Inference of Presynaptic Connectivity from Temporally Blurry Spike Trains by Supervised Learning

Abstract

Reconstruction of neural network connectivity is a central focus of neuroscience. The ability to use neuronal connection information to predict activity at single unit resolution and decipher its effect on whole systems can provide critical information about behavior and cognitive processing. Neuronal sensing modalities come in varying forms, but there is yet to exist a modality that can deliver readouts that sufficiently address the spatiotemporal constraints of biological nervous systems. This necessitates supplementary approaches that rely on mathematical models to mitigate physical limitations and decode network features. Here, we introduce a simple proof-of-concept model that addresses temporal constraints by reconstructing presynaptic connections from temporally blurry data. We use a variation of the perceptron algorithm to process firing rate information at multiple time constraints for a heterogenous feed-forward network of excitatory, inhibitory, and unconnected presynaptic units. We evaluate the performance of the algorithm under these conditions and determine the optimal learning rate, firing rate, and the ability to reconstruct single unit spikes for a given degree of temporal blur. We then test our method on a physiologically relevant configuration by sampling network subpopulations of leaky integrate-and-fire neuronal models displaying bursting firing patterns and find comparable learning rates for optimized reconstruction of network connectivity. Our method provides a recipe for reverse engineering neural networks based on limited data quality that can be extended to more complicated readouts and connectivity distributions relevant to multiple brain circuits.