Journal Article

FZJ-2026-00465

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png How heterogeneity shapes dynamics and computation in the brain

Dahmen, D.FZJ* ; Hutt, A. ; Indiveri, G. ; Kennedy, A. ; Lefebvre, J. ; Mazzucato, L. ; Motter, A. E. ; Narayanan, R. ; Payvand, M. ; Planert, H. ; Gast, R. (Corresponding author)

2025
Cell Press [Cambridge, Mass.]

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Please use a persistent id in citations: doi:10.1016/j.neuron.2025.11.023

Abstract: 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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Contributing Institute(s):

1. Computational and Systems Neuroscience (IAS-6)

Research Program(s):

1. 5231 - Neuroscientific Foundations (POF4-523) (POF4-523)
2. 5232 - Computational Principles (POF4-523) (POF4-523)
3. DFG project G:(GEPRIS)430157073 - Evolutinäre Konvergenz hierarchischer Informationsverarbeitung (430157073) (430157073)

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Database coverage:
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 15 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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