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Spontaneous activity in cortical networks in vitro. A : raster plot for 60 channels in a 1 day in vitro (DIV) 34 network for 200 s ( left ) and zoom-in on 30 s ( right ; gray box in the left plot ). Epochs of synchronous, network-wide bursting and inactivity are clearly distinguishable. Network burst length and interval varied considerably. B : interspike interval probability (p) distribution during 1 h recording for 4 channels marked green in A . The distribution with a peak ϳ 0.01 s reflects intervals within bursts; the peak ϳ 5 s, intervals between bursts. Insets : number of spikes/burst histograms.
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Variable responses of neuronal networks to repeated sensory or electrical stimuli reflect the interaction of the stimulus' response with ongoing activity in the brain and its modulation by adaptive mechanisms such as cognitive context, network state or cellular excitability and synaptic transmission capability. Here, we focus on reliability, length...
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Context 1
... STIMULATION OF nervous tissue is used increasingly in the treatment of central nervous system disorders, e.g., by deep brain stimulation, in neuroprosthetic devices aiding sensory per- ception, as well as in examining the biophysiological properties of single cells and the function of neuronal networks. Whereas the reproducible responses of directly stimulated individual neurons consist of precisely timed single action potentials (APs) or trains of APs under specific conditions (Bryant and Segundo 1976; Mainen and Sejnowski 1995; but see Gal et al. 2010), stimulation in recurrent networks elicits multiphasic responses. These typically consist of: 1 ) a fast excitatory component of precise and reliable firing by antidromic or monosynaptic activation of local neurons with delays between 2 and 20 ms, 2 ) a transition phase with low activity thought to be mediated by inhibitory neurons, and 3 ) a delayed excitatory component driven by recurrent polysynaptic activation (Butovas and Schwarz 2003; Eccles et al. 1974; Fanselow and Nicolelis 1999; Rowland and Jaeger 2008; Wagenaar et al. 2004). More physiological sensory responses induced by, e.g., foot tapping, whisker deflection, or air puffs unfold comparable dynamics in various brain regions of awake and anesthetized animals (Cody et al. 1981; Fanselow and Nicolelis 1999; Rowland and Jaeger 2005). Across stimulation trials, however, the variability of timing and duration of the response, as well as the number and distribution of neurons involved, is typically high (Azouz and Gray 1999; Jones et al. 2007). On short time scales, temporal nonstationarities by modulation of activity and excitability tend to prevail and modify response amplitude, latency, and spatial spread (Kisley and Gerstein 1999; Petersen et al. 2003). In addition, the activity state of the neocortex at stimulus onset may dominate trial-by-trial variability (Arieli et al. 1996; Hasenstaub et al. 2007). Although these influences are known in a general sense, they have not been assessed quantitatively, and the prediction of stimulation outcomes for individual stimuli given during autonomous network dynamics is unreliable. Predictable stimulus-response relations, however, become increasingly im- portant to control adequate functionality of neurotechnological devices, which seems incompatible with trial-by-trial variability. To identify the general rules of the interaction between ongoing activity and stimulus-response relations, independent of a specific tissue architecture, function, or sensory stimulus, we analyzed spontaneous and evoked activity dynamics in generic neuronal networks in vitro. These networks exhibit spontaneous spiking with typical patterns and long-term modulation (Wagenaar et al. 2006) that defines the network state with which different stimuli may interact. Here, we focus on reliability, length, delays, and variability of evoked responses with respect to their spatial distribution, interaction with spontaneous activity in the networks, and the contribution of GABAergic inhibition. We asked which interactions arise between weak, local electrical pulses and network state and how they influence evoked responses. Specifically, we developed quantitative models that show how the state of the network at the moment of stimulation determines response length and delay. Closed-loop stimulation relative to ongoing activity significantly reduced trial-by-trial variability and enabled us to examine systematically the influence of network inhibition and short-term plasticity on stimulus-response relations. Spontaneous network activity. When cultured on MEAs, neuronal networks’ intrinsic spontaneous activity can be monitored noninvasively over long periods of time. During regular baseline activity, neuronal multiunit activity recorded by MEA electrodes consisted of bursts of variable lengths and intervals, forming a pattern of recurrent activity that follows a stereotypical developmental timeline. In our