Experimental analysis and computational modeling of interburst intervals in spontaneous activity of cortical neuronal culture

Rhythmic bursting is the most striking behavior of cultured cortical networks and may start in the second week after plating. In this study, we focus on the intervals between spontaneously occurring bursts, and compare experimentally recorded values with model simulations. In the models, we use standard neurons and synapses, with physiologically plausible parameters taken from literature. All networks had a random recurrent architecture with sparsely connected neurons. The number of neurons varied between 500 and 5,000. We find that network models with homogeneous synaptic strengths produce asynchronous spiking or stable regular bursts. The latter, however, are in a range not seen in recordings. By increasing the synaptic strength in a (randomly chosen) subset of neurons, our simulations show interburst intervals (IBIs) that agree better with in vitro experiments. In this regime, called weakly synchronized, the models produce irregular network bursts, which are initiated by neurons with relatively stronger synapses. In some noise-driven networks, a subthreshold, deterministic, input is applied to neurons with strong synapses, to mimic pacemaker network drive. We show that models with such “intrinsically active neurons” (pacemaker-driven models) tend to generate IBIs that are determined by the frequency of the fastest pacemaker and do not resemble experimental data. Alternatively, noise-driven models yield realistic IBIs. Generally, we found that large-scale noise-driven neuronal network models required synaptic strengths with a bimodal distribution to reproduce the experimentally observed IBI range. Our results imply that the results obtained from small network models cannot simply be extrapolated to models of more realistic size. Synaptic strengths in large-scale neuronal network simulations need readjustment to a bimodal distribution, whereas small networks do not require such change

Neurophysiological modeling of Voiding in Rats

Like man, most animals need a regular supply of nutriments. Eating and drinking cater for this need, but not with 100% efficiency. A considerable part of the conswned goods are expelled from the body as defecation. Waste products from the blood are filtered by the kidneys and excreted as urine. 1ms process continues throughout the day. In many situations, however, it would be very inconvenient to be expelling urine. Animals that leak urine continuously are more easily traced by preditors than the ones that don't. Besides this, for mankind it is socially intolerable at certain moments. Nature supplied q solution that enables storage of urine until a convenient moment for voiding: the bladder. Upon desire the bladder contracts and urine is expelled through the urethra, which, with the bladder, constitutes ti,e lower urinary tract. Thus the bladder owner controls when and where to expel urine

Ghrelin accelerates synapse formation and activity development in cultured cortical networks

Background: While ghrelin was initially related to appetite stimulation and growth hormone secretion, it also has a neuroprotective effect in neurodegenerative diseases and regulates cognitive function. The cellular basis of those processes is related to synaptic efficacy and plasticity. Previous studies have shown that ghrelin not only stimulates synapse formation in cultured cortical neurons and hippocampal slices, but also alters some of the electrophysiological properties of neurons in the hypothalamus, amygdala and other subcortical areas. However, direct evidence for ghrelin's ability to modulate the activity in cortical neurons is not available yet. In this study, we investigated the effect of acylated ghrelin on the development of the activity level and activity patterns in cortical neurons, in relation to its effect on synaptogenesis. Additionally, we quantitatively evaluated the expression of the receptor for acylated ghrelin - growth hormone secretagogue receptor-1a (GHSR-1a) during development. Results: We performed electrophysiology and immunohistochemistry on dissociated cortical cultures from neonates, treated chronically with acylated ghrelin. On average 76 � 4.6% of the cortical neurons expressed GHSR-1a. Synapse density was found to be much higher in ghrelin treated cultures than in controls across all age groups (1, 2 or 3 weeks). In all cultures (control and ghrelin treated), network activity gradually increased until it reached a maximum after approximately 3 weeks, followed by a slight decrease towards a plateau. During early developmental stages (1-2 weeks), the activity was much higher in ghrelin treated cultures and consequently, they reached the plateau value almost a week earlier than controls. Conclusions: Acylated ghrelin leads to earlier network formation and activation in cultured cortical neuronal networks, the latter being a possibly consequence of accelerated synaptogenesis

Intra-burst firing characteristics as network state parameters

Introduction \ud In our group we are aiming to demonstrate learning and memory capabilities of cultured networks of cortical neurons. A first step is to identify parameters that accurately describe changes in the network due to learning. Usually, such parameters are calculated from the responses to test-stimuli before and after a learning experiment. We propose that parameters should be calculated from the spontaneous activity before and after a learning experiment, as the applying of test-stimuli itself may alter the network. Since bursting is dominant in our cultures, we have investigated its spatio-temporal structure. \ud \ud Methods \ud Networks of cortical neurons were cultured on a MEA. Over a period from 9 to 35 DIV, the spontaneous activity has been measured on a regular basis. Measurements on a single day are always continuous; otherwise cultures are kept in a stove under controlled conditions (37 ˚C, 5% CO2, 100% humidity). Network bursts were detected by analysing the Array-Wide Spiking Rate (AWSR, the sum of activity over all electrodes). Next, we estimated the instantaneous AWSR during a burst by convolving spike-occurrences with a Gaussian function. We investigated the changes in burst profiles over time by aligning them to their peak AWSR. In 4 hour recording sessions, we grouped the burst profiles over 1 hour, resulting in 4 average burst profiles per day. In addition, a sufficient amount of aligned bursts yielded enough data to calculate the contribution of each recording site. \ud \ud Results \ud The burst profiles, calculated over a period of 1 hour, generally show little variation (figure 1). In subsequent hours, the profiles gradually change shape. Over a period of days however, the shape can change dramatically (figure 2). The relatively slow changes over the period of hours indicate an underlying probabilistic structure in the AWSR during bursts. The apparent structure in the burst profiles result from the relationships between individual recording sites, and thus also on the connectivity in the neural network. This is revealed in more detail by showing the contributions of individual sites (figure 3). The spike envelopes have a shape that is too detailed to be described accurately by a small set of parameters. \ud \ud Discussion \ud The burst profiles prove to be stable over a period of one hour, and gradually change their shape over several hours, as has also been suggested in [1]. The day-to-day changes in burst profiles may be the result of these gradual changes, thereby suggesting an intrinsically changing network. However, they can also be the result of putting the cultures back in the stove. The spike envelopes per recording site offer more detailed descriptions of the network state than the burst profiles. This may however be the amount of detail required to reveal the changes made during learning experiments. A subsequent refinement can be made by identifying distinct subgroups of bursts, as has been suggested in [2]