Title: Post-synaptic spike prediction for a network of temporally binned spike trains
Legend: Predicting in vitro spike activity from microelectrode array recordings (A) Representation of a neural network, with a distribution of inhibitory and excitatory presynaptic cells connected to a single postsynaptic neuron of interest, and neurons with minimal connection to the postsynaptic cell. (B) Filtered MEA recording from representative channels, with 1 ms binary representations and associated ‘binning’ within 20 ms bins (C) True positive and true negative rates of prediction for spike trains with variable time-alignment conditions and binning parameters (D) Matthews correlation coefficient demonstrating improved binary classification over unlearned conditions.
Citation: Vareberg, A. D., Bok, I., Eizadi, J., Ren, X., & Hai, A. (2024). Inference of network connectivity from temporally binned spike trains. Journal of neuroscience methods, 404, 110073. https://doi.org/10.1016/j.jneumeth.2024.110073
Abstract:
Background: Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, methods that leverage limited data to successfully infer synaptic connections, predict activity at single unit resolution, and decipher their effect on whole systems, can uncover critical information about neural processing. Despite the emergence of powerful methods for inferring connectivity, network reconstruction based on temporally subsampled data remains insufficiently unexplored.
New method: We infer synaptic weights by processing firing rates within variable time bins for a heterogeneous feed-forward network of excitatory, inhibitory, and unconnected units. We assess classification and optimize model parameters for postsynaptic spike train reconstruction. We test our method on a physiological network of leaky integrate-and-fire neurons displaying bursting patterns and assess prediction of postsynaptic activity from microelectrode array data.
Results: Results reveal parameters for improved prediction and performance and suggest that lower resolution data and limited access to neurons can be preferred.
Comparison with existing method(s): Recent computational methods demonstrate highly improved reconstruction of connectivity from networks of parallel spike trains by considering spike lag, time-varying firing rates, and other underlying dynamics. However, these methods insufficiently explore temporal subsampling representative of novel data types.
Conclusions: We provide a framework for reverse engineering neural networks from data with limited temporal quality, describing optimal parameters for each bin size, which can be further improved using non-linear methods and applied to more complicated readouts and connectivity distributions in multiple brain circuits.
Keywords: Neural network reconstruction; Synaptic connectivity; Temporal bins.
Investigator: Aviad Hai, PhD
About the Lab: The Hai Lab focuses on engineering minimally invasive tools to access the nervous system for neurobiological studies of brain function.