Portraits of communication in neuronal networks.

The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.

Differential Coding Strategies in Glutamatergic and GABAergic Neurons in the Medial Cerebellar Nucleus.

The cerebellum drives motor coordination and sequencing of actions at the millisecond timescale through adaptive control of cerebellar nuclear output. Cerebellar nuclei integrate high-frequency information from both the cerebellar cortex and the two main excitatory inputs of the cerebellum: the mossy fibers and the climbing fiber collaterals. However, how nuclear cells process rate and timing of inputs carried by these inputs is still debated. Here, we investigate the influence of the cerebellar cortical output, the Purkinje cells, on identified cerebellar nuclei neurons in vivo in male mice. Using transgenic mice expressing Channelrhodopsin2 specifically in Purkinje cells and tetrode recordings in the medial nucleus, we identified two main groups of neurons based on the waveform of their action potentials. These two groups of neurons coincide with glutamatergic and GABAergic neurons identified by optotagging after Chrimson expression in VGLUT2-cre and GAD-cre mice, respectively. The glutamatergic-like neurons fire at high rate and respond to both rate and timing of Purkinje cell population inputs, whereas GABAergic-like neurons only respond to the mean population firing rate of Purkinje cells at high frequencies. Moreover, synchronous activation of Purkinje cells can entrain the glutamatergic-like, but not the GABAergic-like, cells over a wide range of frequencies. Our results suggest that the downstream effect of synchronous and rhythmic Purkinje cell discharges depends on the type of cerebellar nuclei neurons targeted.SIGNIFICANCE STATEMENT Motor coordination and skilled movements are driven by the permanent discharge of neurons from the cerebellar nuclei that communicate cerebellar computation to other brain areas. Here, we set out to study how specific subtypes of cerebellar nuclear neurons of the medial nucleus are controlled by Purkinje cells, the sole output of the cerebellar cortex. We could isolate different subtypes of nuclear cell that differentially encode Purkinje cell inhibition. Purkinje cell stimulation entrains glutamatergic projection cells at their firing frequency, whereas GABAergic neurons are only inhibited. These differential coding strategies may favor temporal precision of cerebellar excitatory outputs associated with specific features of movement control while setting the global level of cerebellar activity through inhibition via rate coding mechanisms.

Facilitating the propagation of spiking activity in feedforward networks by including feedback.

Transient oscillations in network activity upon sensory stimulation have been reported in different sensory areas of the brain. These evoked oscillations are the generic response of networks of excitatory and inhibitory neurons (EI-networks) to a transient external input. Recently, it has been shown that this resonance property of EI-networks can be exploited for communication in modular neuronal networks by enabling the transmission of sequences of synchronous spike volleys ('pulse packets'), despite the sparse and weak connectivity between the modules. The condition for successful transmission is that the pulse packet (PP) intervals match the period of the modules' resonance frequency. Hence, the mechanism was termed communication through resonance (CTR). This mechanism has three severe constraints, though. First, it needs periodic trains of PPs, whereas single PPs fail to propagate. Second, the inter-PP interval needs to match the network resonance. Third, transmission is very slow, because in each module, the network resonance needs to build up over multiple oscillation cycles. Here, we show that, by adding appropriate feedback connections to the network, the CTR mechanism can be improved and the aforementioned constraints relaxed. Specifically, we show that adding feedback connections between two upstream modules, called the resonance pair, in an otherwise feedforward modular network can support successful propagation of a single PP throughout the entire network. The key condition for successful transmission is that the sum of the forward and backward delays in the resonance pair matches the resonance frequency of the network modules. The transmission is much faster, by more than a factor of two, than in the original CTR mechanism. Moreover, it distinctly lowers the threshold for successful communication by synchronous spiking in modular networks of weakly coupled networks. Thus, our results suggest a new functional role of bidirectional connectivity for the communication in cortical area networks.

Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code.

In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired.

From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks.

Spatio-temporal sequences of neuronal activity are observed in many brain regions in a variety of tasks and are thought to form the basis of meaningful behavior. However, mechanisms by which a neuronal network can generate spatio-temporal activity sequences have remained obscure. Existing models are biologically untenable because they either require manual embedding of a feedforward network within a random network or supervised learning to train the connectivity of a network to generate sequences. Here, we propose a biologically plausible, generative rule to create spatio-temporal activity sequences in a network of spiking neurons with distance-dependent connectivity. We show that the emergence of spatio-temporal activity sequences requires: (1) individual neurons preferentially project a small fraction of their axons in a specific direction, and (2) the preferential projection direction of neighboring neurons is similar. Thus, an anisotropic but correlated connectivity of neuron groups suffices to generate spatio-temporal activity sequences in an otherwise random neuronal network model.

