Groups of neurons throughout the brain exhibit coherent (correlated or synchronized) activity. This remarkable observation has led many to hypothesize that coherent spikes may be interpreted by the nervous system differently from non-coherent spikes. This hypothesis, however, has been difficult to test empirically because of the lack of tools to manipulate spike coherence. An additional obstacle has been the lack of an in vivo preparation where one can record from neurons whose connectivity is known. I propose to study the function of spike coherence in the olfactory system of the fruit fly using genetic, electrophysiological, pharmacological, and behavioral techniques. I will first measure the timescale of coherent activity among olfactory receptor neurons and develop a genetic technique to manipulate coherence. I will then systematically vary the coherence in olfactory receptor neurons while measuring spikes in postsynaptic second-order neurons to ask how coherence affects the speed and magnitude of the postsynaptic spiking response. I will then determine how local inhibition influences the sensitivity to coherence of the postsynaptic neuron. Finally, I will make behavioral measurements to ask whether the neural sensitivity to coherence limits behavioral sensitivity. This study will help elucidate the function and mechanism of coherent activity in the brain and its importance for behavior.
Abnormal neural coherence appears in numerous neurological diseases, including schizophrenia, epilepsy, autism, Alzheimer's disease, Parkinson's disease, and multiple sclerosis as well as playing an important role in cognitive functions such as learning and attention. Understanding how neural coherence influences downstream circuitry is thus of critical importance for both understanding normal brain function and for the development of treatments for numerous debilitating disorders.
|Jeanne, James M; Fi?ek, Mehmet; Wilson, Rachel I (2018) The Organization of Projections from Olfactory Glomeruli onto Higher-Order Neurons. Neuron 98:1198-1213.e6|
|Jeanne, James M; Wilson, Rachel I (2015) Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy. Neuron 88:1014-1026|