Inhibitory control of frontal metastability sets the temporal signature of cognition

Abstract

Cortical neural dynamics organizes over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate that a temporal signature (autocorrelogram time constant and latency) of neural activity enlightens this organization. In monkey frontal areas, recorded during flexible cognitive decisions, temporal signatures display highly specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.

A Diversity of Intrinsic Timescales Underlie Neural Computations

“…Single-neuron autocorrelograms also significantly vary in their offset, and the importance of this parameter has yet to be explored. One recent study also identified important heterogeneity in the initial time-lag before autocorrelation begins to decay as a function of time (Fontanier et al, 2020 ), a feature which was particularly prominent in cingulate cortex (Murray et al, 2014 ; Cavanagh et al, 2016 , 2018 ; Fontanier et al, 2020 ). Related to the time-lag of autocorrelograms, other studies in rodents have demonstrated diversity in the time-lag of stimulus representations (Harvey et al, 2012 ; Morcos and Harvey, 2016 ; Scott et al, 2017 ).…”

“…Interestingly, the cortical layer may also determine the degree of local connectivity, with neurons in layer III of prefrontal cortex thought to have particularly strong recurrent connections (Goldman-Rakic, 1995;Kritzer and Goldman-Rakic, 1995) reflected by an increase in spine density in prefrontal and parietal cortices relative to early sensory areas (Elston, 2003;Elston et al, 2011;Gilman et al, 2017). Recent studies have leveraged new technologies to demonstrate that task-related working memory activity mainly resides in supragranular layers (Markowitz et al, 2015;Bastos et al, 2018;Finn et al, 2019), providing experimental evidence that recurrent circuitry may be important for generating persistent activity. In future studies, laminar electrode probes may also provide insight into the relationship between neural timescales and cortical layer.…”

“…Importantly, this analysis highlighted a striking degree of within-region variability ( Figure 2 )—even within the anterior cingulate cortex (ACC), which sat at the apex of the hierarchy identified in Murray et al ( 2014 ); there was a spectrum of timescales including many neurons with short timescales. Further studies then applied the same single-neuron analysis to many different brain regions throughout the cortical hierarchy ( Figure 3 ), including posterior parietal cortex (Wasmuht et al, 2018 ), lateral prefrontal cortex (lPFC; Cavanagh et al, 2016 , 2018 ; Wasmuht et al, 2018 ; Fascianelli et al, 2019 ; Fontanier et al, 2020 ; Kim and Sejnowski, 2020 ), orbitofrontal cortex (OFC; Cavanagh et al, 2016 , 2018 ; Fascianelli et al, 2019 ), cingulate cortex (Cavanagh et al, 2016 , 2018 ; Fontanier et al, 2020 ), premotor cortex (Cirillo et al, 2018 ), and frontopolar cortex (Fascianelli et al, 2019 ). All of these regions contained single neurons with a diversity of timescales, suggesting that brain regions possessing a heterogonous distribution of timescales is a generalized feature of cortical organization.…”

“…Whereas decision-making requires the gradual integration of evidence across time (Gold and Shadlen, 2007 ), working memory involves the maintenance of task-relevant information in the absence of direct sensory input (Goldman-Rakic, 1995 ). A number of studies demonstrated that neuronal timescales predicted the strength of mnemonic encoding on a variety of different working-memory paradigms, with longer timescale neurons again playing a greater role in the maintenance of mnemonic information (Nishida et al, 2014 ; Cavanagh et al, 2018 ; Cirillo et al, 2018 ; Wasmuht et al, 2018 ; Fascianelli et al, 2019 ; Fontanier et al, 2020 ; Kim and Sejnowski, 2020 ; Figure 4B ). This effect was present in multiple brain regions [lPFC, cingulate cortex, frontal eye field (FEF), premotor cortex], and for multiple different modalities of mnemonic information (spatial location/response direction, expected reward size, stimulus color).…”

“…However, there has been less evidence presented regarding the possible roles of short timescale neurons. This has been addressed by a recent study which demonstrated that during an inter-trial period neurons with short timescales encoded momentary feedback information more strongly (Fontanier et al, 2020 ). This contrasted with long timescale neurons, which at this point of the task preferentially encoded information which was relevant for future decisions which would occur in subsequent trials.…”