You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Coherence Potentials: Loss-Less, All-or-None Network Events in the Cortex}
Article:
Abstract:
Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential( LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks.
Introduction:
Since its introduction by Hebb[1], the transient formation of cell assemblies has been one of the most fundamental and provocative hypotheses to understand cortex function. The existence and identification of such assemblies, however, has been extremely difficult as it requires a criterion that separates the activities of neurons inside the cell assembly from those outside the assembly. Assuming that neurons within an assembly undergo similar changes in their activities, spectral coherence in the local field potential( LFP) reflecting temporal similarities irrespective of amplitudes[2] and synchronization in the spiking activity of individual neurons[3]–[6] have each been used independently to identify functionally associated neuronal groups at different cortical sites during discrete responses to stimuli( for review see[7]–[9]). Many studies focused particularly on similarities in the LFP, as temporal attributes of the LFP waveform have been shown to carry substantial information about a stimulus or behavior[10]–[12], often serving as better predictors of behaviors than spikes[13]–[15]. For instance, in the primate motor and premotor cortex transient phase-locking across cortical sites can be seen in the β- or γ- frequency band of the LFP during voluntary movements and behavioral task performances[16]–[19], which has been proposed to reflect neuronal interactions[20]. In the EEG, transient phase-locking correlates with “gestalt” perception clearly linking similarities in phase across cortical sites to higher cortical function[21]. Importantly, spontaneous or ongoing activity shares many similarities with stimulus-evoked activity[22]–[26], and indeed, transient phase-locking has been observed during ongoing cortical activity in awake monkeys[10],[23]. Because transient phase-locking has been found to include different cortical sites at different times, these dynamics have been widely appreciated in the literature under the concept of “metastability”[8],[27]–[34], emphasizing the idea that cortical dynamics might be sequentially ordered as strings of transient associations. If similarity is the defining property of associative activity, then the exact replication of activity at different sites might be the ultimate degree in similarity that can be utilized by the cortex. Such precise replication of activities requires the transient phase-locking over a wide range of frequencies in addition to maintaining the amplitude of the signal. Therefore, we analyzed the similarity of the complete LFP waveform across cortical sites during spontaneous cortical activity. Ongoing activity was recorded from chronically implanted arrays in the motor, premotor, and somatosensory regions of two monkeys sitting quietly in a recording chair as well as from organotypic cultures from rat cortex. We demonstrate a sigmoidal relationship between the amplitude of a deflection in the spontaneous LFP, reflecting the aggregate neuronal activity at a single cortical site and the occurrence of the identical LFP waveform and amplitude at other sites with millisecond delays. We show that this phenomenon of all or none, loss-less propagation of activity at high amplitudes arises by virtue of synaptic transmission. These results suggest a “tipping point” in the cortical network dynamics analogous to that found for the spread of ideas, innovation, and economic behavior in social networks, where a small increase in the number of participating agents can suddenly lead to widespread cascades of adoption.
