Levels workshop: Difference between revisions
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The brain functions on multiple spatial and temporal scales spanning several orders of magnitude. Experimental neuroscientists and theoreticians alike tend to measure and model the brain at a single scale. regarding lower scales as 'implementational detail' and higher scales as phenomena to explain using the scale under study. While much progress has been made using this approach, | The brain functions on multiple spatial and temporal scales spanning several orders of magnitude. Experimental neuroscientists and theoreticians alike tend to measure and model the brain at a single scale. regarding lower scales as 'implementational detail' and higher scales as phenomena to explain using the scale under study. While much progress has been made using this approach, the success of this paradigm rests on the assumption that the process or mechanism being studied at one scale operates largely independently of lower and higher scales, despite the fact that descriptions at different scales are descriptions of the same thing at different resolutions. The central themes of this workshop are thus: to what extent can overall brain function be understood one scale at a time? What has been learned by measuring and modeling brain processes across scales? Is there an optimal scale to model brain function? What are the correlational dependencies across levels in neural data? What information is processed at a given scale, how much redundancy is there, how are signals conveyed between levels and what are the resulting upward and downward flows of information across time? | ||
the success of this paradigm rests on the assumption that the process or mechanism being studied at one scale | |||
operates largely independently of lower and higher scales, despite the fact that descriptions at different scales are descriptions of the same thing at different resolutions. The central themes of this workshop are thus: to what extent can overall brain function be understood one scale at a time? What has been learned by measuring and modeling brain processes across scales? Is there an optimal scale to model brain function? What are the correlational dependencies across levels in neural data? What information is processed at a given scale, how much redundancy is there, how are signals conveyed between levels and what are the resulting upward and downward flows of information across time? | |||
These are not new questions, but the tools emerging to answer them are. Our workshop speakers use experimental and theoretical techniques that give a window on brain dynamics from single neurons to whole brains, modeling data obtained from whole cell recordings, implantable multielectrode arrays, high-density EEG and epidural electrode arrays, fMRI, and MEG. The risk with such a workshop is that it fades into a series of loosely connected talks. We hope to avoid this veritable "Tower of Babel" by having speakers who not only have diverse multidisciplinary backgrounds, but whose research endeavors explicitly to span two or more scales of measurement and analysis: | These are not new questions, but the tools emerging to answer them are. Our workshop speakers use experimental and theoretical techniques that give a window on brain dynamics from single neurons to whole brains, modeling data obtained from whole cell recordings, implantable multielectrode arrays, high-density EEG and epidural electrode arrays, fMRI, and MEG. The risk with such a workshop is that it fades into a series of loosely connected talks. We hope to avoid this veritable "Tower of Babel" by having speakers who not only have diverse multidisciplinary backgrounds, but whose research endeavors explicitly to span two or more scales of measurement and analysis: | ||
Revision as of 01:08, 22 September 2007
The brain functions on multiple spatial and temporal scales spanning several orders of magnitude. Experimental neuroscientists and theoreticians alike tend to measure and model the brain at a single scale. regarding lower scales as 'implementational detail' and higher scales as phenomena to explain using the scale under study. While much progress has been made using this approach, the success of this paradigm rests on the assumption that the process or mechanism being studied at one scale operates largely independently of lower and higher scales, despite the fact that descriptions at different scales are descriptions of the same thing at different resolutions. The central themes of this workshop are thus: to what extent can overall brain function be understood one scale at a time? What has been learned by measuring and modeling brain processes across scales? Is there an optimal scale to model brain function? What are the correlational dependencies across levels in neural data? What information is processed at a given scale, how much redundancy is there, how are signals conveyed between levels and what are the resulting upward and downward flows of information across time?
These are not new questions, but the tools emerging to answer them are. Our workshop speakers use experimental and theoretical techniques that give a window on brain dynamics from single neurons to whole brains, modeling data obtained from whole cell recordings, implantable multielectrode arrays, high-density EEG and epidural electrode arrays, fMRI, and MEG. The risk with such a workshop is that it fades into a series of loosely connected talks. We hope to avoid this veritable "Tower of Babel" by having speakers who not only have diverse multidisciplinary backgrounds, but whose research endeavors explicitly to span two or more scales of measurement and analysis:
List of invitees:
| Tony Bell | "big picture" overview | cross-scale theoretical framework |
| Kilian Koepsell or Tim Blanche | spikes & LFP with polytrodes | meso scale GLM |
| Gyorgii Buzsaki | spikes & LFP with polytrodes | meso scale modeling |
| Peter Robinson, Jim Wright or David Liley | multimodal human data, esp. EEG | whole brain modeling |
| Jean-Philippe Lachaux | human eCog, EEG & MEG data | macro scale modeling |
| Pascal Fries or Thilo Womelsdorf | spikes and LFP | meso scale modeling |
| Charles Schroeder, Bressler or Lakatos | ? | ? |
| Ryan Canolty, Kai Miller or colleague | human eCog | macro scale modeling |
| David Mc Cormick | intra-/extracellular spike data | micro-meso scale modeling |
| Malach, Arieli, Fried, or Quiroga | human spike and LFP data | meso-macro scale modeling |
We hope you will join us for discussion and debate at what promises to be a very exciting workshop!
Other potential invitees
Grinvald, Tsodyks, Areli; Logothetis; Victor; Purpura; Fred Wolf; Yang Dan; Rob Fromke; Ken Harris; Bijan Pesaran; Terry Sejnowski; Larry Abbott; Jack Cowan; Walter Freeman; Elizabeth Buffalo; Paul Nunez or post-doc...
