Nicolas Brunel - US grants

One of the central hypotheses of neuroscience is that memories are stored in the brain through modifications of synaptic connectivity. This synaptic plasticity has been studied extensively in vitro, and these experiments have led to the formalization of mathematical rules that describe how specific patterns of pre- and post-synaptic activity lead to changes in synaptic strength. However, the vast majority of these experimental studies have been performed in conditions that are far from physiological. In particular, calcium is known to be critical for the induction of synaptic plasticity in many preparations, but the extracellular calcium concentration used in most studies is significantly higher than the experimentally measured concentration in vivo.

The goal of the present collaborative project is to understand the rules of synaptic plasticity under physiological conditions in two major brain structures, the hippocampus and the cerebellum. It groups together a theoretical team (Brunel) with expertise in synaptic plasticity models, and two experimental teams with expertise in hippocampal plasticity (Debanne) and cerebellar plasticity (Barbour). The theoretical team has recently discovered that in a calcium-based model of synaptic plasticity, lowering the extracellular calcium concentration drastically changes the standard plasticity rules that have been widely assumed. Preliminary experiments performed by both the Debanne and Barbour teams have confirmed that plasticity is reduced, or even completely abolished, in protocols that induce plasticity at higher calcium levels. This project will investigate further plasticity rules under conditions that are far closer to in vivo conditions than traditional in vitro studies, through a tight collaboration among the three labs. In particular, the collaborators will investigate whether neuromodulation and/or realistic patterns of activity can rescue plasticity that they found to be absent at physiological calcium levels. They aim to produce minimal biophysical models of synaptic plasticity that will fit the recorded data, and explain how plasticity is determined by a few key biophysical variables: extracellular calcium concentration, nitric oxide, and neuromodulators. Such a mathematical description of plasticity rules under physiological conditions will serve both the theoretical community, by enabling efficient simulation and analytical calculation, and experimental research, by offering insight into key mechanisms of plasticity.

This project will contribute to scientific exchanges between the US and France. Joint workshops will be held in both countries and their participants will contribute to a tutorial-based Masters course combining theoretical and experimental study of research problems in neuroscience. A companion project is being funded by the French National Research Agency (ANR).

Abstract The cerebellum is critical for learning and executing coordinated, well-timed movements. The cerebellar cortex seems to have a particular role in learning to time movements. Since the 1960's and 70's, we have known the architecture of the cerebellar microcircuit, but most analyses of cerebellar function during behavior have focused on Purkinje cells. Here, we propose to investigate the cerebellar cortex at an entirely new level by asking how the full cerebellar microcircuit ? mossy fiber, granule cells, Golgi cells, molecular layer interneurons, and Purkinje cells ? performs neural computations during motor behavior and motor learning. We strive to ?crack? the circuit by identifying all elements, recording their electrical activity during movement and learning, and reconstructing a neural circuit model that reproduces the biological data. We will use three established learning systems that all can learn predictive timing: classical conditioning of the eyelid response (mice), predictive timing of forelimb movements (mice), and direction learning in smooth pursuit eye movements (monkeys). Our proposal has six key features. First, optogenetics (in mice) will link the discharge of different cerebellar interneurons during movement and learning to their molecular cell types. Second, a machine-learning clustering analysis (in mice and monkeys) will find analogies among the cell populations recorded in our three preparations and will classify neurons according to their putative cell types based on recordings of many parameters of non-Purkinje cells during movement and motor learning. Third, multi- contact electrodes will allow us to record simultaneously from multiple neighboring single neurons and compute spike-timing cross-correlograms (CCGs) to identify the sign of connections; we also will look for changes in CCGs that provide evidence of specific sites of plasticity during learning. Fourth, gCAMP imaging of the granule cell layer will reveal the temporal structure of inputs to the cerebellar microcircuit, and determine whether those inputs are modified in relation to motor learning. Fifth, a model neural network with realistic cerebellar architecture will reveal a single set of model parameters that will transform the measured inputs to the cerebellum in our three movement systems to the measured responses of all neurons in the cerebellar cortex. Sixth, the model will elucidate how mechanisms of synaptic and cellular plasticity at different sites in the cerebellar microcircuit work together to cause motor learning.