Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Individual neurons work on short timescales; from an action potential duration of 1 ms to synaptic currents that persist for just 10 ms or 100 ms of milliseconds. Yet, neurons embedded within a network are capable of maintaining persistent activity in the absence of external stimuli. One prominent computational theory uses 'attractor dynamics', whereby the connectivity of the network stabilizes activity without additional input to the network. In general, this is implemented in network that consists of recurrent excitatory connections between neurons that code for similar features along with feedforward inhibitory connectivity to silence the activity of neurons coding for non-similar features. This creates a competitive network that will sustain its current activity without any additional input, and will not change until a new input is strong enough to change the ‘attractor state’. Importantly, this framework can encode discrete and continuous ‘analog’ variables. Here we propose to look for evidence of attractor dynamics in the head direction (HD) cell network.?

The HD system has almost exclusively been modeled using a continuous ring attractor network. Briefly, activity representing the current HD is maintained by traditional attractor connectivity, while activity is gradually moved along this continuous ring attractor by vestibular inputs that relay head movements of the animal. We will use optical imaging in the anterior thalamic nucleus (ATN), which contains abundant HD cells, in an attempt to visualize this activity in freely behaving mice. We will use and further develop miniaturized microscopes that will allow us to record from hundreds of HD cells simultaneously. This approach is a major improvement over traditional electrode recordings, with which researchers have only recorded from up to 10 HD cells at a time. We will investigate whether attractor-like dynamics exist within the head direction network. We will develop the hardware and software for a closed-loop system to perform optogenetic perturbations based on slight head movements, perform optical imaging of HD cells in a variety of behaviors to look for ring attractors and coherent drift in the network, and finally will perform optogenetic silencing of the HD generation circuitry to determine to induce and analyze larger shifts in the HD network and compare this larger drift to behavior in a path integration task. These experiments will provide the first optical recordings of large populations of HD cells and will provide insight into the network dynamics of the HD network.