Hyperactivity of Anterior Cingulate Cortex Areas 24a/24b Drives Chronic Pain-Induced Anxiodepressive-like Consequences.

Pain associates both sensory and emotional aversive components, and often leads to anxiety and depression when it becomes chronic. Here, we characterized, in a mouse model, the long-term development of these sensory and aversive components as well as anxiodepressive-like consequences of neuropathic pain and determined their electrophysiological impact on the anterior cingulate cortex (ACC, cortical areas 24a/24b). We show that these symptoms of neuropathic pain evolve and recover in different time courses following nerve injury in male mice. In vivo electrophysiological recordings evidence an increased firing rate and bursting activity within the ACC when anxiodepressive-like consequences developed, and this hyperactivity persists beyond the period of mechanical hypersensitivity. Whole-cell patch-clamp recordings also support ACC hyperactivity, as shown by increased excitatory postsynaptic transmission and contribution of NMDA receptors. Optogenetic inhibition of the ACC hyperactivity was sufficient to alleviate the aversive and anxiodepressive-like consequences of neuropathic pain, indicating that these consequences are underpinned by ACC hyperactivity.SIGNIFICANCE STATEMENT Chronic pain is frequently comorbid with mood disorders, such as anxiety and depression. It has been shown that it is possible to model this comorbidity in animal models by taking into consideration the time factor. In this study, we aimed at determining the dynamic of different components and consequences of chronic pain, and correlated them with electrophysiological alterations. By combining electrophysiological, optogenetic, and behavioral analyses in a mouse model of neuropathic pain, we show that the mechanical hypersensitivity, ongoing pain, anxiodepressive consequences, and their recoveries do not necessarily exhibit temporal synchrony during chronic pain processing, and that the hyperactivity of the anterior cingulate cortex is essential for driving the emotional impact of neuropathic pain.

Direct pathway neurons in mouse dorsolateral striatum in vivo receive stronger synaptic input than indirect pathway neurons.

Striatal projection neurons, the medium spiny neurons (MSNs), play a crucial role in various motor and cognitive functions. MSNs express either D1- or D2-type dopamine receptors and initiate the direct-pathway (dMSNs) or indirect pathways (iMSNs) of the basal ganglia, respectively. dMSNs have been shown to receive more inhibition than iMSNs from intrastriatal sources. Based on these findings, computational modeling of the striatal network has predicted that under healthy conditions dMSNs should receive more total input than iMSNs. To test this prediction, we analyzed in vivo whole cell recordings from dMSNs and iMSNs in healthy and dopamine-depleted (6OHDA) anaesthetized mice. By comparing their membrane potential fluctuations, we found that dMSNs exhibited considerably larger membrane potential fluctuations over a wide frequency range. Furthermore, by comparing the spike-triggered average membrane potentials, we found that dMSNs depolarized toward the spike threshold significantly faster than iMSNs did. Together, these findings (in particular the STA analysis) corroborate the theoretical prediction that direct-pathway MSNs receive stronger total input than indirect-pathway neurons. Finally, we found that dopamine-depleted mice exhibited no difference between the membrane potential fluctuations of dMSNs and iMSNs. These data provide new insights into the question of how the lack of dopamine may lead to behavioral deficits associated with Parkinson's disease.NEW & NOTEWORTHY The direct and indirect pathways of the basal ganglia originate from the D1- and D2-type dopamine receptor expressing medium spiny neurons (dMSNs and iMSNs). Theoretical results have predicted that dMSNs should receive stronger synaptic input than iMSNs. Using in vivo intracellular membrane potential data, we provide evidence that dMSNs indeed receive stronger input than iMSNs, as has been predicted by the computational model.

Sensorimotor Processing in the Basal Ganglia Leads to Transient Beta Oscillations during Behavior.

