Biology subjects

von der Behrens, W.

Publications and source records attributed to von der Behrens, W..

3 recordsLinked to original sources

bioRxiv2021

Towards on-chip real-time classification of extra-cellular neural recordings

On-line classification of neural recordings can be extremely useful in brain-machine interface, prosthetic applications or therapeutic intervention. In this work we present a feasibility study for developing compact low-power VLSI systems able to classify neural recordings in real-time, using spike-based neuromorphic circuits. We developed a framework for classifying extra-cellular recordings made in rat auditory cortex in response to different auditory stimuli and porting the classification algorithm onto a spiking multi-neuron VLSI chip with programmable synaptic weights. We present recording methods and software classification algorithms; we demonstrate real-time classification in hardware and quantify the system performance; finally, we identify the potential sources of problems in developing such types of systems and propose strategies for overcoming them.

neuroscience↗

bioRxiv2021

Electrocorticography based monitoring of anaesthetic depth in mice

To improve animal welfare and data quality and reproducibility during research conducted under anaesthesia, anaesthetic depth in laboratory animals must be precisely monitored and controlled. While a variety of methods have been developed to estimate the depth of anaesthesia in humans, such tools for monitoring anaesthetic depth in laboratory animals remain limited. Here we propose an epidural electrocorticogram-based monitoring system that accurately tracks the depth of anesthesia in mice receiving inhalable isoflurane anaesthesia. Several features of the electrocorticogram signals exhibit robust modulation by the concentration of the administered anesthetic, notably, corticocortical coherence serves as an excellent indicator of anaesthetic depth. We developed a gradient boosting regressor framework that utilizes the extracted features to accurately estimate the depth of anaesthesia. Our method for feature extraction and estimation is conducted with a latency of only ten seconds, establishing a system for the real-time tracking of anaesthetic depth in mice.

neuroscience↗

bioRxiv2020

SIPEC: the deep-learning Swiss knife for behavioral data analysis

The quantification of behaviors of interest from video data is commonly used to study brain function, the effects of pharmacological interventions, and genetic alterations. Existing approaches lack the capability to analyze the behavior of groups of animals in complex environments. We present a novel deep learning architecture for classifying individual and social animal behavior, even in complex environments directly from raw video frames, while requiring no intervention after initial human supervision. Our behavioral classifier is embedded in a pipeline (SIPEC) that performs segmentation, identification, pose-estimation, and classification of complex behavior, outperforming the state of the art. SIPEC successfully recognizes multiple behaviors of freely moving individual mice as well as socially interacting non-human primates in 3D, using data only from simple mono-vision cameras in home-cage setups.

neuroscience↗