Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space

Abstract

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about 1 mm${}^2$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials (LFPs) can be computed from the simulated spiking activity. We here extend a local network and LFP model to an area of 4x4 mm${}^2$, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of LFP signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around 50 Hz may explain the recent report of an apparent band-pass filter effect in the spatial reach of the LFP.

31 pages, 10 figures, 7 tables

References in corpus (20)

• Decorrelation of neural-network activity by inhibitory feedback
• Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
• Frequency dependence of signal power and spatial reach of the local field potential
• Full-density multi-scale account of structure and dynamics of macaque visual cortex
• The correlation structure of local cortical networks intrinsically results from recurrent dynamics
• Hybrid scheme for modeling local field potentials from point-neuron networks
• Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations
• Balanced networks of spiking neurons with spatially dependent recurrent connections
• Identifying anatomical origins of coexisting oscillations in the cortical microcircuit
• Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks
• Connectivity Concepts in Neuronal Network Modeling
• LFP beta amplitude is predictive of mesoscopic spatio-temporal phase patterns
• Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): Comparing multi-electrode recordings from simulated and biological mammalian cortical tissue
• Fundamental activity constraints lead to specific interpretations of the connectome
• Bringing Anatomical Information into Neuronal Network Models
• Conditions for wave trains in spiking neural networks
• Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
• Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse
• Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution
• Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density

Cited by in corpus (2)

• Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
• Boundary solution based on rescaling method: recoup the first and second-order statistics of neuron network dynamics