Brief epochs of beta oscillations have been implicated in sensorimotor control in the basal ganglia of task-performing healthy animals. However, which neural processes underlie their generation and how they are affected by sensorimotor processing remains unclear. To determine the mechanisms underlying transient beta oscillations in the LFP, we combined computational modeling of the subthalamo-pallidal network for the generation of beta oscillations with realistic stimulation patterns derived from single-unit data recorded from different basal ganglia subregions in rats performing a cued choice task. In the recordings, we found distinct firing patterns in the striatum, globus pallidus, and subthalamic nucleus related to sensory and motor events during the behavioral task. Using these firing patterns to generate realistic inputs to our network model led to transient beta oscillations with the same time course as the rat LFP data. In addition, our model can account for further nonintuitive aspects of beta modulation, including beta phase resets after sensory cues and correlations with reaction time. Overall, our model can explain how the combination of temporally regulated sensory responses of the subthalamic nucleus, ramping activity of the subthalamic nucleus, and movement-related activity of the globus pallidus leads to transient beta oscillations during behavior.SIGNIFICANCE STATEMENT Transient beta oscillations emerge in the normal functioning cortico-basal ganglia loop during behavior. Here, we used a unique approach connecting a computational model closely with experimental data. In this way, we achieved a simulation environment for our model that mimics natural input patterns in awake, behaving animals. We demonstrate that a computational model for beta oscillations in Parkinson's disease (PD) can also account for complex patterns of transient beta oscillations in healthy animals. Therefore, we propose that transient beta oscillations in healthy animals share the same mechanism with pathological beta oscillations in PD. This important result connects functional and pathological roles of beta oscillations in the basal ganglia.

Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control.

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.

Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model.

How neuronal activity of motor cortex is related to movement is a central topic in motor neuroscience. Motor-cortical single neurons are more closely related to hand movement velocity than speed, that is, the magnitude of the (directional) velocity vector. Recently, there is also increasing interest in the representation of movement parameters in neuronal population activity, such as reflected in the intracranial EEG (iEEG). We show that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity. The predominant speed representation was present in nearly all iEEG signal features, up to the 600-1000 Hz range. Using a model of motor-cortical signals arising from neuronal populations with realistic single neuron tuning properties, we show how this reversal can be understood as a consequence of increasing population size. Our findings demonstrate that the information profile in large population signals may systematically differ from the single neuron level, a principle that may be helpful in the interpretation of neuronal population signals in general, including, for example, EEG and functional magnetic resonance imaging. Taking advantage of the robust speed population signal may help in developing brain-machine interfaces exploiting population signals.

Role of input correlations in shaping the variability and noise correlations of evoked activity in the neocortex.

Recent analysis of evoked activity recorded across different brain regions and tasks revealed a marked decrease in noise correlations and trial-by-trial variability. Given the importance of correlations and variability for information processing within the rate coding paradigm, several mechanisms have been proposed to explain the reduction in these quantities despite an increase in firing rates. These models suggest that anatomical clusters and/or tightly balanced excitation-inhibition can generate intrinsic network dynamics that may exhibit a reduction in noise correlations and trial-by-trial variability when perturbed by an external input. Such mechanisms based on the recurrent feedback crucially ignore the contribution of feedforward input to the statistics of the evoked activity. Therefore, we investigated how statistical properties of the feedforward input shape the statistics of the evoked activity. Specifically, we focused on the effect of input correlation structure on the noise correlations and trial-by-trial variability. We show that the ability of neurons to transfer the input firing rate, correlation, and variability to the output depends on the correlations within the presynaptic pool of a neuron, and that an input with even weak within-correlations can be sufficient to reduce noise correlations and trial-by-trial variability, without requiring any specific recurrent connectivity structure. In general, depending on the ongoing activity state, feedforward input could either increase or decrease noise correlation and trial-by-trial variability. Thus, we propose that evoked activity statistics are jointly determined by the feedforward and feedback inputs.

Existence and control of Go/No-Go decision transition threshold in the striatum.

A typical Go/No-Go decision is suggested to be implemented in the brain via the activation of the direct or indirect pathway in the basal ganglia. Medium spiny neurons (MSNs) in the striatum, receiving input from cortex and projecting to the direct and indirect pathways express D1 and D2 type dopamine receptors, respectively. Recently, it has become clear that the two types of MSNs markedly differ in their mutual and recurrent connectivities as well as feedforward inhibition from FSIs. Therefore, to understand striatal function in action selection, it is of key importance to identify the role of the distinct connectivities within and between the two types of MSNs on the balance of their activity. Here, we used both a reduced firing rate model and numerical simulations of a spiking network model of the striatum to analyze the dynamic balance of spiking activities in D1 and D2 MSNs. We show that the asymmetric connectivity of the two types of MSNs renders the striatum into a threshold device, indicating the state of cortical input rates and correlations by the relative activity rates of D1 and D2 MSNs. Next, we describe how this striatal threshold can be effectively modulated by the activity of fast spiking interneurons, by the dopamine level, and by the activity of the GPe via pallidostriatal backprojections. We show that multiple mechanisms exist in the basal ganglia for biasing striatal output in favour of either the `Go' or the `No-Go' pathway. This new understanding of striatal network dynamics provides novel insights into the putative role of the striatum in various behavioral deficits in patients with Parkinson's disease, including increased reaction times, L-Dopa-induced dyskinesia, and deep brain stimulation-induced impulsivity.

