Title: Dynamic compartmental computations in tuft dendrites of layer 5 neurons during motor behavior

Author: Yara Otor, Shay Achvat, Nathan Cermak, Hadas Benisty, Maisan Abboud, Omri Barak, Yitzhak Schiller, Alon Poleg-Polsky, Jackie Schiller

Issue&Volume: 2022-04-15

Abstract: Tuft dendrites of layer 5 pyramidal neurons form specialized compartments important for motor learning and performance, yet their computational capabilities remain unclear. Structural-functional mapping of the tuft tree from the motor cortex during motor tasks revealed two morphologically distinct populations of layer 5 pyramidal tract neurons (PTNs) that exhibit specific tuft computational properties. Early bifurcating and large nexus PTNs showed marked tuft functional compartmentalization, representing different motor variable combinations within and between their two tuft hemi-trees. By contrast, late bifurcating and smaller nexus PTNs showed synchronous tuft activation. Dendritic structure and dynamic recruitment of the N-methyl-D-aspartate (NMDA)–spiking mechanism explained the differential compartmentalization patterns. Our findings support a morphologically dependent framework for motor computations, in which independent amplification units can be combinatorically recruited to represent different motor sequences within the same tree.

DOI: abn1421

Source: https://www.science.org/doi/10.1126/science.abn1421