A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents.

Spontaneous synaptic activity is a hallmark of biological neural networks. A thorough description of these synaptic signals is essential for understanding neurotransmitter release and the generation of a postsynaptic response. However, the complexity of synaptic current trajectories has either precluded an in-depth analysis or it has forced human observers to resort to manual or semi-automated approaches based on subjective amplitude and area threshold settings. Both procedures are time-consuming, error-prone and likely affected by human bias. Here, we present three complimentary methods for a fully automated analysis of spontaneous excitatory postsynaptic currents measured in major cell types of the mouse retina and in a primary culture of mouse auditory cortex. Two approaches rely on classical threshold methods, while the third represents a novel machine learning-based algorithm. Comparison with frequently used existing methods demonstrates the suitability of our algorithms for an unbiased and efficient analysis of synaptic signals in the central nervous system.

Cell Types and Synapses Expressing the SNARE Complex Regulating Proteins Complexin 1 and Complexin 2 in Mammalian Retina.

Complexins (Cplxs) 1 to 4 are components of the presynaptic compartment of chemical synapses where they regulate important steps in synaptic vesicle exocytosis. In the retina, all four Cplxs are present, and while we know a lot about Cplxs 3 and 4, little is known about Cplxs 1 and 2. Here, we performed in situ hybridization experiments and bioinformatics and exploited Cplx 1 and Cplx 2 single-knockout mice combined with immunocytochemistry and light microscopy to characterize in detail the cell type and synapse-specific distribution of Cplx 1 and Cplx 2. We found that Cplx 2 and not Cplx 1 is the main isoform expressed in normal and displaced amacrine cells and ganglion cells in mouse retinae and that amacrine cells seem to operate with a single Cplx isoform at their conventional chemical synapses. Surprising was the finding that retinal function, determined with electroretinographic recordings, was altered in Cplx 1 but not Cplx 2 single-knockout mice. In summary, the results provide an important basis for future studies on the function of Cplxs 1 and 2 in the processing of visual signals in the mammalian retina.

The structure dilemma in biological and artificial neural networks.

Brain research up to date has revealed that structure and function are highly related. Thus, for example, studies have repeatedly shown that the brains of patients suffering from schizophrenia or other diseases have a different connectome compared to healthy people. Apart from stochastic processes, however, an inherent logic describing how neurons connect to each other has not yet been identified. We revisited this structural dilemma by comparing and analyzing artificial and biological-based neural networks. Namely, we used feed-forward and recurrent artificial neural networks as well as networks based on the structure of the micro-connectome of C. elegans and of the human macro-connectome. We trained these diverse networks, which markedly differ in their architecture, initialization and pruning technique, and we found remarkable parallels between biological-based and artificial neural networks, as we were additionally able to show that the dilemma is also present in artificial neural networks. Our findings show that structure contains all the information, but that this structure is not exclusive. Indeed, the same structure was able to solve completely different problems with only minimal adjustments. We particularly put interest on the influence of weights and the neuron offset value, as they show a different adaption behaviour. Our findings open up new questions in the fields of artificial and biological information processing research.