Veranstaltungsort: FIAS Hörsaal

Das interdisziplinäre FIGSS Seminar findet während des Semesters jeden zweiten Montag von 12:00-13:00 statt. Die Doktoranden der Graduiertenschule erhalten hier die Möglichkeit, ihre Forschungsergebnisse vorzustellen und zu diskutieren. Von Zeit zu Zeit werden auch externe Sprecher eingeladen.

Programm am 15.01.2024

Vorträge von: Claudia Quinteros (Srivastava Group) und Deyue Kong (Kaschube Group). 

Abstracts:

Claudia Quinteros: A deep learning approach for large earthquakes monitoring using High-Rate Global Navigation Satellite System data

The High-rate Global Navigation Satellite System (HR-GNSS) instruments are devices that can measure ground displacement generated by an earthquake with high precision and detect the first seismic wave arrivals. By integrating HR-GNSS data with other sensors and models, we can improve the accuracy of earthquake assessments and provide valuable information for early warning and disaster preparedness. Our focus lies in developing deep-learning models leveraging HR-GNSS waveform data. These models significantly empower our capacity to detect, evaluate, and respond to large earthquakes. Yet, the rapid analysis of HR-GNSS data using deep learning algorithms remains a current challenge. To overcome this challenge, it is crucial to have access to large and high-quality datasets. Unfortunately, GNSS stations are not distributed enough in all the regions, which can lead to data gaps. Additionally, the presence of noise in GNSS recordings particularly impacts data quality, especially for earthquakes measuring below magnitude 7. As a consequence, our training of Deep Learning (DL) models primarily relies on the data available from the largest earthquakes—events that occur less frequently and provide a limited dataset, making it less representative for model training. Therefore, we have faced a lack of data and have used both synthetic and real HR-GNSS data for model training, validation, and testing. Our investigation explores how diverse factors—such as noise, earthquake magnitude, station density, distance from the epicenter, and duration of the signal—affect the performance of our models. Our ultimate aim is to generalize this methodology for real-time monitoring of large earthquakes across diverse tectonic regions.

Deyue Kong: Optogenetic inhibition reveals large-scale intracortical interactions during early development

In ferret visual cortex, spontaneous activity prior to eye-opening is organized into large-scale, modular patterns in the absence of long-range horizontal projections. This correlated activity reveals endogenous networks that predict aspects of future orientation selectivity [1].Previous modelling works have shown that the long-range correlations observed in these networks can arise purely from locally connected neurons through multi-synaptic interactions [1,2]. Here we seek to test the extent to which cortical activity is organized through lateral interactions using localized optogenetic perturbations in vivo.

We first constructed a recurrent neural network model of rectified excitatory and inhibitory units, with effective local heterogeneous Mexican hat connections. Our model predicts that perturbing a small region of inhibitory neurons leads to long-range reorganization in the spatial patterns of ongoing activity as well as network correlation structure. Notably, the degree of disruption to correlation structure depends on the perturbation location and can be predicted from the stimulation site’s overlap with the leading principal components of baseline spontaneous activity. Only a fraction of variance of perturbed activity patterns overlaps with the leading variance components of spontaneous activity, suggesting a shift in the activity manifold towards novel patterns after local disruption.

To test these predictions, we virally expressed GCaMP6s in excitatory neurons and Chrimson-ST in inhibitory neurons in layer 2/3 of young ferret visual cortex, allowing us to optogenetically activate small regions (~500μm diameter) of inhibitory neurons and simultaneously record widefield calcium activity. 

In line with model predictions, local optogenetic inhibitory perturbations induce a large-scale reorganization of activity, even in areas up to 2mm away from stimulation site. Perturbing locations that overlap better with prominent spontaneous neural modes leads to a larger degree of disruption. Furthermore, the variance in perturbed activity patterns can only partially be explained by spontaneous components, confirming that local perturbation indeed introduces new patterns.

Our results are consistent with the presence of strongly coupled E and I networks in early cortex, and demonstrate that network behaviour is an emergent property with local activity exerting specific and large-scale influences.  

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