Electrophysiological monitoring of inhibition in mammalian species, from rodents to humans

Peyrache, Adrien; Destexhe, Alain

doi:10.1016/j.nbd.2019.104500

Cited by 17 publications

(13 citation statements)

References 135 publications

(259 reference statements)

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“…Secondly, the separation between putative excitatory and inhibitory neurons based on the widths of their spike waveforms is also known to have several limitations ( Bartho et al, 2004 ; Kaufman et al, 2010, 2013 ; Peyrache et al, 2012 ; Dehghani et al, 2016 ; Peyrache and Destexhe, 2019) . Some pyramidal neurons, in particular when recorded close to the axon, exhibit narrow waveforms.…”

Section: Discussionmentioning

confidence: 99%

“…Still, it was shown that over 10% of M1 interneurons have intermediate or broad waveforms ( Kaufman et al, 2010 ; Vigneswaran et al, 2011 ; Kaufman et al, 2013) . When discussing the differences between the two populations, it should be kept in mind that not all narrow-spiking units are inhibitory and only a part (majority) of broad-spiking SUs are excitatory ( Peyrache and Destexhe, 2019) . Nevertheless, our separation yields higher average firing rates for putative inhibitory neurons which agrees well with what is known from the literature ( Peyrache et al, 2012 ; Dehghani et al, 2016 ; Kaufman et al, 2010) .…”

Section: Discussionmentioning

confidence: 99%

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On the complexity of resting state spiking activity in monkey motor cortex

Dąbrowska

Voges

Papen

et al. 2020

Preprint

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Resting state has been established as a classical paradigm of brain activity studies, 11 mostly based on large scale measurements such as fMRI or M/EEG. This term typically refers to a 12 behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal 13 activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks 14 claim to mimic such idle states, but compare their results to task-or stimulus-driven experiments, 15 which might lead to misleading conclusions. To provide a proper basis for comparing physiological 16 and simulated network dynamics, we characterize simultaneously recorded single neurons' spiking 17 activity in monkey motor cortex and show the differences from spontaneous and task-induced 18 movement conditions. The resting state shows a higher dimensionality, reduced firing rates and 19 less balance between population level excitation and inhibition than behavior-related states. 20 Additionally, our results stress the importance of distinguishing between rest with eyes open and 21 closed. 22 23 2007; Fox and Raichle, 2007; van den Heuvel and Hulshoff Pol, 2010)Defined on the whole-brain 32 level, they are shaped by anatomical connectivity and derived from functional connectivity (Honey 33 et al., 2009; Bastos and Schoffelen, 2016). 34 While the exact link between the fMRI signal and neuronal activity is a matter of ongoing research 35 (Logothetis and Wandell, 2004; Ekstrom, 2010), resting state studies have also been carried out on 36 the level of single brain areas. Here, the spontaneous activity is often referred to as ongoing, intrinsic, 37 baseline, or resting state activity, and can be studied by means of, for example, optical imaging 38 combined with single electrode recordings (Arieli et al., 1996; Tsodyks et al., 1999; Kenet et al., 39 2003). Such data, collected under anesthesia, were used to investigate the variability in evoked 40 1 of 34 Manuscript submitted to eLife cortical responses (Arieli et al., 1996), the switching of cortical states (Tsodyks et al., 1999), and the 41 link of these cortical states to the underlying functional architecture (Kenet et al., 2003). In our 42 study, we aim to characterize the resting state on yet another spatio-temporal scale, namely on the 43 scale of simultaneous single unit (SU) spiking activity recorded in macaque monkey (pre-)motor cortex. 44 Spiking activity in monkey motor cortex has been studied extensively during arm movements, 45 which gives rise to an increased average neuronal firing compared to wait (Nawrot et al., 2008; 46 Rickert et al., 2009; Riehle et al., 2018). On a single unit level, direction-specific neuronal sub-47 populations encode the movement direction by firing rate modulations (Georgopoulos et al., 1986; 48 Rickert et al., 2009). These and other studies also investigated the spike time irregularity and the 49 spike count variability in monkey motor cortex during various behavioral epochs: Movements have 50 been related ...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

On the complexity of resting state spiking activity in monkey motor cortex

Dąbrowska

Voges

Papen

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Offline spike-sorted single units (SUs) are separated into putative excitatory (broad-spiking) and putative inhibitory (narrow-spiking) based on their spike waveform width (50,51,52,53,54). The width is defined as the time (num-ber of data samples) between the trough and peak of the waveform.…”

Section: Separation Of Putative Excitatory and Inhibitory Neuronsmentioning

confidence: 99%

Global organization of neuronal activity only requires unstructured local connectivity

Dahmen

Layer

Deutz

et al. 2022

eLife

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Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet, this parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

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“…NS and BS waveforms have been directly linked to these two classes in some instances using intra-or juxtacellular monitoring in rodents and monkeys (Gonzá lez-Burgos et al, 2019;Joshi and Hawken, 2006;Krimer et al, 2005;McCormick et al, 1985), cross-correlation of spike trains to reveal monosynaptic excitatory and inhibitory connections (Barthó et al, 2004;Mendoza et al, 2016;Peyrache et al, 2012;Tamura et al, 2004), and antidromic electrical stimulation to identify excitatory projection neurons (Johnston et al, 2009). Importantly, in vivo recordings throughout the brain reveal that groups of neurons with NS or BS EAPs play functionally distinct roles in behavior (Anastassiou et al, 2015;Ison et al, 2011;Mitchell et al, 2007;Oemisch et al, 2015;Peyrache et al, 2012;Peyrache and Destexhe, 2019;Rutishauser et al, 2015;Takahashi et al, 2015;Trainito et al, 2019;Viskontas et al, 2007). Nevertheless, the assumption that all NS cells are inhibitory and all BS cells are excitatory is not always true: some excitatory cells elicit narrow spikes and some inhibitory cells elicit wider spikes (Freund and Buzsá ki, 1996;Gray and Mc-Cormick, 1996;Onorato et al, 2020;Vigneswaran et al, 2011Gouwens et al, 2019.…”

Section: Introductionmentioning

confidence: 99%