Pattern separation is the process by which the brain distinguishes between similar or overlapping features of the external world. This fundamental computation is performed by multilayered circuits ? including the dentate gyrus, cerebellar cortex, and insect mushroom body ? that expand and diversify neural representations. Diversity in ?space? (i.e. the identities of neurons responding to a given stimulus) is achieved by sparse and unstructured synaptic connectivity, but we currently lack an understanding of how synapses generate diversity in time to support temporal pattern separation. This gap is due to two basic challenges: (1) measuring dynamical properties of multiple synaptic inputs to a given neuron and (2) causally perturbing those properties while monitoring downstream neurons and behavior. Here we propose to overcome these challenges, by investigating synaptic mechanisms of temporal pattern separation in a tractable experimental system: the mushroom body of the fruit fly, Drosophila. We have developed new methods to rapidly characterize the short-term plasticity dynamics of multiple synaptic inputs to a single neuron, as well as genetic strategies to perturb these dynamics. This enables a systematic examination of how synapses generate diversity over time to separate fine temporal differences in sensory inputs.
In Aim 1, we will determine how populations of mushroom body neurons use temporal diversity to expand their sensory coding capacity.
In Aim 2, we will combine 2-photon optogenetic stimulation with whole- cell electrophysiology to examine the organization of short-term plasticity properties across the synaptic inputs to the mushroom body.
In Aim 3, we will experimentally manipulate short-term plasticity at mushroom body input synapses to study their role in associative learning. Together, these studies will reveal synaptic and cellular mechanisms for temporal pattern separation in the mushroom body. Although there are differences between flies and mammals, the basic logic of pattern separation is strikingly conserved between circuits of invertebrates and vertebrates. These similarities suggest that discoveries made in the fruit fly will be relevant to the mechanisms of temporal pattern separation in other animals. A more thorough understanding of how short-term synaptic plasticity implements higher-order computations has the potential to transform our understanding of the role of timing in learning and memory, which could lead to improved treatments for memory-related disorders.
Nearly all synapses in the brain exhibit some form of short-term plasticity, yet the computational functions of this plasticity are poorly understood. This proposal applies innovative methods to develop fundamental knowledge about how synaptic plasticity implements the canonical computation of temporal pattern separation. An improved understanding of how the brain processes information over time could help guide the development of improved therapies for memory-related disorders.
