Dendritic spines, small protrusions emerging from the dendrites of most excitatory synapses in the mammalian brain, are highly dynamic structures and their shape and number is continuously modulated by memory formation and other adaptive changes of the brain. In this study, using a behavioral paradigm of motor learning, we applied the non-linear analysis of dendritic spines to study spine complexity along dendrites of cortical and subcortical neural systems, such as the basal ganglia, that sustain important motor learning processes. We show that, after learning, the spine organization has greater complexity, as indexed by the maximum Lyapunov exponent (LyE). The positive value of the exponent demonstrates that the system is chaotic, while recurrence plots show that the system is not simply composed by random noise, but displays quasi-periodic behavior. The increase in the maximum LyE and in the system entropy after learning was confirmed by the modification of the reconstructed trajectories in phase-space. Our results suggest that the remodeling of spines, as a result of a chaotic and non-random dynamical process along dendrites, may be a general feature associated with the structural plasticity underlying processes such as long-term memory maintenance. Furthermore, this work indicates that the non-linear method is a very useful tool to allow the detection of subtle stimulus-induced changes in dendritic spine dynamics, giving a key contribution to the study of the relationship between structure and function of spines.
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.bbr.2017.09.020|
|Codice identificativo ISI:||WOS:000413382100033|
|Codice identificativo Scopus:||2-s2.0-85029404929|
|Titolo:||Information content of dendritic spines after motor learning|
|Appare nelle tipologie:||1.1 Articolo in rivista|