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| 005 | 20260115133845.0 | ||
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| 100 | 1 | _ | |a Dahmen, David |0 P:(DE-Juel1)156459 |b 0 |u fzj |
| 245 | _ | _ | |a How heterogeneity shapes dynamics and computation in the brain |
| 260 | _ | _ | |a [Cambridge, Mass.] |c 2025 |b Cell Press |
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| 520 | _ | _ | |a Much effort has been spent clustering neurons into transcriptomic or functional cell types and characterizingthe differences between them. Beyond subdividing neurons into categories, we must recognize that no twoneurons are identical and that graded physiological or transcriptomic properties exist within cells of agiven type. This often overlooked ‘‘within-type’’ heterogeneity is a specific neuronal implementation ofwhat statistical physics refers to as ‘‘disorder’’ and exhibits rich computational properties, the identificationof which may shed crucial insights into theories of brain function. In this perspective article, we address thisgap by highlighting theoretical frameworks for the study of neural tissue heterogeneity and discussing thebenefits and implications of within-type heterogeneity for neural network dynamics, computation, andself-organization. |
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| 700 | 1 | _ | |a Lefebvre, Jeremie |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Mazzucato, Luca |0 P:(DE-HGF)0 |b 5 |
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| 700 | 1 | _ | |a Gast, Richard |0 P:(DE-HGF)0 |b 10 |e Corresponding author |
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