Discussion:
The propagation of coherence potentials was saltatory in nature, rather than wave-like, frequently “jumping” across neighboring sites( Figure 8B) such that the fidelity of propagation was not substantially impacted by physical distance( Figure 8C). How might this phenomenon be reconciled with wave-like propagation ubiquitously reported in the cortex using techniques ranging from voltage sensitive dye imaging to LFPs and spikes recordings[10],[42],[43]? While this remains to be clearly understood, we provide a hypothetical framework that could reconcile these two findings. Coherence potentials arise at high thresholds of −4 SD in vivo and −9 SD in vitro, thereby representing only a very small fraction of nLFP activity in the cortex( <1%). This means that most of the activity one measures is sub-threshold in nature. Thus, in the absence of knowledge of a threshold dependent process of loss-less propagation, one would include both sub-threshold and supra-threshold activity, whereby alternative dynamics, such as wave-like propagation, might dominate. We therefore suggest that sub-threshold network activity may spread in a wave-like manner analogous to the spread of a sub-threshold depolarization in a neuron. We note that both waves and coherence potential propagation depend crucially on inhibition[41]. It will be interesting in the future to understand the mechanistic similarities and differences. The amplitude dependence of the transition to loss-less propagation indicates that coherence potentials are characterized by a certain level of synchronization in the local field[35]. Theory has suggested that synchrony might propagate differently from single spikes. For example, synfire chain models in feed-forward networks report that the size of the initial synchronized group determines propagation fate in a threshold dependent manner[44],[45]. In these basic models, however, there is convergence to a simple waveform as activity propagates, which is in contrast to coherence potentials, which can be highly diverse in their waveforms, and where the original temporal properties of the waveform or underlying synchronized group are preserved in the propagation to other sites. This interpretation equates the time course of a local nLFP with the synchronization of inputs arriving at a single layer of a synfire chain and where the coherence potential itself, which spatially extends over different sites, would reflect the various, spatially distinct layers of the chain. Alternatively, and in line with the general argument that only local connectivity between pyramidal neurons is high enough to sustain a synfire chain[44],[46], the nLFP time course could reflect the synchronization time course of a local synfire chain. In that case, coherence potentials would reflect phase-locking of distant synfire chains as suggested in studies on compositionality by simulating several chains that are weakly coupled through long-range connections[47]. Additionally, once recruited in this manner, sites may become locked into recurrent interactions that “attract” each site to a common pattern of activity for a short period of time. Such an interpretation is consistent with the long durations of the coherence potentials( several tens to hundreds of milliseconds) relative to the rapid time scales of propagation( generally a few milliseconds) and is supported by the high degree of reciprocal connectivity observed in cortical networks[48]–[51]. This interpretation has parallels to the principles underlying the construction of attractor neural networks or Hebbian cell assemblies[1],[52]. Evidence for recurrent interactions has also been described in the propagation of waves across cortical areas[53]. Such inter-areal phenomena also raise interesting questions about how propagation of coherence potentials across cortical regions may occur and how coherence potential originating at different cortical regions may interact. Finally, we note that in each of the datasets used in this study, nLFPs have been shown to organize as neuronal avalanches[35],[54]. Neuronal avalanche organization is a statistical property characterized by scale invariance in the temporal and spatial clustering of nLFPs suggesting that propagation could in theory span the entire cortex on a wide range of time scales[55]. Furthermore, partial reduction of fast GABAergic transmission abolishes the neuronal avalanche dynamics[35],[54] as well as the sigmoidal dependency of coherence potentials( cf., Figure 6C). It is thus of considerable importance to further understand the sigmoidal, non-linear transition to the coherence potential regime that reflects propagation without distortion and the general scale-invariant organization of nLFPs in the context of neuronal avalanches that may govern the organization of successive coherence potentials. The greater similarity of aggregate unit firing patterns at sites with similar LFP waveforms( Figure 9) suggests that coherence potential propagation reflects a preservation of the temporal characteristics of the underlying neuronal activity. Given that electrode noise and the spatial arrangement of the participating neurons relative to each electrode contribute to differences in waveforms observed at distinct sites[56], the precision with which coherence potentials propagate in the network might be even higher than we measured. The amplitude dependence and the initiation of high correlation periods on the rise phase only milliseconds before the peak( Figure S7) suggests that it is the synchronization of activity itself that acts as the trigger. Indeed, at the level of the individual neuron, increasing synchronization of input gives rise to a non-linear increase in precision and reliability of the input-output relationship by altering the threshold for spike formation[57]–[60]. Computational models also suggest a crucial role for fast inhibition in regulating the precision and reliability of output[61]–[63], consistent with the dependence of coherence potential propagation on GABAA dependent transmission. However, even with these findings at the single cell level, it remains to be understood