25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Frequency-dependent signal transfer at the interface between electrogenic cells and nanocavity electrodes

We present a model to describe the response of chip-based nanocavity sensors during extracellular recording of action potentials. These sensors feature microelectrodes which are embedded in liquid-filled cavities. They can be used for the highly localized detection of electrical signals on a chip. We calculate the sensor's impedance and simulate the propagation of action potentials. Subsequently we apply our findings to analyze cell-chip coupling properties. The results are compared to experimental data obtained from cardiomyocyte-like cells. We show that both the impedance and the modeled action potentials fit the experimental data well. Furthermore, we find evidence for a large seal resistance of cardiomyocytes on nanocavity sensors compared to conventional planar recording systems

Nanocavity electrode array for recording from electrogenic cells

We present a new nanocavity device for highly localized on-chip recordings of action potentials from individual cells in a network. Microelectrode recordings have become the method of choice for recording extracellular action potentials from high density cultures or slices. Nevertheless, interfacing individual cells of a network with high resolution still remains challenging due to an insufficient coupling of the signal to small electrodes, exhibiting diameters below 10 µm. We show that this problem can be overcome by a new type of sensor that features an electrode, which is accessed via a small aperture and a nanosized cavity. Thus, the properties of large electrodes are combined with a high local resolution and a good seal resistance at the interface. Fabrication of the device can be performed with state-of-the-art clean room technology and sacrificial layer etching allowing integration of the devices into sensor arrays. We demonstrate the capability of such an array by recording the propagation of action potentials in a network of cardiomyocyte-like cells

Auswertung und Zusammenfassung der Literatur zu Emissionen in die Atmosphaere und ihre Einfluesse auf die globale Klimaentwicklung (1. aktualisierte Fassung)

Available from FIZ Karlsruhe / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Growing neuronal islands on multi-electrode arrays using an accurate positioning-μCP device

Background: Multi-electrode arrays (MEAs) allow non-invasive multi-unit recording in-vitro from cultured neuronal networks. For sufficient neuronal growth and adhesion on such MEAs, substrate preparation is required. Plating of dissociated neurons on a uniformly prepared MEA's surface results in the formation of spatially extended random networks with substantial inter-sample variability. Such cultures are not optimally suited to study the relationship between defined structure and dynamics in neuronal networks. To overcome these shortcomings, neurons can be cultured with pre-defined topology by spatially structured surface modification. Spatially structuring a MEA surface accurately and reproducibly with the equipment of a typical cell-culture laboratory is challenging. New method: In this paper, we present a novel approach utilizing micro-contact printing (mu CP) combined with a custom-made device to accurately position patterns on MEAs with high precision. We call this technique AP-mu CP (accurate positioning micro-contact printing). Comparison with existing methods: Other approaches presented in the literature using mu CP for patterning either relied on facilities or techniques not readily available in a standard cell culture laboratory, or they did not specify means of precise pattern positioning. Conclusion: Here we present a relatively simple device for reproducible and precise patterning in a standard cell-culture laboratory setting. The patterned neuronal islands on MEAs provide a basis for high throughput electrophysiology to study the dynamics of single neurons and neuronal networks