Our ability to learn relies on the potential of neuronal circuits to change through experience. The overall theme of this project is to understand how cortical circuits are modified by experience. In this project, a computer science and a neurophysiology research group join strengths to apply the latest methodologies from image processing and machine learning, to analyse and interpret imaging data of cortical network activity. The PhD student will receive training in working at the life sciences/computing interface since the project combines state-of-the art neurophysiology with computational methods of data analysis.
The aim of this project is to characterize the activity of different types of inhibitory and excitatory neurons in the adult mouse primary visual cortex (V1), during the learning of a visually-guided behaviour. To this aim, the student will be trained and will apply the technique of two-photon calcium imaging that allows in vivo imaging of the activity of large neuronal populations with single-cell resolution, in awake behaving mice (Peron et al., 2015). The activity of the different neuronal populations will be monitored before, during and after the acquisition of a behavioural task acquired in a virtual reality environment. The mice will be trained to recognize, along the virtual track, a visual cue associated with a reward. The project is organized around 3 aims:
Aim 1. To image the activity of large populations of neurons in layers 2/3 and 4, before, during and after the learning of the behavioural task. By using 3-D two-photon imaging, somatic calcium responses of neurons expressing a genetically-encoded calcium indicator in layer 2/3 and layer 4 will be imaged.
Aim 2. Development of analysis tools for big imaging data. This aim will benefit from the results obtained in the framework of a BBSRC grant awarded to the two supervisors of this project (call: Support for development of bioinformatics tools and computational approaches to the biosciences; title: FOCIA: Fast, Open, Cellular Imaging Analysis toolbox). These methods will be used for the analysis of the functional organization of population activity and its changes during the learning of the behavioural task.
Aim 3. The findings in aims 1 and 2 will be synthesized in a computational model of experience-dependent plasticity in granular and supragranular layers in the primary visual cortex.
This interdisciplinary project is combining state-of-the art in vivo imaging with powerful analysis and computational methods applied to big neuronal population activity. Both the imaging method and the behavioural task are already established in the laboratory. Imaging data obtained in the 2-photon set-up of the lab have recently been published (Pakan et al., 2016).
Selected publications relevant to proposal
Pakan JM, Lowe SC, Dylda E, Keemink SW, Currie SP, Coutts CA, Rochefort NL. Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex. Elife. 2016 Aug 23;5. pii: e14985. doi: 10.7554/eLife.14985.
Peron, Chen, Svoboda, 2015. Comprehensive imaging of cortical networks. Current opinion in neurobiology 32, 115–123.
Costa, Sjöström JP, Van Rossum M. Probabilistic Inference of Short-Term Synaptic Plasticity in Neocortical Microcircuits, Frontiers in Computational Neuroscience 7, 75 (2013).