Investigation into the synaptic origins of hippocampal replay

项目总结

Project Summary A fundamental goal of neuroscience is to understand how the synaptic architecture of the brain gives rise to neural dynamics. A central challenge in this line of inquiry is that the electrical activity of neurons is recorded in the intact subject, while anatomical analysis is conducted ex vivo. Therefore, it is often impossible to match the identities of neurons recorded in vivo with those under study in histological sections. Since the 1960s, it has been speculated that the existence of a synapse should manifest itself through the reliable spiking activity of the postsynaptic cell after the presynaptic spike. Recent stimulation studies have confirmed that the existence of strong glutamatergic synapses between excitatory and inhibitory hippocampal neurons can be inferred from analysis of spike timing relationships, thus allowing simultaneous access to partial circuit connectivity and neural dynamics in the awake, behaving subject. However, the limits of what spiking alone can, and cannot, reveal about the synapse are not well understood, as there are many reasons why a spike in a presynaptic cell could fail to elicit one in the postsynaptic target. This work is critical since neural computation is thought to depend on the pattern of synaptic strength. This computational supplement adds to the parent R00 grant, which seeks an understanding for how hippocampal neurons change their sequential firing patterns after learning, a phenomenon known as replay. Through Aim 1 of the parent R00, we found potential evidence that plasticity at the CA1 excitatory to inhibitory synapse could be a theoretical basis for replay. However, the observed changes in spike timing could arise from other, non-synaptic mechanisms. Here, we are requesting funds for statistical analyses of ground-truth biophysical simulations to determine what causal inferences can be made through an analysis of spike-timing relationships. Many labs now possess databases of spiking neurons recorded in behaving animals. The tools proposed in this supplement aim to add another layer of understanding to these datasets by providing a mechanistic description of how synaptic interactions could drive observed patterns of neural spiking.