NeuArc2Fun · Biological neural networks: from structure to function
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2016-03-01 → 2018-02-28
- Финансиране от ЕС
- 158 122 €
- Участници
- 1
- Схема
- MSCA-IF-EF-ST
Линиите свързват координатора с партньорите.
Накратко на български
Невронните мрежи с обратна връзка се анализират чрез данни от електрофизиология и fMRI, за да се разбере как групи от клетки обработват информация. Това помага да се предвиди как различните връзки между невроните оформят мозъчната активност.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
Biological neural networks: from structure to function
The NeuArc2Fun project proposal was to set at the connection between theoretical and experimental neuroscience. The goal was the investigation of information processing in neural networks with feedback. Technically, it aimed to adapt and improve analysis tools for newly collected data by nowadays recording and imaging techniques, which have much improved recently. The whole approach relies on the estimation and interpretation of interactions between large (mesoscopic) populations of neurons from their activity that is simultaneously recorded. This includes electrophysiology (e.g., Utah electrode array), calcium imaging or fMRI (considering whole-brain activity). In particular, the focus of NeuArc2Fun was on “large” networks (i.e., from 20 to hundreds of nodes), which is suitable for state-of-the-art measurements. It produced novel analysis tools for recurrently connected neural networks, bridging the structural and functional levels (towards information processing). The key for a useful formalism is finding the adequate balance between the mathematical tractability and biological realism of the model. To address this trade-off problem, NeuArc2Fun focuses on the mesoscopic level, i.e., scales at which many interacting neural populations can be simultaneously recorded by current state-of-the-art experimental techniques, such as electrode arrays. The advantage of this model-based approach is the ability to make predictions about the role of each component of the model –in particular, its heterogeneous connectivity– in shaping neural activity. A particular focus was on bridging several disciplines in a common comprehensive formalism: dynamic system, graph theory, statistics and information theory. The primary application of the framework was targeted at electrophysiological data recorded in monkeys from the laboratory of Prof. Thiele in Newcastle University. Modeling such data is particularly challenging because they exhibit a large variability over repeated trials in the same condition, which hinders the extraction of consistent condition-specific information.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
The present project lies at the connection between theoretical and experimental neuroscience. It investigates how information is processed in neural networks with feedback, via the firing activity. On the one hand, the past decades have seen a growing interest for the analysis of the functional connectivity, namely how the spiking activity of neural populations is organized spatially and temporally. These activity patterns are hypothesized to form the basis of neural information, i.e., how neurons collectively encode information. On the other hand, experiments have revealed the complex design of the neural circuitry with many levels of organization, from the local connectivity of neurons to broad-scale pathways between cortical areas. NeuArc2Fun aims to develop a recurrent neural network model that bridges these structural and functional levels. The advantage of this model-based approach is the ability to make predictions about the role of each component of the model - in particular, its connectivity - in shaping neural activity. A key issue is to keep a balance between the mathematical tractability and biological realism in the model. To address this trade-off problem, NeuArc2Fun focuses on the mesoscopic level, namely scales at which many interacting neural populations can be simultaneously recorded by current state-of-the-art experimental techniques, such as electrode arrays. In practice, experimental data from the visual cortex will be used to tune and test the network models. In turn, gaining precise knowledge about neural cognitive processing will be applied to design experiments and test new ideas for information coding in networks. To a broader extent, this project will also benefit to applications that involve information decoding and interaction with the brain, e.g., neural prostheses and brain-machine interfaces.
Оригинален текст от CORDIS (на английски).
Участници
- UNIVERSIDAD POMPEU FABRA · BarcelonaКоординаторИспания
Връзки
Данни: CORDIS, © Европейски съюз