Supercomputer framework for reverse engineering firing patterns of neuron populations to identify their synaptic inputs.

ABSTRACT: In this study, we develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. We tested these techniques in silico to allow rigorous evaluation of their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. We chose spinal motoneurons as our target neural system, since motoneurons process all motor commands and have well-established input-output properties. One set of simulated motoneurons was driven by 300,000+ simulated combinations of excitatory, inhibitory, and neuromodulatory inputs. Our goal was to determine if these firing patterns had sufficient information to allow RE identification of the input combinations. Like other n

SUBMITTER: Chardon MK 

PROVIDER: S-EPMC11483124 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature