The Hai lab introduces a new concept for reverse engineering silent neuronal networks. By using a supervised learning algorithm combined with stimulation they quantified the performance of the algorithm and the precision of deriving synaptic weights in inhibitory and excitatory subpopulations. And then showed that stimulation enables deciphering connectivity of heterogeneous circuits fed with real electrode array recordings, which could extend in the future to deciphering connectivity in broad biological and artificial neural networks.
Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. The Hai lab has created a protocol for deriving connectivity of simulated silent neuronal networks using stimulation combined with a supervised learning algorithm, which enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell levels with high accuracy. This method used recordings fed through a circuit of heterogeneously connected leaky integrate-and-fire neurons firing at typical lognormal distributions and demonstrate improved performance during stimulation for multiple subpopulations. These testable predictions about the number and protocol of the required stimulations are expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.Read the full article at: https://journals.physiology.org/doi/full/10.1152/jn.00100.2023