Barbara Priwitzer’s research while affiliated with Brandenburg University of Technology Cottbus - Senftenberg and other places

Background Microelectrode array (MEA) is a widely used technique to study for example the functional properties of neuronal networks derived from human embryonic stem cells (hESC-NN). With hESC-NN, we can investigate the earliest developmental stages of neuronal network formation in the human brain. Methods In this paper, we propose an in silico model of maturating hESC-NNs based on a phenomenological model called INEX. We focus on simulations of the development of bursts in hESC-NNs, which are the main feature of neuronal activation patterns. The model was developed with data from developing hESC-NN recordings on MEAs which showed increase in the neuronal activity during the investigated six measurement time points in the experimental and simulated data. ResultsOur simulations suggest that the maturation process of hESC-NN, resulting in the formation of bursts, can be explained by the development of synapses. Moreover, spike and burst rate both decreased at the last measurement time point suggesting a pruning of synapses as the weak ones are removed. Conclusions To conclude, our model reflects the assumption that the interaction between excitatory and inhibitory neurons during the maturation of a neuronal network and the spontaneous emergence of bursts are due to increased connectivity caused by the forming of new synapses.

Our aim is to develop a simple model which is suitable to simulate concentration-response curves as observed in in-vitro experiments with multielectrode array (MEA) neurochips. In an in-vitro experiment approximately 10.000 neurons of the frontal cortex of embryonic mice [1] are cultivated on a MEA neurochip [2]. Neuro-active substances like bicuculline are added to the network. Based on the recorded data, various features [3] are calculated adapted from spikes and bursts. The features are separately displayed in concentration-response curves [4] which show the logarithm of the substance concentration and the chosen feature. The developed INEX (inhibitory-excitatory) model is a cellular automaton whose cells are neurons with two possible states: ON or OFF. Each neuron obtains several inputs and produces exactly one output (respectively 0 or 1). Furthermore, it is phenomenological model where the neurons are described as black boxes. The probability if a spike occurs in time slice was calculated using a Poisson process [5]. Neurons are connected by either inhibitory or excitatory synapses with varying strength. The corresponding parameters are called weights. The network is fully connected and has direct feedbacks. Additionally, a spike time history was added. The aim was to vary the parameters of the model in such a way that we obtain a sigmoid concentration-response curve to simulate excitatory and inhibitory effects in neuronal networks. A network with 100 neurons ran over 10 seconds with varying weights and Δt = 1 ms. Ninety inhibitory synapses with weights between -0.2 and 0 and ten excitatory synapses with weights between 0 and 0.7 are used. We detected spikes and bursts (figure (figure1)1) as known from experiments with MEA neurochips. Thereafter, the same network ran over 18 minutes. The excitatory weights are reduced in six steps respectively by 0.05 every 3 minutes. The mean spike rate for each step is calculated and displayed in a concentration-response curve [6]. Figure 1 In each row, bursting spike trains of the first ten simulated neurons are displayed. Each dash marks a spike. The time scale (below) is in seconds. The INEX model shows potential to simulate inhibitory and excitatory effects which are also observed in experiments with MEA neurochips. A sigmoid concentration-response curve can be obtained by the simulation. We will work on parallelisation of processes to decrease the run time of the algorithm.