recordings, the ISI distribution was mul- timodal for intervals Ͻ 0.1 s within bursts and Ͼ 1 s up to tens of seconds between bursts (Fig. 1 B ). Bursting activity was synchronized across almost all active sites (Fig. 1 A ). Network burst durations ranged between tens of milliseconds and several seconds. Network burst intervals were approximately lognormal distributed (Fig. 2 A ). This spike activity can be recorded continu- ously for periods up to several days under stationary conditions due to the extremely stable recording configuration. Burst duration and subsequent IBIs reflect an interaction of synaptic depression and recovery mechanisms, as well as differential adaptation of excitatory and inhibitory networks (Eytan et al. 2003; Toib et al. 1998). Since these processes modulating spontaneous activity may likewise determine the stability of stimulated responses, we quantified the relation between the intervals between network bursts and their length. We first asked if the preceding interval was predictive for network burst length or if, in turn, network burst length was predictive for the subsequent interval. We calculated the sequence of CC(m) between network burst intervals and lengths. The index variable m indicates whether the correlation was calculated for preceding ( m Ͻ 0) or following interval ( m Ն 0). Similarly, the sequence of autocorrelations [AC(m)] identified temporal correlations between a selected feature of network bursts n and n ϩ m . The CC between network burst length and interval was highest for the preceding interval [ CC ( Ϫ 1) ϭ 0.24], it decreased for the following, and approached zero correlation for the one-after-next interval [ CC (0) ϭ 0.13, CC (1) ϭ 0.01; Fig. 2 B ]. The preceding interval thus has higher predictive power for the duration of a network burst than this has for the duration of the following interval. When the intervals were shuffled randomly, the sequence of CC decays to zero and is basically a flat line [CC( Ϫ 2) ϭ Ϫ 0.0014, CC( Ϫ 1) ϭ 0.0062, CC(0) ϭ Ϫ 0.0035, CC(1) ϭ 0.0053]. In contrast, in AC analyses, the length of network bursts was not correlated with the length of any of the preceding or following network bursts (Fig. 2 C ), indicating that there is no serial correlation in the time series of network burst length. These findings suggest that bursts terminate, not after a uniform duration but at some reproducible threshold that may be defined by inhibitory activity and/or some limitation of synaptic resources. Network recovery would thus always start at approximately the same level, comparable with mechanisms described by Tabak et al. (2001). A coherent, systematic pattern across networks thus emerges. In the following, we quantitatively examined the interaction between spontaneous bursting and stimulus-evoked responses and ask whether sim- ilar principles between preceding inactivity and response properties apply. State-dependent network responses. Stimulation at one site with the same pulse every 10 or 20 s evoked spike activity at many other sites throughout the network. To avoid interaction between stimuli, we chose the stimulation intervals such that spontaneous activity resumed to a prestimulus, asynchronous re- gime between stimuli; shorter intervals attenuate or suppress spontaneous activity (Eytan et al. 2003). Responses at individual sites consisted of an early ( Յ 25 ms poststimulus) and late ( Ͼ 50 ms poststimulus) component or a late component only. Exact transition times between the two response components varied across different sites and networks. A striking difference between these components was the very precise and reliable firing within a narrow window for early responses and in contrast, seemingly irreproducible firing without any apparent pattern during late responses (Fig. 3 A ). Recording sites with early and late responses were preferentially located close to the stimulation site, whereas exclusively late components were found at more distant sites (Fig. 3 C ). This supports local initiation of early spikes and polysynaptic transfer of activity into distant parts of the network (Butovas and Schwarz 2003). Early responses are likely a direct and fast activation of neurons close to the stimulation site or bypassing axons (Jimbo et al. 2000; Marom and Shahaf 2002). These responses could originate from nonsynaptic antidromic excitation and/or early postsynaptic spikes with a very low jitter, Ͻ 2 ms (Bonifazi et al. 2005; Wagenaar et al. 2004). Late responses occurred after a transition period with low firing rates and resembled globally synchronized activity during spontaneous network bursts. The late component is sensitive to glutamate receptor blockers, supporting a polysynaptic origin (Jimbo et al. 2000). It is this component that reflects how a local stimulus will eventually invade the network, and that thus should have predictable dynamics (Kermany et al. 2010). Understanding what influences the properties of the polysynaptic response is therefore crucial for a defined interaction with neuronal networks. In the following, we analyze reliability, ...
