Analysis of spontaneous bursting activity in random neural networks

Propagation of temporal and rate signals in cultured multilayer networks

Analyses of idealized feedforward networks suggest that several conditions have to be satisfied in order for activity to propagate faithfully across layers. Verifying these concepts experimentally has been difficult owing to the vast number of variables that must be controlled. Here, we cultured cortical neurons in a chamber with sequentially connected compartments, optogenetically stimulated individual neurons in the first layer with high spatiotemporal resolution, and then monitored the subthreshold and suprathreshold potentials in subsequent layers. Brief stimuli delivered to the first layer evoked a short-latency transient response followed by sustained activity. Rate signals, carried by the sustained component, propagated reliably through 4 layers, unlike idealized feedforward networks, which tended strongly towards synchrony. Moreover, temporal jitter in the stimulus was transformed into a rate code and transmitted to the last layer. This novel mode of propagation occurred in the balanced excitatory-inhibitory regime and is mediated by NMDA-mediated receptors and recurrent activity.

Temporal correlations in neuronal avalanche occurrence

Ongoing cortical activity consists of sequences of synchronized bursts, named neuronal avalanches, whose size and duration are power law distributed. These features have been observed in a variety of systems and conditions, at all spatial scales, supporting scale invariance, universality and therefore criticality. However, the mechanisms leading to burst triggering, as well as the relationship between bursts and quiescence, are still unclear. The analysis of temporal correlations constitutes a major step towards a deeper understanding of burst dynamics. Here, we investigate the relation between avalanche sizes and quiet times, as well as between sizes of consecutive avalanches recorded in cortex slice cultures. We show that quiet times depend on the size of preceding avalanches and, at the same time, influence the size of the following one. Moreover we evidence that sizes of consecutive avalanches are correlated. In particular, we show that an avalanche tends to be larger or smaller than the following one for short or long time separation, respectively. Our analysis represents the first attempt to provide a quantitative estimate of correlations between activity and quiescence in the framework of neuronal avalanches and will help to enlighten the mechanisms underlying spontaneous activity.

Critical role for inhibitory neurons in modulation of synaptic signaling in ex vivo neuronal networks

A number of laboratories have modeled aspects of synaptic plasticity using neuronal networks established on micro-electrode arrays. Such studies demonstrate that external stimulation can increase or hasten maturation of network signaling as evidenced an increase in complex bursts. Herein, we demonstrate that repetitive stimulation with a recorded synaptic signal was capable of increasing overall signaling, including the percentage of bursts, over a 5-day period, but that this increase was completely prevented by the presence of the GABAergic antagonist bicuculline. These findings demonstrate a critical role for inhibitory neurons in signal maturation following stimulation, which supports the purported role for inhibitory neuronal activity in long-term potentiation and learning in situ.

Accelerated establishment of mature signaling patterns following stimulation of developing neuronal networks: "learning" versus "plasticity"

Neuronal networks established on micro-electrode arrays provide useful models for synaptic plasticity. Whether or not this represents a facet of learning is debated since ex vivo networks are deprived of organismal interaction with the environment. We compared developmental signaling of such networks with and without stimulation with a prerecorded synaptic signal from another mature culture as a model of sensory input. Unstimulated networks displayed a developmental increase in individual signals that eventually declined, yielding a pattern containing organized bursts of signaling. Minimal stimulation, to model the onset of sensory input hastened the onset of developmental signaling. However, the overall developmental pattern of stimulated networks, including the total number and type of signals as well as the length of this developmental period, was identical to that of unstimulated networks. One interpretation of these findings is that ongoing plasticity may be essential to establish an appropriate platform for learning once sensory input ensues.

Adult neural progenitor cells reactivate superbursting in mature neural networks

Behavioral recovery in animal models of human CNS syndromes suggests that transplanted stem cell derivatives can augment damaged neural networks but the mechanisms behind potentiated recovery remain elusive. Here we use microelectrode array (MEA) technology to document neural activity and network integration as rat primary neurons and rat hippocampal neural progenitor cells (NPCs) differentiate and mature. The natural transition from neuroblast to functional excitatory neuron consists of intermediate phases of differentiation characterized by coupled activity. High-frequency network-wide bursting or "superbursting" is a hallmark of early plasticity that is ultimately refined into mature stable neural network activity. Microelectrode array (MEA)-plated neurons transition through this stage of coupled superbursting before establishing mature neuronal phenotypes in vitro. When plated alone, adult rat hippocampal NPC-derived neurons fail to establish the synchronized bursting activity that neurons in primary and embryonic stem cell-derived cultures readily form. However, adult rat hippocampal NPCs evoke re-emergent superbursting in electrophysiologically mature rat primary neural cultures. Developmental superbursting is thought to accompany transient states of heightened plasticity both in culture preparations and across brain regions. Future work exploring whether NPCs can re-stimulate developmental states in injury models would be an interesting test of their regenerative potential.

