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- Neuronales Netz (2)
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- 2019 (4) (remove)
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Background: Connectivity is a relevant parameter for the information flow within neuronal networks. Network connectivity can be reconstructed from recorded spike train data. Various methods have been developed to estimate connectivity from spike trains. New method: In this work, a novel effective connectivity estimation algorithm called Total Spiking Probability Edges (TSPE) is proposed and evaluated. First, a cross-correlation between pairs of spike trains is calculated. Second, to distinguish between excitatory and inhibitory connections, edge filters are applied on the resulting cross-correlogram. Results: TSPE was evaluated with large scale in silico networks and enables almost perfect reconstructions (true positive rate of approx. 99% at a false positive rate of 1% for low density random networks) depending on the network topology and the spike train duration. A distinction between excitatory and inhibitory connections was possible. TSPE is computational effective and takes less than 3 min on a high-performance computer to estimate the connectivity of an 1 h dataset of 1000 spike trains. Comparison of existing methods: TSPE was compared with connectivity estimation algorithms like Transfer Entropy based methods, Filtered and Normalized Cross-Correlation Histogram and Normalized Cross-Correlation. In all test cases, TSPE outperformed the compared methods in the connectivity reconstruction accuracy. Conclusions: The results show that the accuracy of functional connectivity estimation of large scale neuronal networks has been enhanced by TSPE compared to state of the art methods. Furthermore, TSPE enables the classification of excitatory and inhibitory synaptic effects.
Graphene is a promising material both as a coating for existing neural electrodes as well as for transparent electrodes made exclusively from graphene. We studied graphene‐based microelectrodes by investigating their recording and stimulation properties in order to evaluate their suitability for neuronal implants. In this work, we compare three different electrode material compositions. Microelectrode arrays (MEA) with an electrode size of about 700 μm2 were prepared of gold, graphene on gold, and plain graphene on glass substrate. In order to reduce polymer contamination during graphene transfer, we employed a polymer‐free transfer and lift‐off process. Impedance studies revealed a value of 2.3 MΩ at 1 kHz for plain, and 0.88 MΩ for graphene on gold. Neuronal recording experiments showed a sufficient SNR for both graphene‐based materials and a stable impedance, unaffected by surface degradation metal electrodes are known for. Stimulation measurements yielded a charge injection capacity of 0.15 mC/cm2 using biphasic pulses of 1 ms and 1 μA transparent graphene electrodes. Cyclic voltammetry revealed a large voltage range of −1.4 V to +1.6 V before water electrolysis occurs. Graphene‐coated gold microelectrodes show enhanced recording properties, whereas plain graphene electrodes might be better suited for stimulation applications.
This paper presents an optimized laser-bonding process for piezoelectric energy-harvesters based on thin fluorinated-ethylene-propylene (FEP) foils, using an ultra-short-pulse(USP) laser. Due to the minimized thermal stress in the material during bonding, achieved bypulse durations of few picoseconds, we created seams down to 40μm width without generatingholes in the 12.5μm thick FEP-foils. Using a galvanometer scanning system allowed for fastbonding-speed up to several centimeters per second, making the process also suitable for largestructures and areas. The achieved bond strength of the seams under influence of shearingstress was examined using tensile testing, which showed a sufficient strength of about 25 % of the maximum strength of an unbonded, single layer of FEP.