Gesture tracking and neural activity segmentation in head-fixed behaving mice by deep learning methods
The typical approach used by neuroscientists is to study the response of laboratory animals to a stimulus while recording their neural activity at the same time. With the advent of calcium imaging technology, researchers can now study neural activity at sub-cellular resolutions in vivo. Similarly, r...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/674476 |
| Acceso en línea: | http://hdl.handle.net/10803/674476 |
| Access Level: | acceso abierto |
| Palabra clave: | neurociència neurociencia neuroscience activitat neuronal actividad neuronal neural activity dades de comportament datos de comportamiento behavioral data xarxa neuronal convolucional tridimensional (3D-CNN) red neuronal convolucional tridimensional (3D-CNN) 3-dimensional convolutional neural network (3D-CNN) xarxa de memòria a llarg i curt termini (LSTM) red de memoria a largo y corto plazo (LSTM) long-term and short-term memory network (LSTM) Neurociència 616.8 |
| Sumario: | The typical approach used by neuroscientists is to study the response of laboratory animals to a stimulus while recording their neural activity at the same time. With the advent of calcium imaging technology, researchers can now study neural activity at sub-cellular resolutions in vivo. Similarly, recording the behaviour of laboratory animals is also becoming more affordable. Although it is now easier to record behavioural and neural data, this data comes with its own set of challenges. The biggest challenge, given the sheer volume of the data, is annotation. A traditional approach is to annotate the data manually, frame by frame. With behavioural data, manual annotation is done by looking at each frame and tracing the animals; with neural data, this is carried out by a trained neuroscientist. In this research, we propose automated tools based on deep learning that can aid in the processing of behavioural and neural data. These tools will help neuroscientists annotate and analyse the data they acquire in an automated and reliable way. |
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