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

HIGHLIGHTS

• What: The authors develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. The authors show that using the "MN Pool Firing Pattern" one is able to reverse engineer back to the MN pool inputs. The summary plots are for the models showing highest R2 results in Table 1. As the authors defined earlier (see section Ensemble modeling for Reverse Engineering) the model explores range of possible combinations of excitatory and inhibitory motor commands from pushpull to balanced, for a motoneuron pool to produce a triangular . . .
  

   

  

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