Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

Abstract

Previous research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as ‘stiff’ dimensions, while it is insensitive to many others (‘sloppy’ dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring

“…Indeed, previous work using maximum entropy models fit to spiking data was similarly successful at identifying cellular-level stiff and sloppy dimensions (Panas et al, 2015;Ponce-Alvarez et al, 2020). These results are consistent with a previous experimental study that identified neurons of varying levels of correlation with the network activity (Okun et al, 2015;Ponce-Alvarez et al, 2020). In sum, these studies along with this work form a compelling argument to study mesoscale connectivity as well as network wide correlations by fitting models to spiking statistics.…”

“…For example, ionic conductances within individual neurons have consistently been found to vary greatly across neurons and between individuals despite regularity in spiking activity (Prinz et al, 2004;Schulz et al, 2006;Ransdell et al, 2013). At the neuronal circuit level, stability and state changes are mediated by a subset of neurons described by a small number of stiff parameter combinations while the parameters of the remainder of the neurons are sloppy (Panas et al, 2015;Ponce-Alvarez et al, 2020).…”

Neural circuit function redundancy in brain disorders

“…Accordingly, in experiments, these neurons showed twofold to threefold heterogeneity in cellular properties across animals, despite exhibiting consistent circuit function [ 6 ]. Similar redundancy phenomena have also been described in Hodgkin–Huxley models [ 7 ], mammalian pyramidal neuron models [ 8 ], tadpole neurons [ 9 ], rodent neuronal activity in vitro and in vivo [ 10 , 11 ] and human neuroimaging data [ 12 ]. Collectively, these studies, plus theoretical arguments [ 2 , 13 , 14 , 15 ], suggest that redundancy is a universal property of the nervous system.…”

Bayesian Mapping of the Striatal Microcircuit Reveals Robust Asymmetries in the Probabilities and Distances of Connections

“…From this it follows that any model of the striatum should sample its connection probabilities from these posteriors to understand the robustness of the results. It is now well established that parameters of neural models fall into two classes: those whose precise values are critical to the resulting predictions of a model, and those a model is not sensitive to (Panas et al, 2015;Ponce-Alvarez et al, 2020). And it is likely that striatal dynamics are indeed sensitive to variations in the probabilities and distances of connections (Humphries et al, 2010;Spreizer et al, 2017).…”