Context 2
... STIMULATION OF nervous tissue is used increasingly in the treatment of central nervous system disorders, e.g., by deep brain stimulation, in neuroprosthetic devices aiding sensory per- ception, as well as in examining the biophysiological properties of single cells and the function of neuronal networks. Whereas the reproducible responses of directly stimulated individual neurons consist of precisely timed single action potentials (APs) or trains of APs under specific conditions (Bryant and Segundo 1976; Mainen and Sejnowski 1995; but see Gal et al. 2010), stimulation in recurrent networks elicits multiphasic responses. These typically consist of: 1 ) a fast excitatory component of precise and reliable firing by antidromic or monosynaptic activation of local neurons with delays between 2 and 20 ms, 2 ) a transition phase with low activity thought to be mediated by inhibitory neurons, and 3 ) a delayed excitatory component driven by recurrent polysynaptic activation (Butovas and Schwarz 2003; Eccles et al. 1974; Fanselow and Nicolelis 1999; Rowland and Jaeger 2008; Wagenaar et al. 2004). More physiological sensory responses induced by, e.g., foot tapping, whisker deflection, or air puffs unfold comparable dynamics in various brain regions of awake and anesthetized animals (Cody et al. 1981; Fanselow and Nicolelis 1999; Rowland and Jaeger 2005). Across stimulation trials, however, the variability of timing and duration of the response, as well as the number and distribution of neurons involved, is typically high (Azouz and Gray 1999; Jones et al. 2007). On short time scales, temporal nonstationarities by modulation of activity and excitability tend to prevail and modify response amplitude, latency, and spatial spread (Kisley and Gerstein 1999; Petersen et al. 2003). In addition, the activity state of the neocortex at stimulus onset may dominate trial-by-trial variability (Arieli et al. 1996; Hasenstaub et al. 2007). Although these influences are known in a general sense, they have not been assessed quantitatively, and the prediction of stimulation outcomes for individual stimuli given during autonomous network dynamics is unreliable. Predictable stimulus-response relations, however, become increasingly im- portant to control adequate functionality of neurotechnological devices, which seems incompatible with trial-by-trial variability. To identify the general rules of the interaction between ongoing activity and stimulus-response relations, independent of a specific tissue architecture, function, or sensory stimulus, we analyzed spontaneous and evoked activity dynamics in generic neuronal networks in vitro. These networks exhibit spontaneous spiking with typical patterns and long-term modulation (Wagenaar et al. 2006) that defines the network state with which different stimuli may interact. Here, we focus on reliability, length, delays, and variability of evoked responses with respect to their spatial distribution, interaction with spontaneous activity in the networks, and the contribution of GABAergic inhibition. We asked which interactions arise between weak, local electrical pulses and network state and how they influence evoked responses. Specifically, we developed quantitative models that show how the state of the network at the moment of stimulation determines response length and delay. Closed-loop stimulation relative to ongoing activity significantly reduced trial-by-trial variability and enabled us to examine systematically the influence of network inhibition and short-term plasticity on stimulus-response relations. Spontaneous network activity. When cultured on MEAs, neuronal networks’ intrinsic spontaneous activity can be monitored noninvasively over long periods of time. During regular baseline activity, neuronal multiunit activity recorded by MEA electrodes consisted of bursts of variable lengths and intervals, forming a pattern of recurrent activity that follows a stereotypical developmental timeline. In our recordings, the ISI distribution was mul- timodal for intervals Ͻ 0.1 s within bursts and Ͼ 1 s up to tens of seconds between bursts (Fig. 1 B ). Bursting activity was synchronized across almost all active sites (Fig. 1 A ). Network burst durations ranged between tens of milliseconds and several seconds. Network burst intervals were approximately lognormal distributed (Fig. 2 A ). This spike activity can be recorded continu- ously for periods up to several days under stationary conditions due to the extremely stable recording configuration. Burst duration and subsequent IBIs reflect an interaction of synaptic depression and recovery mechanisms, as well as differential adaptation of excitatory and inhibitory networks (Eytan et al. 2003; Toib et al. 1998). Since these processes modulating spontaneous activity may likewise determine the stability of stimulated responses, we quantified the relation between the intervals between network bursts and their length. We first asked if the preceding interval was predictive for network burst length or if, in turn, network burst length was predictive for the subsequent interval. We calculated the sequence of CC(m) between network burst intervals and lengths. The index variable m indicates whether the correlation was calculated for preceding ( m Ͻ 0) or following interval ( m Ն 0). Similarly, the sequence of autocorrelations [AC(m)] identified temporal correlations between a selected feature of network bursts n and n ϩ m . The CC between network burst length