Estimation of neural network model parameters from local field potentials (LFPs)

Abstract

Most modeling in systems neuroscience has been descriptive where neural representations such as 'receptive fields', have been found by statistically correlating neural activity to sensory input. In the traditional physics approach to modelling, hypotheses are represented by mechanistic models based on the underlying building blocks of the system, and candidate models are validated by comparing with experiments. Until now validation of mechanistic cortical network models has been based on comparison with neuronal spikes, found from the high-frequency part of extracellular electrical potentials. In this computational study we investigated to what extent the low-frequency part of the signal, the local field potential (LFP), can be used to validate and infer properties of mechanistic cortical network models. In particular, we asked the question whether the LFP can be used to accurately estimate synaptic connection weights in the underlying network. We considered the thoroughly analysed Brunel network comprising an excitatory and an inhibitory population of recurrently connected integrate-and-fire (LIF) neurons. This model exhibits a high diversity of spiking network dynamics depending on the values of only three network parameters. The LFP generated by the network was computed using a hybrid scheme where spikes computed from the point-neuron network were replayed on biophysically detailed multicompartmental neurons. We assessed how accurately the three model parameters could be estimated from power spectra of stationary 'background' LFP signals by application of convolutional neural nets (CNNs). All network parameters could be very accurately estimated, suggesting that LFPs indeed can be used for network model validation. Author summaryMost of what we have learned about brain networks in vivo has come from the measurement of spikes (action potentials) recorded by extracellular electrodes. The low-frequency part of these signals, the local field potential (LFP), contains unique information about how dendrites in neuronal populations integrate synaptic inputs, but has so far played a lesser role. To investigate whether the LFP can be used to validate network models, we computed LFP signals for a recurrent network model (the Brunel network) for which the PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi.org/10.

Computation of the electroencephalogram (EEG) from network models of point neurons

“…We then computed a “ground-truth” EEG (referred to simply as “EEG” in the paper), following the hybrid modelling scheme [30, 35, 42, 43], and used this ground-truth EEG to compare the performance of the different proxies. To do so, we created a network of unconnected multicompartment neuron models with realistic morphologies and homogeneous distribution within the circular section of a cylinder of radius r = 0.5 mm (Fig 1 C), which roughly approximates the spatial extension of a layer in a cortical column.…”

“…Synaptic inputs onto neurons that have a closed-field configuration, such as interneurons, largely cancel out when they are superimposed so that the net contribution to the current dipole is weak [35]. The hybrid modelling scheme [30, 35, 42, 43] gives us the opportunity to study, independently from the spiking dynamics of the point-neuron network, how different parameters of the multicompartment neuron network (e.g., distribution of synapses or dendritic morphology) affect the EEG signal and, as a consequence, modify the prediction capabilities of the proxies.…”

“…Our focus is on computing an accurate prediction of the EEG (denoted as "proxy" in The ground-truth EEG (referred to simply as "EEG" in the paper) with which to compare the performance of the different proxies is here computed using the hybrid modelling scheme [30,35,42,43]. We created a network of unconnected multicompartment neuron models with realistic morphologies and distribute them within a cylinder of radius r = 0.5 mm ( Fig C).…”

“…This approach is however computationally cumbersome, and it does not allow an easily tractable and exhaustive analysis of the dynamics of such networks. One alternative could be using a hybrid scheme [30, 35, 42, 43] that projects the spike times generated by the LIF point-neuron network onto morphologically detailed 3D neuron models and then computing the electric field that the currents flowing through these 3D networks generate. This scheme provides a simplification by separating the study of the network dynamics (described by the point-neuron network model) from that of field generation (described by the multicompartment neuron model), but still requires running cumbersome multicompartment model simulations for each simulation of the LIF network.…”

Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions

“…Therefore, we regard our model as a toy-model, which still incorporate key features of real LFPs. A method to efficiently compute LFPs is the so-called kernel method [29,30].…”