Physiology and Impact of Horizontal Connections in Rat Neocortex.

Cortical information processing at the cellular level has predominantly been studied in local networks, which are dominated by strong vertical connectivity between layers. However, recent studies suggest that the bulk of axons targeting pyramidal neurons most likely originate from outside this local range, emphasizing the importance of horizontal connections. We mapped a subset of these connections to L5B pyramidal neurons in rat somatosensory cortex with photostimulation, identifying intact projections up to a lateral distance of 2 mm. Our estimates of the spatial distribution of cells presynaptic to L5B pyramids support the idea that the majority is located outside the local volume. The synaptic physiology of horizontal connections does not differ markedly from that of local connections, whereas the layer and cell-type-dependent pattern of innervation does. Apart from L2/3, L6A provides a strong source of horizontal connections. Implementing our data into a spiking neuronal network model shows that more horizontal connections promote robust asynchronous ongoing activity states and reduce noise correlations in stimulus-induced activity.

Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding.

The brain is a highly modular structure. To exploit modularity, it is necessary that spiking activity can propagate from one module to another while preserving the information it carries. Therefore, reliable propagation is one of the key properties of a candidate neural code. Surprisingly, the conditions under which spiking activity can be propagated have received comparatively little attention in the experimental literature. By contrast, several computational studies in the last decade have addressed this issue. Using feedforward networks (FFNs) as a generic network model, they have identified two dynamical activity modes that support the propagation of either asynchronous (rate code) or synchronous (temporal code) spiking. Here, we review the dichotomy of asynchronous and synchronous propagation in FFNs, propose their integration into a single extended conceptual framework and suggest experimental strategies to test our hypothesis.

Communication through resonance in spiking neuronal networks.

The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance"). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks.

Visualization of the amygdalo-hippocampal border and its structural variability by 7T and 3T magnetic resonance imaging.

The amygdala and the hippocampus are two adjacent structures in the medial temporal lobe that have been broadly investigated in functional and structural neuroimaging due to their central importance in sensory perception, emotion, and memory. Exact demarcation of the amygdalo-hippocampal border (AHB) is, however, difficult in conventional structural imaging. Recent evidence suggests that, due to this difficulty, functional activation sites with high probability of being located in the hippocampus may erroneously be assigned to the amygdala, and vice versa. In the present study, we investigated the potential of ultra-high-field magnetic resonance imaging (MRI) in single sessions for detecting the AHB in humans. We show for the first time the detailed structure of the AHB as it can be visualized in T1-weighted 7T in vivo images at 0.5-mm(3) isotropic resolution. Compared to data acquired at 3T, 7T images revealed considerably more structural detail in the AHB region. Thus, we observed a striking inter-hemispheric and interindividual variability of the exact anatomical configuration of the AHB that points to the necessity of individual imaging of the AHB as a prerequisite for accurate anatomical assignment in this region. The findings of the present study demonstrate the usefulness of ultra-high-field structural MRI to resolve anatomical ambiguities of the human AHB. Highly accurate morphometric and functional investigations in this region at 7T may allow addressing such hitherto unexplored issues as whether the structural configuration of the AHB is related to functional differences in amygdalo-hippocampal interaction.

Impact of correlated inputs to neurons: modeling observations from in vivo intracellular recordings.

In vivo recordings in rat somatosensory cortex suggest that excitatory and inhibitory inputs are often correlated during spontaneous and sensory-evoked activity. Using a computational approach, we study how the interplay of input correlations and timing observed in experiments controls the spiking probability of single neurons. Several correlation-based mechanisms are identified, which can effectively switch a neuron on and off. In addition, we investigate the transfer of input correlation to output correlation in pairs of neurons, at the spike train and the membrane potential levels, by considering spike-driving and non-spike-driving inputs separately. In particular, we propose a plausible explanation for the in vivo finding that membrane potentials in neighboring neurons are correlated, but the spike-triggered averages of membrane potentials preceding a spike are not: Neighboring neurons possibly receive an ongoing bombardment of correlated subthreshold background inputs, and occasionally uncorrelated spike-driving inputs.