and interval was highest for the preceding interval [ CC ( Ϫ 1) ϭ 0.24], it decreased for the following, and approached zero correlation for the one-after-next interval [ CC (0) ϭ 0.13, CC (1) ϭ 0.01; Fig. 2 B ]. The preceding interval thus has higher predictive power for the duration of a network burst than this has for the duration of the following interval. When the intervals were shuffled randomly, the sequence of CC decays to zero and is basically a flat line [CC( Ϫ 2) ϭ Ϫ 0.0014, CC( Ϫ 1) ϭ 0.0062, CC(0) ϭ Ϫ 0.0035, CC(1) ϭ 0.0053]. In contrast, in AC analyses, the length of network bursts was not correlated with the length of any of the preceding or following network bursts (Fig. 2 C ), indicating that there is no serial correlation in the time series of network burst length. These findings suggest that bursts terminate, not after a uniform duration but at some reproducible threshold that may be defined by inhibitory activity and/or some limitation of synaptic resources. Network recovery would thus always start at approximately the same level, comparable with mechanisms described by Tabak et al. (2001). A coherent, systematic pattern across networks thus emerges. In the following, we quantitatively examined the interaction between spontaneous bursting and stimulus-evoked responses and ask whether sim- ilar principles between preceding inactivity and response properties apply. State-dependent network responses. Stimulation at one site with the same pulse every 10 or 20 s evoked spike activity at many other sites throughout the network. To avoid interaction between stimuli, we chose the stimulation intervals such that spontaneous activity resumed to a prestimulus, asynchronous re- gime between stimuli; shorter intervals attenuate or suppress spontaneous activity (Eytan et al. 2003). Responses at individual sites consisted of an early ( Յ 25 ms poststimulus) and late ( Ͼ 50 ms poststimulus) component or a late component only. Exact transition times between the two response components varied across different sites and networks. A striking difference between these components was the very precise and reliable firing within a narrow window for early responses and in contrast, seemingly irreproducible firing without any apparent pattern during late responses (Fig. 3 A ). Recording sites with early and late responses were preferentially located close to the stimulation site, whereas exclusively late components were found at more distant sites (Fig. 3 C ). This supports local initiation of early spikes and polysynaptic transfer of activity into distant parts of the network (Butovas and Schwarz 2003). Early responses are likely a direct and fast activation of neurons close to the stimulation site or bypassing axons (Jimbo et al. 2000; Marom and Shahaf 2002). These responses could originate from nonsynaptic antidromic excitation and/or early postsynaptic spikes with a very low jitter, Ͻ 2 ms (Bonifazi et al. 2005; Wagenaar et al. 2004). Late responses occurred after a transition period with low firing rates and resembled globally synchronized activity during spontaneous network bursts. The late component is sensitive to glutamate receptor blockers, supporting a polysynaptic origin (Jimbo et al. 2000). It is this component that reflects how a local stimulus will eventually invade the network, and that thus should have predictable dynamics (Kermany et al. 2010). Understanding what influences the properties of the polysynaptic response is therefore crucial for a defined interaction with neuronal networks. In the following, we analyze reliability, length, delays, and variability of evoked responses with respect to their spatial ...
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Objectives:
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Met...
Citations
... Spontaneous network activity underpins the development of functional networks in early stages (Teppola et al., 2019). A hallmark of this activity is the recurrent occurrence of intense, time-constrained network bursts (NBs) that rapidly propagate throughout the entire dissociated culture in vitro (Okujeni et al., 2017;Weihberger et (which was not certified by peer review) is the author/funder. All rights reserved. ...
Excitatory/inhibitory (E/I) balance is thought to play a key role in cortical activity development. However, the modeling of cortical networks with different E/I ratios is not feasible in vivo . To address this point, we modeled an in vitro cortical network deployed of the inhibitory neurons normally migrating from the ventral telencephalon. Moreover, we implemented striatal cultures and co-cultures with mixed proportions of cortical and striatal neurons. The resulting cultures contained various proportions of inhibitory Parvalbumin (PV) ⁺ neurons, ranging from 7% to 73%. Interestingly, these pure and mixed cortical/striatal cultures exhibited four distinct patterns of spontaneous activity and functional connectivity. Our findings highlighted a critical role for the inhibitory component in developing correlated network activity. Unexpectedly, cortical networks with 7% of PV ⁺ neurons were not able to generate appreciable network burst activity due to the development of a strong network inhibition, despite their lowest E/I ratio. Our observations support the notion that an optimal ratio of PV ⁺ neurons during cortical development is essential for the establishment of local inhibitory networks capable of generating and spreading correlated activity.
