Developmental increase of inhibition drives decorrelation of neural activity

Abstract

Throughout development, the brain transits from early highly synchronous activity patterns to a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying this process are unknown. The developmental transition has important functional consequences, as the latter state allows for more efficient storage, retrieval and processing of information. Here, we show that, in the mouse medial prefrontal cortex (mPFC), neural activity during the first two postnatal weeks decorrelates following specific spatial patterns. This process is accompanied by a concomitant tilting of excitation/inhibition (E-I) ratio towards inhibition. Using optogenetic manipulations and neural network modeling, we show that the two phenomena are mechanistically linked, and that a relative increase of inhibition drives the decorrelation of neural activity. Accordingly, in two mouse models of neurodevelopmental disorders, subtle alterations in E-I ratio are associated with specific impairments in the correlational structure of spike trains. Finally, capitalizing on EEG data from newborn babies, we show that an analogous developmental transition takes place also in the human brain. Thus, changes in E-I ratio control the (de)correlation of neural activity and, by these means, its developmental imbalance might contribute to the pathogenesis of neurodevelopmental disorders.

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks

“…43,44 Recent experimental results have suggested that inhibitory neurons drive decorrelation of neural activities. 45,46 In line with these results, in our model, interconnected inhibitory neurons repel each other and their tuning curves decorrelate (Fig. 2D).…”

Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

“…Importantly, however, propofol is known to commonly result in a net increase of inhibition ( Concas et al, 1991 ; Franks, 2008 ) whereas ketamine results in a relative increase of excitation ( Deane et al, 2020 ; Miller et al, 2016 ). In accordance with invasive work and single cell modelling ( Chini et al, 2021 ; Gao et al, 2017 ), propofol anaesthesia should thus lead to an increase in the spectral exponent (steepening of the spectrum) and ketamine anaesthesia to a decrease (flattening). Based on previous results, the effect of anaesthesia on EEG spectral exponents is expected to be highly consistent and display little topographical variation ( Lendner et al, 2020 ).…”

“…Intra-individual variations in EEG spectral exponents have been reported as a function of overall arousal level and activation ( Colombo et al, 2019 ; Lendner et al, 2020 ; Podvalny et al, 2015 ). Based on computational models and invasive recordings of neural activity, it has been demonstrated that electrophysiological spectral exponents capture the balance of excitatory and inhibitory neural activity (E:I; Gao et al, 2017 ), with recent causal, optogenetic work showing that lower exponents are mechanistically linked to increased E:I balance ( Chini et al, 2021 ). Although unknown at present, it is plausible that differences in non-invasive electrophysiological spectral exponents might also reflect variations in E:I balance.…”

Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

“…To this end, we parameterized the power spectra of consecutive, overlapping data bins of 2 s length of the source-projected MEG data in a frequency range from 3 to 40 Hz, separately for all source locations ( Donoghue et al, 2020 ). Simulations of a biologically plausible neural network ( Trakoshis et al, 2020 ) as well as empirical insights from optogenetic stimulations in neonatal mice ( Chini et al, 2021 ) show that spectral slopes extracted from the chosen frequency range are in fact sensitive to changes in the underlying ratio between excitation and inhibition. The resulting parameter estimates described the periodic and the aperiodic components per epoch (data segmentation as in the previous section; see Materials and methods).…”