Dopamine-induced hippocampal plasticity: A synaptic model of foraging in mice

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

The overall aim of this proposal is to investigate possible behavioural implications of the retroactive modulation of hippocampal plasticity that we recently discovered. The objective is to incorporate this novel learning rule in a computer model of the hippocampal network, in order to predict the behaviour of mice in a simple navigation task, and then test the predictions during equivalent learning tasks in behaving animals. Specifically, we will design a hippocampal network model consisting of 'place cells' coding for the location of the agent (artificial animal), projecting onto actor neurons determining the speed and direction of the agent. The new plasticity rule will be implemented at the synapses between place cells and actor neurons. Preliminary results suggest two fundamental advantages of the new learning rule over conventional reinforcement learning rules in such a network: 1) the agent learns from the absence of reward to enhance efficient exploration, and 2) the agent quickly 'unlearns' reward locations once reward is exhausted or absent. We will test the predictions in behaving mice using optogenetics to stimulate or silence dopaminergic reward input into the hippocampus during the task, using DAT-cre mice to express the optogenetic molecule selectively in dopaminergic neurons of the ventral tegmental area (VTA). We will also silence the plastic hippocampal CA3-CA1 synapses at different stages of task performance, using Grik4-cre mice cross-bred with Ai35 mice, to enable temporally-restricted optogenetic silencing of CA3 input. Finally, we would directly monitor synaptic weights at CA3-to-CA1 synaptic connections using extracellular multi-site recording of optogenetically-evoked field EPSPs during the task. The results of this study would indicate whether the computationally attractive properties of the novel learning rule discovered in a brain slice preparation operate in the intact mouse brain.

The proposed project aims to understand the effect of reward on synaptic plasticity. We have identified four areas of possible impact:
1. Computational neuroscience and machine learning. The results of our studies are likely to be of significant interest to researchers in computational neuroscience and machine learning. The algorithms used by the brain will be important for biologically inspired reinforcement learning. We will collaborate with computational neuroscientists to increase the impact of our results.
2. Robotics. Algorithms that are capable of seeking novelty and navigating towards rewarding locations would be useful to enable robots and drones to search more efficiently through an environment. We intend to contact investigators in robotics to explore possible interactions.
3. Education. Better understanding of the mechanisms of reward-based learning could have implications for education, and we would discuss with researchers in the neuroscience of education to understand if our basic findings might have practical applications in education.
4. Combating age-related memory decline. Better understanding of reward processing and how it influences memory could have important consequences for treatment of normal age-related memory decline as well as memory disorders such as dementia. We will share our insights with clinicians to enable them to utilise new basic understanding for treating patients.
In addition, we would communicate our research to the wider public.