Highlights
In vitro neurogenesis models the development of mouse cortical network devoid of inhibitory neurons
Cortical network with low inhibitory neuron ratio develops poor synchronized network activity
GABA inhibition unmask intrinsic network ability to generate highly synchronized activity
Network response to single node stimulus depends on optimal inhibitory neuron ratio
A proper excitatory/inhibitory ratio is necessary for the development of network burst activity
Graphical Abstract
... These stimulus responses are often interpreted as short-term memory of a stimulus, which has been suggested to depend mainly on sustained spiking in recurrent networks, creating attractor states [65]. Stimulus responses depend on the state of the network at the time of stimulation [66,67], and thus, changing network states might affect prediction. However, the ISI distribution was chosen such that the network was able to respond to (almost) all stimuli. ...
Objective. Memory has been extensively studied at the behavioural as well as the cellular level. Spike timing dependent plasticity is widely considered essential for long-term memory and is associated with activation of N-methyl-D-aspartate (NMDA) receptors. This suggests that NMDA receptor activation plays a crucial role in enabling long-term memory. However, experimental evidence remains sparse, probably due to the complex combination of cellular and functional readouts required. Approach. Recent work showed that in-vitro cortical networks memorize and predict inputs. The initial dependency of prediction on short-term memory decreased during the formation of long-term memory traces. Here, we stimulated networks of dissociated cortical neurons that were grown on multi electrode arrays to investigate memory and prediction under control conditions, or under NMDA block. Main results. The NMDA antagonist 2-amino-5-phosphonovaleric acid (APV) at the used concentration impeded long-term memory trace formation, but did not significantly reduce network excitability. In APV-treated cultures short-term memory of stimuli persisted and they were still able to predict. In contrast to control cultures, prediction remained fully dependent on short-term memory. Significance. This confirms that NMDA receptor activation is essential for the formation of long-term memory traces and supports the notion that, as control cultures learn to memorize the stimulus, long-term memory starts to contribute to their predictive capability.
... We wondered if such variability would similarly be 283 observed if we attempted to increase activity. GABAR blockade in cortical cultures has been 284 shown to increase certain features of spiking acutely(Turrigiano et al., 1998a; Corner et al., 285 2002;Weihberger et al., 2013). In order to assess the variability of this response and to 286 timing of such a recovery, we disinhibited cortical cultures and monitored various 287 ...
Homeostatic plasticity represents a set of mechanisms thought to stabilize some function of neural activity. Here, we identified the specific features of cellular or network activity that were maintained after the perturbation of GABAergic blockade in two different systems: mouse cortical neuronal cultures where GABA is inhibitory and motoneurons in the isolated embryonic chick spinal cord where GABA is excitatory (males and females combined in both systems). We conducted a comprehensive analysis of various spiking activity characteristics following GABAergic blockade. We observed significant variability in many features after blocking GABA A receptors (e.g., burst frequency, burst duration, overall spike frequency in culture). These results are consistent with the idea that neuronal networks achieve activity goals using different strategies (degeneracy). On the other hand, some features were consistently altered after receptor blockade in the spinal cord preparation (e.g., overall spike frequency). Regardless, these features did not express strong homeostatic recoveries when tracking individual preparations over time. One feature showed a consistent change and homeostatic recovery following GABA A receptor block. We found that spike rate within a burst (SRWB) increased after receptor block in both the spinal cord preparation and cortical cultures and then returned to baseline within hours. These changes in SRWB occurred at both single cell and population levels. Our findings indicate that the network prioritizes the burst spike rate, which appears to be a variable under tight homeostatic regulation. The result is consistent with the idea that networks can maintain an appropriate behavioral response in the face of challenges.
... The bursts of elevated population activity, correlated in space and time, are supposed to be tied to neuronal avalanches and synchronized oscillation 88 . The hallmarks of neuronal spontaneous activity are intermittent SBs, separated by periods of silent activity, and the intervals between SBs are distributed approximately lognormally 89 . To assess changes in network bursts, the following parameters were analyzed: SB duration, interSB interval and SB percentage 80 . ...
A bidirectional in vitro brain-computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.
... Since neuronal responsiveness to external inputs depends on the state of the network [53], we applied a mutual information and cross-correlation analysis to the evoked activity to quantify the sensitivity to external inputs. NSCs are incorporated into the network to make connections with neighboring neurons [54], and they respond to a broader range of inputs [55]. ...
