Neuroscience

Summary

Neuroscience is the scientific study of the nervous system (the brain, spinal cord, and peripheral nervous system), its functions and disorders. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, psychology, physics, computer science, chemistry, medicine, statistics, and mathematical modeling to understand the fundamental and emergent properties of neurons, glia and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "epic challenge" of the biological sciences. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. The techniques used by neuroscientists have expanded enormously, from molecular and cellular studies of individual neurons to imaging of sensory, motor and cognitive tasks in the brain. History History of n

Official source

https://en.wikipedia.org/wiki/Neuroscience

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (100)

Related publications

Related people (18)

Related people

Related units (16)

Related units

Related concepts (228)

Related concepts

Related courses (46)

Related courses

Related lectures (79)

Related lectures

The field of neuroscience is witnessing a huge influx of experimental data thanks to the improvements in the data acquisition tools and techniques. Most of this data is being collected by thousands of experimenters located in various institutions around the world. There is also a growing interest in using this experimental data for building biologically realistic computational models of the neuronal systems. One such project i.e. the Blue Brain Project is developing a bottom-up neuronal modeling and simulation framework using experimental data collected at laboratories worldwide. However, to use the experimental data effectively in computational neuroscience research, the data needs to be annotated with metadata such as details of the experimental protocols and conditions, animal subject used etc. Most of the platforms for experimental neuroscience data management operate under the assumption that the primary data has been validated, and properly annotated before it is uploaded to the system. Thus putting the responsibility of validating, and annotating the experimental data with the experimenter conducting the study. Consequently, most experimenters maintain their own data using ad-hoc systems with non-standard metadata schemes; and only upload the final set of data to the data management platform. As a result, the metadata is usually incomplete, and does not always comply with the requirements of the data management platform, negatively impacting the reusability of their data. The current thesis work explored the question of the experimental data management within the context of the Blue Brain Project, and designs a data management pipeline catering for the acquisition, annotation and validation of the experimental data and associated metadata, in order to ensure that the data is usable for the computational modeling purposes. The pipeline enforces a data review process whereby incoming experimental data was made to go through a series of steps, analogous to the scientific review process, systematically improving the quality of the experimental data and associated metadata.

EPFL2011

Flight of mind: Sensorimotor and multisensory embodiment with aviation robotics, flight simulator, and virtual reality

Myeong Seop Song

Normally, humans experience an 'I' as residing in one's body and as the agent of one's actions. In other words, the self is experienced as being inside the body (self-location), as having a body (self-identification), and as being able to control the person's body (agency). Over the last decade, research in cognitive neuroscience has revealed that vestibular signals contribute to such aspects of the embodied self. On the other hand, the influences of embodiment on vestibular sensations have received scant attention. In my thesis, I highlight how changes in multisensory and sensorimotor embodiment impact vestibular sensations. For this purpose, I merged technologies from photorealistic virtual reality (VR), aviation robotics such as a drone, flight simulator, haptics with knowledge of cognitive neuroscience about embodiment and bodily self-consciousness (BSC). The multidisciplinary approaches provide new paradigms to modulate embodiment and investigate related vestibular sensations based on multisensory and sensorimotor mechanisms of BSC. Also, the approaches reveal the relationship between embodiment and vestibular sensations. In Study 1, we have objectively shown the possibility of the duplication of the self by measuring peripersonal space (PPS). To this aim, we provided participants with real-time feedback from a drone-mounted camera that autonomously followed them while they responded to multisensory stimuli on their body and in their environment and walked forward. Our results demonstrate that this setup induces two spatially distinct PPSs in healthy participants: reaction times for visuotactile stimuli were faster around both the physical body and the observed body. In Studies 2 & 3, we have added a photorealistic avatar and a haptic vest to a robotic flight simulator to enable and investigate personalized immersive flight experiences in healthy humans. Study 2 showed that modulations of embodiment using sensorimotor stimulation resulted in enhanced subjective flying experience, better piloting performance, and learning. In Study 3, we showed that multisensory - visual and tactile - stimulation could further increase embodiment and improve piloting performance. Furthermore, in both studies, we found a positive relationship between embodiment and flight sensations. In Studies 4 & 5, we developed a new multisensory body scanning technology platform to induce and investigate one of the most common experiences in which the center of awareness is dissociated from the physical body: out-of-body experiences (OBEs). OBEs represent an interesting link between embodiment and vestibular sensations as they are characterized by embodiment of an elevated aerial position and perspective in space that is commonly associated with prominent sensations of floating, elevation, and flight. In this study, we have investigated how several sensorimotor and audio-visual stimulation conditions affect subjective sensations of disembodiment, floating, lightness as well as behavioral measures of self-location. In conclusion, my work developed new paradigms to investigate embodiment, BSC, and vestibular processing. My work revealed the influence of embodiment on vestibular sensations and applied these insights between embodiment and vestibular sensations to the field of field engineering by combining state-of-the-art technologies with knowledge of BSC with broad implications for robotics, neuroscience, of immersive VR, as well as experiential

EPFL2020

Data-driven whole mouse brain modeling for multi-scale simulations

Dimitri Bruno Marie Rodarie

In this thesis, we present a data-driven iterative pipeline to generate, simulate and validate point-neuron models of the whole mouse brain. The ultimate goal is to replicate close loop experiments with a virtual body in a virtual world. This pipeline was origi-nally created by a PhD student of the Blue Brain Project and this pioneer work has yielded the first versions of the model and their simulations. My objective is to refine, extend and consolidate the previous version of the workflow and in particular to extend the repertoire of neuron types by integrating and consolidating available literature data.The pipeline has four main parts. First, we define a pair of reference atlases to create a spatial reference for every dataset that we want to integrate. Second, we create a cell atlas with estimates of the number and density of the major cell types in each brain region. Then, we build a connectivity atlas that describes the connections of each neuron of the mouse brain and their properties. Finally, we assign point-neuron parameters to each of the neuron types, and synaptic parameters to each of the connection types of our model to obtain a point-neuron network of the mouse brain. This network can be simulated to perform mouse brain experi-ments in-silico.In this thesis, we evaluate the quality of the different reference atlases, released by the Allen Institute for Brain Science and pro-vide methods to mitigate the impact of artifacts in the original images. We further extend the cell atlas with density estimates of more inhibitory neuron types. To do so, we introduce a new method to combine estimates of inhibitory neuron counts from the literature into a consistent framework. We also estimate inhibitory neuron density in regions where no literature data are avai-lable, using collected literature estimates and gene expression data from in situ hybridization image stacks. Our approach can be further extended to other cell types and provides a resource to build more detailed circuits of the mouse brain. Next, we refine the connectivity atlas, including literature findings on short-range connectivity. Additionally, we construct a database to store data collected on neuron types point-neuron parameters and synaptic parameters. With the results of the previous steps, we generate a new version of the whole mouse brain point-neuron network. We tested this new model against its previous version using three benchmark experiments. We explore the possibility to embed and connect detailed circuit reconstructions of the mouse brain to our point neuron model and co-simulate them. Finally, we constrain a 3D skeleton model of the mouse with joint constraints from the literature and attach muscle to its bones to create a musculoskeletal model of the mouse body that can be simulated in close-loop with our model to reproduce motor control experiments.

EPFL2022