Causal measures of structure and plasticity in simulated and living neural networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time.

Cortical networks grown on microelectrode arrays as a biosensor for botulinum toxin

Botulinum toxin (BoNT) is a potent neurotoxin produced by toxigenic strains of Clostridium botulinum. Botulinum toxin poses a major threat since it could be employed in a deliberate attack on the U.S. food supply. Furthermore, BoNT may be liberated in any insufficiently processed food containing a reduced oxygen atmosphere. Hence, rapid and reliable detection of BoNT in foods is necessary to reduce risks posed through food contamination. We present a BoNT biosensor employing living neural cultures grown in vitro on microelectrode arrays (MEAs). An MEA is a culture dish with a grid of electrodes embedded in its surface, enabling extracellular recording of action potentials of neural cultures grown over the array. Pharmaceutical grade BoNT A was applied to the media bath of mature cortical networks cultured on MEAs. Both spontaneous and evoked activities were monitored over 1 wk to quantify changes in the neural population produced by BoNT A. Introduction of BoNT A resulted in an increased duration and number of spikes in spontaneous and evoked bursts relative to control cultures. Increases were significant within 48 h of BoNT A dosage (P < 0.05). Application of BoNT A also induced unique oscillatory behavior within each burst that is reminiscent of early developmental activity patterns rather than the mature cultures used here. Three or more activity peaks were observed in 50% of the BoNT dosed cultures. Control cultures exhibited only a single activity peak. Thus activity of these cortical networks measured with MEAs could provide a valuable substrate for BoNT detection.

Identification and dynamics of spontaneous burst initiation zones in unidimensional neuronal cultures

Spontaneous activity is typical of in vitro neural networks, often in the form of large population bursts. The origins of this activity are attributed to intrinsically bursting neurons and to noisy backgrounds as well as to recurrent network connections. Spontaneous activity is often observed to emanate from localized sources or initiation zones, propagating from there to excite large populations of neurons. In this study, we use unidimensional cultures to overcome experimental difficulties in identifying initiation zones in vivo and in dissociated two-dimensional cultures. We found that spontaneous activity in these cultures is initiated exclusively in localized zones that are characterized by high neuronal density but also by recurrent and inhibitory network connections. We demonstrate that initiation zones compete in driving network activity in a winner-takes-most scenario.

Differential control of active and silent phases in relaxation models of neuronal rhythms

Rhythmic bursting activity, found in many biological systems, serves a variety of important functions. Such activity is composed of episodes, or bursts (the active phase, AP) that are separated by quiescent periods (the silent phase, SP). Here, we use mean field, firing rate models of excitatory neural network activity to study how AP and SP durations depend on two critical network parameters that control network connectivity and cellular excitability. In these models, the AP and SP correspond to the network's underlying bistability on a fast time scale due to rapid recurrent excitatory connectivity. Activity switches between the AP and SP because of two types of slow negative feedback: synaptic depression-which has a divisive effect on the network input/output function, or cellular adaptation-a subtractive effect on the input/output function. We show that if a model incorporates the divisive process (regardless of the presence of the subtractive process), then increasing cellular excitability will speed up the activity, mostly by decreasing the silent phase. Reciprocally, if the subtractive process is present, increasing the excitatory connectivity will slow down the activity, mostly by lengthening the active phase. We also show that the model incorporating both slow processes is less sensitive to parameter variations than the models with only one process. Finally, we note that these network models are formally analogous to a type of cellular pacemaker and thus similar results apply to these cellular pacemakers.

Transport of information along unidimensional layered networks of dissociated hippocampal neurons and implications for rate coding

The ability of synchronous population activity in layered networks to transmit a rate code is a focus of recent debate. We investigate these issues using a patterned unidimensional hippocampal culture. The network exhibits population bursts that travel its full length, with the advantage that signals propagate along a clearly defined path. The amplitudes of activity are measured using calcium imaging, a good approximate of population rate code, and the distortion of the signal as it travels is analyzed. We demonstrate that propagation along the line is precisely described by information theory as a chain of Gaussian communication channels. The balance of excitatory and inhibitory synapses is crucial for this transmission. However, amplitude information carried along this layered neuronal structure fails within 3 mm, approximately 10 mean axon lengths, and is limited by noise in the synaptic transmission. We conclude that rate codes cannot be reliably transmitted through long layered networks.