Preexisting Hippocampal Network Dynamics Constrain Optogenetically Induced Place Fields
Neuronal circuits face a fundamental tension between maintaining existing structure and changing to accommodate new information. We tested how neural circuits integrate arbitrary signals by employing novel μLED silicon probes to optogenetically stimulate small groups of hippocampal neurons. New patterns of neural activity were created as mice traversed a chosen segment of a linear track, mimicking the emergence of place fields. Stimulation of principal neurons in CA1, but less so CA3 or the dentate gyrus, induced persistent place field remapping. Novel place fields emerged in both stimulated and non-stimulated neurons, which could be predicted from sporadic firing in the new place field location and the temporal relationship to peer neurons prior to the optogenetic perturbation. Circuit modification was reflected by altered spike transmission between connected pyramidal cells – inhibitory interneuron pairs, which persisted during post-experience sleep. We hypothesize that optogenetic perturbation induced plasticity in recurrent/lateral inhibition that unmasked sub-threshold, pre-existing place fields.
Sam Mckenzie, Assistant Professor,University of New Mexico
Sam McKenzie earned his PhD from Boston University in 2014 working in the laboratory of Howard Eichenbaum and has conducted his postdoc with György Buzsáki at NYU Langone Medical Center Sam has made contributions to the understanding of how hippocampal networks represent a set of related experiences, how receptive fields map around behaviorally meaningful stimuli, and how patterns of neural synchrony change during learning and after optogenetic perturbations. Sam’s work employs cutting-edge μLED silicon probe technology to record from and control neural activity in awake behaving rodents. Starting in the Fall of 2020, Sam will open an independent laboratory at the University of New Mexico to continue investigating how patterns of neural synchrony form, propagate and change with learning. The lab will also work to develop seizure forecasting algorithms and closed-loop stimulation therapeutics to control the spread of seizures in animal models of epilepsy.