Neural mechanisms for decoding olfactory information in Drosophila

Yale University, New Haven CT

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Abstract

Project abstract Neural encoding, the process by which the brain converts sensory stimuli into patterns of electrical activity within neurons, is critical for sensation to guide action. Despite this importance, little is known about how neural codes are actually used – or “decoded” – by downstream networks in the brain. This gap is due to two basic challenges: (1) identifying the exact target neurons that decode the activity in a neural population, and (2) recording from and manipulating those neurons in vivo during natural sensory stimulation. Here, we propose to overcome these challenges by investigating how an olfactory neural code is decoded by its downstream network in a tractable experimental system: the fruit fly, Drosophila. We use new approaches to precisely control and measure the spatiotemporal dynamics of the population of central projection neurons while recording from and manipulating their postsynaptic target neurons and networks. This affords direct investigation into the biophysical mechanisms that enable downstream neurons to decode specific information from the olfactory neural population code. In Aim 1, we will control temporal correlations of activity in projection neurons to determine how these patterns are decoded by downstream neurons. In Aim 2, we will manipulate ion channels, pumps, and synaptic inhibition in downstream neurons to identify mechanisms of decoding. In Aim 3, we will test how these biophysical mechanisms of neural decoding sculpt behavioral algorithms. Together, these studies will reveal basic mechanisms by which the brain decodes its own neural code for olfaction. The basic logic of neural coding is remarkably conserved between invertebrates and vertebrates. These similarities suggest that discoveries made in the fruit fly will be relevant to the mechanisms of decoding in other animals. A more thorough understanding of the principles of neural decoding within the brain has the potential to transform the development of novel brain-machine interfaces that could improve the outcomes of patients with brain injuries.