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- Neuronales Netz (2)
- Connectivity Estimation (1)
- Inhibitory and Excitatory (1)
- Neuronal Networks (1)
- Parallel Spike Trains (1)
- biological neural network (1)
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- in silico (1)
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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.
Understanding the complexity of biological neural
networks like the human brain is one of the scientific
challenges of our century. The organization of the brain
can be described at different levels, ranging from small neural
networks to entire brain regions. Existing methods for
the description of functionally or effective connectivity are
based on the analysis of relations between the activities of
different neural units by detecting correlations or information
flow. This is a crucial step in understanding neural disorders
like Alzheimer’s disease and their causative factors.
To evaluate these estimation methods, it is necessary to refer
to a neural network with known connectivity, which is
typically unknown for natural biological neural networks.
Therefore, network simulations, also in silico, are available.
In this work, the in silico simulation of large scale neural
networks is established and the influence of different topologies
on the generated patterns of neuronal signals is investigated.
The goal is to develop standard evaluation methods
for neurocomputational algorithms with a realistic large
scale model to enable benchmarking and comparability of
different studies.