Objective. Neural stem cells (NSCs) are continuously produced throughout life in the hippocampus, which is a vital structure for learning and memory. NSCs in the brain incorporate into the functional hippocampal circuits and contribute to processing information. However, little is known about the mechanisms of NSCs’ activity in a pre-existing neuronal network. Here, we investigate the role of NSCs in the neuronal activity of a pre-existing hippocampal in vitro network grown on microelectrode arrays. Approach. We assessed the change in internal dynamics of the network by additional NSCs based on spontaneous activity. We also evaluated the networks’ ability to discriminate between different input patterns by measuring evoked activity in response to external inputs. Main results. Analysis of spontaneous activity revealed that additional NSCs prolonged network bursts with longer intervals, generated a lower number of initiating patterns, and decreased synchronization among neurons. Moreover, the network with NSCs showed higher synchronicity in close connections among neurons responding to external inputs and a larger difference in spike counts and cross-correlations during evoked response between two different inputs. Taken together, our results suggested that NSCs alter the internal dynamics of the pre-existing hippocampal network and produce more specific responses to external inputs, thus enhancing the ability of the network to differentiate two different inputs. Significance. We demonstrated that NSCs improve the ability to distinguish external inputs by modulating the internal dynamics of a pre-existing network in a hippocampal culture. Our results provide novel insights into the relationship between NSCs and learning and memory.
... Furthermore, highfrequency spiking increases the metabolic demand on the neurons. Work on in vitro networks has shown that the delay between signals can play an important role in maximizing the amplitude and duration of network events (Weihberger, Okujeni, Mikkonen, & Egert, 2013), suggesting that not only the high-frequency components of irregular stimuli may play a role but also the low-frequency gaps that occur in between. More generally, this provides further support for a functional interpretation of bursting; for example, chattering neurons, which emit high-frequency bursts of spikes, play an important role in the reliable transmission of signals via unreliable synapses (Wang, 1999). ...
Behavior is controlled by complex neural networks in which neurons process thousands of inputs. However, even short spike trains evoked in a single cortical neuron were demonstrated to be sufficient to influence behavior in vivo. Specifically, irregular sequences of interspike intervals (ISIs) had a more reliable influence on behavior despite their resemblance to stochastic activity. Similarly, irregular tactile stimulation led to higher rates of behavioral responses.
In this study, we identify the mechanisms enabling this sensitivity to stimulus irregularity (SSI) on the neuronal and network levels using simulated spiking neural networks. Matching in vivo experiments, we find that irregular stimulation elicits more detectable network events (bursts) than regular stimulation. Dissecting the stimuli, we identify short ISIs—occurring more frequently in irregular stimulations—as the main drivers of SSI rather than complex irregularity per se. In addition, we find that short-term plasticity modulates SSI.
We subsequently eliminate the different mechanisms in turn to assess their role in generating SSI. Removing inhibitory interneurons, we find that SSI is retained, suggesting that SSI is not dependent on inhibition. Removing recurrency, we find that SSI is retained due to the ability of individual neurons to integrate activity over short timescales (“cell memory”). Removing single-neuron dynamics, we find that SSI is retained based on the short-term retention of activity within the recurrent network structure (“network memory”). Finally, using a further simplified probabilistic model, we find that local network structure is not required for SSI.
Hence, SSI is identified as a general property that we hypothesize to be ubiquitous in neural networks with different structures and biophysical properties. Irregular sequences contain shorter ISIs, which are the main drivers underlying SSI. The experimentally observed SSI should thus generalize to other systems, suggesting a functional role for irregular activity in cortex.
... To the best of our knowledge, this study is the first to unravel in detail how the INTRODUCTION Spontaneous network activity plays a fundamental role in the formation of functional networks during early development of the central nervous system (Feller, 1999;O'Donovan, 1999;Ben-Ari, 2001;Blankenship and Feller, 2010;Egorov and Draguhn, 2013;Luhmann et al., 2016). Recurrent network bursts (NBs) are observed in cerebral cortex in vivo (Chiu and Weliky, 2001;Crochet et al., 2005;Minlebaev et al., 2007;Yang et al., 2009;Wang and Arnsten, 2015), in cortical brain slice preparations in vitro (Yuste and Katz, 1991;Garaschuk et al., 2000;Harsch and Robinson, 2000;Sanchez-Vives and McCormick, 2000;Corner et al., 2002;Corlew et al., 2004;Sun and Luhmann, 2007;Allene et al., 2008), as well as in dissociated in vitro cortical cell cultures (Dichter, 1978;Murphy et al., 1992;Muramoto et al., 1993;Maeda et al., 1995;Marom and Shahaf, 2002;Opitz et al., 2002;van Pelt et al., 2004;Chiappalone et al., 2006;Eytan and Marom, 2006;Wagenaar et al., 2006;Tetzlaff et al., 2010;Teppola et al., 2011;Weihberger et al., 2013;Okujeni et al., 2017). Considering that the bursting dynamics are an essential feature of the activity both in vivo and in vitro and that the complex underlying mechanisms that shape this activity are not well understood, their better characterization is highly important. ...
