Friday, May 9, 2025
5/9/2025
Unraveling the synaptic and circuit mechanisms underlying a plasticity-driving instructive signal - PROJECT SUMMARY Learning, fundamental to cognition, requires storing of information in flexible neural activation patterns and synaptic weight changes (i.e., plasticity) within neuronal ensembles. These representations are modified with experience on the timescale of seconds to minutes and even lifetimes. Although recent pivotal work has provided insights into how population activity drives memory-guided behaviors, many fundamental questions remain about the neural plasticity mechanisms that underlie the formation of these representations in response to new experiences. The standard synaptic plasticity rule (i.e., spike timing- dependent plasticity, STDP) requires precisely timed and repetitive pre- and postsynaptic activation, which is incongruent with the seemingly chaotic activity of networks in awake behaving animals. In contrast, behavioral timescale synaptic plasticity (BTSP), a learning rule I recently co-discovered to underlie the development of experience-dependent spatial representations in hippocampal CA1, requires only a single induction trial and operates on the cognitively relevant timescale of seconds. Thus, BTSP provides one of the first biologically plausible mechanisms for how a single experience can produce learning-related changes in brain activity. This previous research has positioned my laboratory to address fundamental questions regarding the circuit and synaptic mechanisms underlying learning. Building upon my published work, this proposal will test the model that the medial entorhinal cortex layer 3 (mEC3) serves as an instructor, providing a context-specific target signal to CA1 neurons via their tuft dendrites, thereby driving BTSP and directing the CA1 network in how to form a learning-related representation. Specifically, we will determine how the mEC3 produces this target signal. We will first use extracellular recordings with Neuropixels probes to monitor the neural activity from large populations of medial entorhinal cortex (mEC) neurons in awake mice during a flexible spatial memory paradigm that allows control over the learning time course. Using this approach, we will determine the flow of information through the mEC network. Second, we will use in vivo whole-cell recordings of mEC3 neurons during the same learning task to pinpoint the single-cell computations underlying the instructive signal. We will identify the processes involved, which may include changes in excitability, synaptic input integration, and plasticity. Third, we will combine activity recording techniques and optogenetics to determine the extent to which the instructive signal is produced by local computation or inherited from upstream cortical regions. This proposal will have a far-reaching influence on cellular, systems, and cognitive neuroscience. As learning is a fundamental component of virtually all major brain functions, understanding the neural algorithms of learning, from synaptic to population level neural coding, will provide a basis for understanding how the brain performs all complex tasks that depend upon learning.
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