Because of its superior performance, this ‘sparse+latent’ estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix.

We propose that the most efficient among many estimators provides a more informative picture of the functional connectivity than previous analyses of neural correlations.

As neural recordings become increasingly dense, partial correlations may prove useful as indicators of conditional independence (lack of functional connectivity ) between pairs of neurons.

Functional connectivity as a network of pairwise interactions

Functional connectivity is often represented as a graph of pairwise interactions.

Functional connectivity is a statistical description of observed multineuronal activity patterns not reducible to the response properties of the individual cells.

Functional connectivity reflects local synaptic connections, shared inputs from other regions, and endogenous network activity.

Although functional connectivity is a phenomenological description without a strict mechanistic interpretation, it can be used to generate hypotheses about the anatomical architecture of the neural circuit and to test hypotheses about the processing of information at the population level.

Ising models have been used to infer functional connectivity from neuronal spike trains [56].

One way around these difficulties may be to use large-scale extracellular recording of spike trains and apply statistical methods to model and infer functional connections between neurons.

However, the interpretation of functional connectivity is often approximate, since only a small fraction of pre-synaptic inputs are typically observed.

Here we use in Vitro current injection in layer 2/3 pyramidal neurons to validate methods for inferring functional connectivity in a setting where input to the neuron is controlled.

By modeling how spikes from one neuron, statistically, affect the spiking of another neuron, statistical inference methods can reveal “functional” connections between neurons.

We study how well functional connectivity methods are able to reconstruct the simulated inputs, and assess the validity and limitations of functional connectivity inference.

However, it has proven difficult to relate functional connectivity reconstructed from spikes to the known anatomy and physiology of cortical connectivity [26,32—34].

Sparse sampling of neurons and large electrode spacing may contribute somewhat to the difficulty in interpreting the results of functional connectivity analyses of cortical circuits, but it is also unclear what information these inference methods can provide about actual synaptic inputs and what limitations there are to the use of these methods in general.

Here we examine to what extent the functional connections estimated from spike trains correspond to simulated synaptic processes in a highly controlled setting.

Here we examine the relationship between simulated synaptic input and functional connections estimated from spikes using in vitro current injection experiments.