TY  - JOUR
AU  - Dahmen, David
AU  - Hutt, Axel
AU  - Indiveri, Giacomo
AU  - Kennedy, Ann
AU  - Lefebvre, Jeremie
AU  - Mazzucato, Luca
AU  - Motter, Adilson E.
AU  - Narayanan, Rishikesh
AU  - Payvand, Melika
AU  - Planert, Henrike
AU  - Gast, Richard
TI  - How heterogeneity shapes dynamics and computation in the brain
JO  - Neuron
VL  - -
IS  - -
SN  - 0896-6273
CY  - [Cambridge, Mass.]
PB  - Cell Press
M1  - FZJ-2026-00465
SP  - -
PY  - 2025
AB  - 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.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1016/j.neuron.2025.11.023
UR  - https://juser.fz-juelich.de/record/1050720
ER  -