how locally, synchronized activity can be directed to another site in an all-or-none manner. Unlike the stereotypical action potential, which can serve only as a binary code, the diversity of coherence potential waveforms represents a high dimensional degree of freedom, which could be used to encode information. In contrast, coherence potentials demonstrate the maintenance of spectrally complex waveforms and therefore indicate phase-locking or spectral coherence between sites across a broad range of component frequencies. While coherence potentials in our data showed no dominant frequency, this does not preclude them from exhibiting clear oscillatory components in the waveform under certain stimulus or behavioral conditions. Indeed in a small fraction of cases, we did find an oscillatory component( Figure S8). We propose that oscillatory behavior may represent a certain class of coherence potentials, analogous to a burst of action potentials, where the duration of waveform correlation may be extended across a greater number of positive–negative cycles. This proposition, of course, must be tested empirically but points to a new approach to LFP analysis, where, by not restricting analysis to a particular frequency band, it might be possible to make finer distinctions in the information contained in the complex structure of the LFP waveforms. A fundamental question that arises is how to reconcile the occurrence of similar associations both in stimulus deprived explant networks and the awake animal in a functional context. Here we suggest that just as the action potential arises by virtue of the intrinsic properties of the cell in a dish but takes on meaning in the intact organism by virtue of an input-output feedback mechanism, coherence potentials, which likely represent an aggregation of spiking activity, arise by virtue of intrinsic properties of the cortical network and take on meaning in a similar fashion. That temporal properties of the LFP waveform often serve as better predictors of behaviors than spikes[10],[12]–[15],[64]–[66] is consistent with such an explanation, suggesting that coherence potential propagation in vivo represents the transfer of meaningful information in the cortical network. Sequences of propagated coherence potentials occurred in rapid succession as a stream of dynamical associations( Figure 4) representing an identifiable switching of the cortical network from one dynamical state to another, which is a hallmark of “metastability”[8],[27]–[34]. However, the question remains how the cortex might make use of the specific phenomenon of coherence potentials, where many sites mirror one another with millisecond delays, reflecting transmission of information without any distortion. One possibility is that such propagation may serve as a form of working memory. However, to spark debate and discussion, we put forth a more radical hypothesis, drawing from the analogy to the spread of ideas, innovation, and economic behavior in social networks, which tend to display a threshold or “tipping point” such that a small increase in the number of participating agents can suddenly lead to widespread cascades of adoption[67],[68]. The initial adoption of the idea or behavior often arises among geographically proximal agents[69],[70] on networks with small-world principles[71] similar to those observed in cortical networks[72]–[75]. “Tipping points” are commonly modeled under conditions where individual agents make binary decisions( e. g., to buy or not to buy) by integrating the incoming information from their local neighborhood of friends[76], in close parallel to the “integrate and fire” property of neurons. We propose that perhaps information competes to pervade the cortex analogous to the way that ideas compete to pervade society, originating from various sensory areas in the way that ideas arise in society from specialized regions or areas focused on particular subjects or activities. Perhaps it is the case that when a sufficient number of sites across multiple cortical regions have adopted a particular module of cortical information, this now has the ability to impact the behavior of the organism. Such a hypothesis is not entirely unwarranted. For instance, coordinated cortical activity has been shown to be associated with motor behavior[10],[17], the spread of voltage-sensitive dye signals from sensory to motor areas of cortex and their amplitudes have been found to be correlated with animal whisking behavior[77], and the diversity found in average LFP waveforms across many electrodes have been shown to encode behaviors[78]. We note that the diversity of coherence potential waveforms in vivo was much greater than in vitro indicating a richer associative environment and we suggest that a comprehensive characterization of coherence potential waveforms and their spatial spread in relation to behavior is likely to be highly instructive with respect to a broad range of cognitive functions.
Perception and behavior are thought to arise from transient associations among sub-groups of nerve cells in the brain . However , identifying which of the many active neurons are associated at any given time and how poses a challenge . Here we show that when the composite activity of a local group of cortical neurons , measured as a complex waveform in the extracellular field , exceeds a threshold , its activity pattern extending up to hundreds of milliseconds occurs without distortion at other cortical sites via fast synaptic transmission . We call these all-or-none propagated patterns “coherence potentials” , in analogy to action potentials at the single cell level . In contrast to action potentials , which are stereotypical and thus capable only of binary coding , coherence potentials are diverse and complex waveforms that can serve as a high-dimensional parameter for encoding information . The non-linear relationship between local activity and its extent of replicated spread suggests a “tipping point” that bears analogy to the propagation of innovations and economic behavior in social networks , which can spread rapidly once they have garnered a local critical mass .