provide insights into disease progression at a number of different time and space scales. An ongoing challenge is a criterion for fixing thresholds that define an observable cognitive regime shift.<br />
|Post-meeting notes=A number of critical questions were raised about the best levels at which to establish causality when it comes to understanding both natural and disease-related aging. Namely what are the best observables to consider? Should these be single measurements or network based measurements. Could the best indicators involve comparisons across genetic and cognitive networks applying similar methods, or as is more typical time-dependent changes in a given network at one level of analysis. A recurring question was the relationship between energy and information and how their reciprocal dependencies change over the course of time and the course of disease.<br />
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Some very general issues that arose in conversation that require further exploration include:1. Approaching disease from a first-principles theoretical perspective - as is common in ecology - thus establishing principled data collection objectives (this would require a rigorous operational definition of the disease state in formal terms)<br />
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2. The value and limitation of the current inductive, big data approach, that focuses on time-dependent associations<br />
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3. The meaning of cognitive reserve, exercise or error correction, and the limits to these<br />
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4. How adaptive phenomena that are ongoing mitigate the disease state or at some point perhaps accelerate it. <br />
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5. How we might better explore causality in large systems with extensive non-linear feedback mechanisms.<br />
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