Heart cycle-related effects on event-related potentials, spectral power changes, and connectivity patterns in the human ECoG.

The perception of one's own heartbeat is a fundamental interoceptive process that involves cortical and subcortical structures. Yet, the precise spatiotemporal neuronal activity patterns underlying the cortical information processing have remained largely elusive. Although the high temporal and spatial resolution of electrocorticographic (ECoG) recordings is increasingly being exploited in functional neuroimaging, it has not been used to study heart cycle-related effects. Here, we addressed the capacity of ECoG to characterize neuronal signals within the cardiac cycle, as well as to disentangle them from heart cycle-related artifacts. Based on topographical distribution and latency, we identified a biphasic potential within the primary somatosensory cortex, which likely constitutes a heartbeat-evoked potential (HEP) of neuronal origin. We also found two different types of artifacts: i) oscillatory potential changes with a frequency identical to the heart pulse rate, which probably represent pulsatility artifacts and ii) sharp potentials synchronized to the R-peak, corresponding to the onset of ventricular contraction and the cardiac field artifact (CFA) in EEG. Finally, we show that heart cycle-related effects induce pronounced phase-synchrony patterns in the ECoG and that this kind of correlation patterns, which may confound ECoG connectivity studies, can be reduced by a suitable correction algorithm. The present study is, to our knowledge, the first one to show a focally localized cortical HEP that could be clearly and consistently observed over subjects, suggesting a basic role of primary sensory cortex in processing of heart-related sensory inputs. We also conclude that taking into account and reducing heart cycle-related effects may be advantageous for many ECoG studies, and are of crucial importance, particularly for ECoG-based connectivity studies. Thus, in summary, although ECoG poses new challenges, it opens up new possibilities for the investigation of heartbeat-related viscerosensory processing in the human brain.

Somatotopic mapping of natural upper- and lower-extremity movements and speech production with high gamma electrocorticography.

Precise delineation of pathological and eloquent cortices is essential in pre-neurosurgical diagnostics of epilepsy. A limitation of existing experimental procedures, however, is that they critically require active cooperation of the patient, which is not always achievable, particularly in infants and in patients with insufficient cognitive abilities. In the present study, we evaluated the potential of electrocorticographic recordings of high gamma activity during natural, non-experimental behavior of epilepsy patients to localize upper- and lower-extremity motor and language functions, and compared the results with those obtained using electrocortical stimulation. The observed effects were highly significant and functionally specific, and agreed well with the somatotopic organization of the motor cortex, both on the lateral convexity and in the supplementary motor area. Our approach showed a similar specificity and sensitivity for extremity movements as previously obtained from experimental data. We were able to quantify, for the first time, sensitivity and specificity of high gamma underlying non-experimental lower-extremity movements in four patients, and observed values in the same range as for upper extremities (analyzed in six patients). Speech-related responses in the three investigated patients, however, exhibited only a very low sensitivity. The present findings indicate that localization of not only upper- but also lower-extremity movements congruent with electrocortical stimulation mapping is possible based on event-related high gamma responses that can be observed during natural behavior. Thus, non-experimental mapping may be usefully applied as adjunct to established clinical procedures for identification of both upper- and lower-extremity motor functions.

Beyond statistical significance: implications of network structure on neuronal activity.

It is a common and good practice in experimental sciences to assess the statistical significance of measured outcomes. For this, the probability of obtaining the actual results is estimated under the assumption of an appropriately chosen null-hypothesis. If this probability is smaller than some threshold, the results are deemed statistically significant and the researchers are content in having revealed, within their own experimental domain, a "surprising" anomaly, possibly indicative of a hitherto hidden fragment of the underlying "ground-truth". What is often neglected, though, is the actual importance of these experimental outcomes for understanding the system under investigation. We illustrate this point by giving practical and intuitive examples from the field of systems neuroscience. Specifically, we use the notion of embeddedness to quantify the impact of a neuron's activity on its downstream neurons in the network. We show that the network response strongly depends on the embeddedness of stimulated neurons and that embeddedness is a key determinant of the importance of neuronal activity on local and downstream processing. We extrapolate these results to other fields in which networks are used as a theoretical framework.