Abstract
Dynamics and function of neuronal networks are determined by their synaptic connectivity. Current experimental methods to analyze synaptic network structure on the cellular level, however, cover only small fractions of functional neuronal circuits, typically without a simultaneous record of neuronal spiking activity. Here we present a method for the reconstruction of large recurrent neuronal networks from thousands of parallel spike train recordings. We employ maximum likelihood estimation of a generalized linear model of the spiking activity in continuous time. For this model the point process likelihood is concave, such that a global optimum of the parameters can be obtained by gradient ascent. Previous methods, including those of the same class, did not allow recurrent networks of that order of magnitude to be reconstructed due to prohibitive computational cost and numerical instabilities. We describe a minimal model that is optimized for large networks and an efficient scheme for its parallelized numerical optimization on generic computing clusters. For a simulated balanced random network of 1000 neurons, synaptic connectivity is recovered with a misclassification error rate of less than 1 % under ideal conditions. We show that the error rate remains low in a series of example cases under progressively less ideal conditions. Finally, we successfully reconstruct the connectivity of a hidden synfire chain that is embedded in a random network, which requires clustering of the network connectivity to reveal the synfire groups. Our results demonstrate how synaptic connectivity could potentially be inferred from large-scale parallel spike train recordings.
Highlights
The synaptic organization of neuronal networks is key to understanding the dynamics of brain circuits, and, eventually, to link them to higher level cognitive functions
All simulations presented were carried out with the NEural Simulation Tool (NEST) (Gewaltig and Diesmann 2007) and reconstructions were performed using the CPU implementation of the maximum likelihood estimation (MLE) optimizer as described
By stimulating the synfire chain in a simulation of the estimated model, and comparing resulting dynamics to the output of the original network, we highlight the generative aspect of generalized linear models (GLM) network models
Summary
The synaptic organization of neuronal networks is key to understanding the dynamics of brain circuits, and, eventually, to link them to higher level cognitive functions. The connectivity of neuronal networks can be inferred from parallel recordings of their spiking activity This enables the recovery of the connections in circuits of hundreds and thousands of cells. Recent technical achievements in conducting large-scale parallel recordings of neuronal dynamics, such as multi-electrode array technology for in vivo implantation (Hatsopoulos and Donoghue 2009; Ghane-Motlagh and Sawan 2013), micro-electrode dishes for recording the in vitro activity of acute brain slices and dissociated cell cultures (Nam and Wheeler 2011; Spira and Hai 2013), and optical imaging techniques (Grewe and Helmchen 2009; Lutcke et al 2013; Ahrens et al 2013), make this path even more compelling
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