... Previous research has shown that less synchronized burst activity correlates with the gradual maturation of GABA A receptor signaling, which depends on the presence of large GABAergic neurons with widespread connections in cultured cortical networks (Baltz et al., 2010). Additionally, it has been demonstrated that the late phase substantially increases after the blockade of GABA A Rs with their antagonists (10µM bicuculline (BIC), 5µM picrotoxin (PTX) or 20µM gabazine) which indicates that the intensity and duration of the late phase are controlled by inhibitory synapses among cortical neurons in vitro (Jimbo et al., 2000;Weihberger et al., 2013;Baltz and Voigt, 2015). Furthermore, GABAergic interneurons are shown to control the dynamic spatio-temporal pattern formation in neuronal networks by organizing spatially and temporally the network activity rather than only reducing firing probability (Whittington and Traub, 2003;Mann and Paulsen, 2007;Klausberger and Somogyi, 2008). ...
... The MEA technique is a widely used, reliable and feasible recording technique for high-throughput screening of network dynamics, particularly when multiple cell cultures and experimental protocols are considered similarly to this study. The network activity has been studied in cell cultures obtained from the neocortex of P0 (Shahaf and Marom, 2001;Eytan and Marom, 2006;Teppola et al., 2011;Weihberger et al., 2013;Reinartz et al., 2014;Haroush and Marom, 2015;Okujeni et al., 2017) and E17-18 (Robinson et al., 1993;Maeda et al., 1995;Kamioka et al., 1996;Jimbo et al., 2000;Opitz et al., 2002;van Pelt et al., 2004;Chiappalone et al., 2006;Wagenaar et al., 2006;Baltz et al., 2010;Fong et al., 2015) rats. In addition, network activity has been studied in cultures prepared from other areas of the rodent central nervous system, including hippocampus (Arnold et al., 2005;Mazzoni et al., 2007;Chen and Dzakpasu, 2010;Niedringhaus et al., 2013;Eisenman et al., 2015;Slomowitz et al., 2015;Suresh et al., 2016;Lonardoni et al., 2017) and spinal cord (Keefer et al., 2001;Gramowski et al., 2004;Legrand et al., 2004;Ham et al., 2008). ...
Spontaneous network activity plays a fundamental role in the formation of functional networks during early development. The landmark of this activity is the recurrent emergence of intensive time-limited network bursts (NBs) rapidly spreading across the entire dissociated culture in vitro. The main excitatory mediators of NBs are glutamatergic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) and N-Methyl-D-aspartic-acid receptors (NMDARs) that express fast and slow ion channel kinetics, respectively. The fast inhibition of the activity is mediated through gamma-aminobutyric acid type A receptors (GABAARs). Although the AMPAR, NMDAR and GABAAR kinetics have been biophysically characterized in detail at the monosynaptic level in a variety of brain areas, the unique features of NBs emerging from the kinetics and the complex interplay of these receptors are not well understood. The goal of this study is to analyze the contribution of fast GABAARs on AMPAR- and NMDAR- mediated spontaneous NB activity in dissociated neonatal rat cortical cultures at 3 weeks in vitro. The networks were probed by both acute and gradual application of each excitatory receptor antagonist and combinations of acute excitatory and inhibitory receptor antagonists. At the same time, the extracellular network-wide activity was recorded with microelectrode arrays (MEAs). We analyzed the characteristic NB measures extracted from NB rate profiles and the distributions of interspike intervals, interburst intervals, and electrode recruitment time as well as the similarity of spatio-temporal patterns of network activity under different receptor antagonists. We show that NBs were rapidly initiated and recruited as well as diversely propagated by AMPARs and temporally and spatially maintained by NMDARs. GABAARs reduced the spiking frequency in AMPAR-mediated networks and dampened the termination of NBs in NMDAR-mediated networks as well as slowed down the recruitment of activity in all networks. Finally, we show characteristic super bursts composed of slow NBs with highly repetitive spatio-temporal patterns in gradually AMPAR blocked networks. To the best of our knowledge, this study is the first to unravel in detail how the three main mediators of synaptic transmission uniquely shape the NB characteristics, such as the initiation, maintenance, recruitment and termination of NBs in cortical cell cultures in vitro.
