| EPSRC Reference: |
EP/V048716/1 |
| Title: |
NetClamp: conducting neural network rhythms with mathematics |
| Principal Investigator: |
Tabak, Dr J |
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| Department: |
Institute of Biomed & Clinical Science |
| Organisation: |
University of Exeter |
| Scheme: |
Standard Research - NR1 |
| Starts: |
21 June 2021 |
Ends: |
19 January 2024 |
Value (£): |
201,337
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| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
All of our behaviours, from recognising friends, to remembering where we parked our car, to making a cup a tea, result from the coordinated behaviour of networks of neurons in our brain. Each behaviour is defined by a specific pattern of electrical activity in these networks. Understanding how these patterns are generated is one of the key problems in neuroscience.
Solving this problem requires cooperation between lab-based, and theory-based neuroscientists. Lab-based neuroscientists determine how individual neurons work electrically and determine how neurons communicate with each other. They create maps of connections which represent which neurons influence the electrical activity of which other neurons.
Theory-based neuroscientists use this information to construct mathematical descriptions of neural network activity, called 'models'. Models predict how the map of connections in a network determines the patterns of electrical activity. They show that subtle changes in the map can have large effects on network activity. For example, activity can switch from seemingly random to highly coordinated, with rhythmic changes in neuron activity resembling a Mexican wave in football stadiums.
In some brain regions, coordinated rhythms are healthy. For example, our breathing is controlled by a brain area with highly synchronised activity. In other contexts, such as epilepsy, high levels of synchrony can lead to seizures. By uncovering how maps of connections affect network activity, mathematical models can be a useful part of the neuroscientist toolkit.
Models are only useful if we can show that they match results from experiments with real neural networks. This requires altering the map of connections between neurons. To this end, lab-based neuroscientists can turn on and off groups of neurons, and can block large fractions of the connections in a network in rather crude ways. However, there is currently no experimental way to alter the connections in the subtle way required for testing models.
This project is about building new technology to address this. To do this, we will integrate recently developed experimental tools with models in a single system. The experimental tools allow us to measure electrical activity in neurons using a digital camera, and to alter this activity by shining light of specific colour and intensity on each neuron. The key component of our system is that we will combine these tools with a mathematical model of the connection map between neurons.
The model will use the camera recordings of electrical activity to calculate what input signals each neuron in the network should receive according to the map of connections. It will then control an illumination system, which will shine light patterns to deliver the computed signals to each neuron. In this way, we will be able to manipulate the map of connections between neurons in the same way as our mathematical model.
This system will enable lab-based neuroscientists to do experiments in a radically new way. They will be able to explore how communication between neurons affects network activity with the same freedom that theory-based neuroscientists enjoy with models. They will be able to directly test theories about how connection maps shape patterns of activity. This will be a significant step towards understanding how the brain creates behaviour.
Our system will also enable the development of smart implants to treat brain diseases such as Parkinson's and epilepsy, which are characterised by abnormal network rhythms. When drugs fail in changing these rhythms, doctors may turn to drastically invasive medical procedures that permanently alter network structures. However, if the right connections can be altered at the right time, more subtle therapies might suffice. Future smart implants will detect when abnormal activity starts, then shine light to specific neurons to modify their connections to restore normal, healthy activity.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.ex.ac.uk |