... Yet, this would be sufficient to initiate SBEs only if the output of this local network is well connected to recruit large parts of the network. Conversely, recurrent input from highly excitable regions to the BIZ must not be too strong to avoid lasting depression of excitability in the BIZ by SBEs (Weihberger et al., 2013;Kumar et al., 2016). A moderately connected position with locally recurrent connectivity would fulfill these prerequisites. ...
The mesoscale architecture of neuronal networks strongly influences the initiation of spontaneous activity and its pathways of propagation. Spontaneous activity has been studied extensively in networks of cultured cortical neurons that generate complex yet reproducible patterns of synchronous bursting events that resemble the activity dynamics in developing neuronal networks in vivo. Synchronous bursts are mostly thought to be triggered at burst initiation sites due to build-up of noise or by highly active neurons, or to reflect reverberating activity that circulates within larger networks, although neither of these has been observed directly. Inferring such collective dynamics in neuronal populations from electrophysiological recordings crucially depends on the spatial resolution and sampling ratio relative to the size of the networks assessed. Using large-scale microelectrode arrays with 1024 electrodes at 0.3 mm pitch that covered the full extent of in vitro networks on about 1 cm², we investigated where bursts of spontaneous activity arise and how their propagation patterns relate to the regions of origin, the network’s structure, and to the overall distribution of activity. A set of alternating burst initiation zones (BIZ) dominated the initiation of distinct bursting events and triggered specific propagation patterns. Moreover, BIZs were typically located in areas with moderate activity levels, i.e., at transitions between hot and cold spots. The activity-dependent alternation between these zones suggests that the local networks forming the dominating BIZ enter a transient depressed state after several cycles (similar to Eytan et al., 2003), allowing other BIZs to take over temporarily. We propose that inhomogeneities in the network structure define such BIZs and that the depletion of local synaptic resources limit repetitive burst initiation.
... During the onset of the NB, there was an exponential increase of the neuronal firing rate, as a dynamical reflection of the considerable structural recurrent excitation, which effectively represents a positive feedback loop in the system. The impact of the recruitment of inhibitory neurons became apparent at a later stage, together with the activation of several intrinsic and synaptic adaptation mechanisms, as the NB profile decayed and the network became ultimately silent, by an effective negative feedback loop in the system (Weihberger et al., 2013). The main features of these NBs, such as their duration and occurrence frequency, are therefore directly related to the mutual interaction and balance between excitation and inhibition in the network. ...
Neurons are embedded in an extracellular matrix (ECM), which functions both as a scaffold and as a regulator of neuronal function. The ECM is in turn dynamically altered through the action of serine proteases, which break down its constituents. This pathway has been implicated in the regulation of synaptic plasticity and of neuronal intrinsic excitability. In this study, we determined the short-term effects of interfering with proteolytic processes in the ECM, with a newly developed serine protease inhibitor. We monitored the spontaneous electrophysiological activity of in vitro primary rat cortical cultures, using microelectrode arrays. While pharmacological inhibition at a low dosage had no significant effect, at elevated concentrations it altered significantly network synchronization and functional connectivity but left unaltered single-cell electrical properties. These results suggest that serine protease inhibition affects synaptic properties, likely through its actions on the ECM.
... These can occur when stimulated at electrodes that are poorly embedded in the network or due to the network being in a refractory period after an SB event. Ongoing SB activity is known to influence the network's interaction with external stimuli [14] . Response strength (RS) -the count of spikes detected in a fixed post-stimulus interval -depends on the The inter-stimulus-interval was set to 7 s in this example. ...
... Black lines indicate model residuals. stimulus latency relative to the previous SB, and can be described by a saturating exponential model [8,14] ( Fig. 3 (B)). However, we found that this dependency was non-stationary when observed over long time scales. ...
... We adopted the following procedure to choose a set of stimulation electrodes (SEs) and a recording electrode (RE) to provide feedback in the closed-loop paradigm. Sites that were likely to participate early in SBs -the so-called "major burst leaders" -were marked as candidates [14,22,23] . Periodic stimuli were delivered at these sites cyclically with an inter stimulus interval of 7 − 10 s, and the responses elicited were analyzed. ...
