4) Detectability of changes of synaptic weights follows same rules and has same limitations as detection of individual synaptic connections .

These recordings provide a link between properties of synaptic connections typically measured in intracellular experiments in vitro, such as PSC amplitude, and estimates of connectivity made from in vivo extracellular recordings, such as inferred functional connectivity and coupling strength.

Here we have neglected these effects in order to simplify our analyses and determine the basic constraints of what can be inferred about simulated synaptic connectivity from spiking of neuronal ensembles.

Statistical inference of synaptic connections of different strength from spike trains.

D) Detectability of synaptic connections from spike trains: Dependence of the log likelihood ratio between Models M1 and M2 on the input amplitude.

Detectability of synaptic connections from spike trains depends strongly on how much data is available.

In typical multi-electrode spike recordings grouping information would not be available, and PSCs at different synaptic connections would have different shapes.

Such signatures are ambiguous as they can arise from network effects other than direct synaptic connections [66].

Although some investigators have interpreted such correlations as indicators of (chemical or electrical) synaptic connectivity , most used them as more general indicators of functional connectivity without relating them to underlying mechanisms.

Since neurons form synaptic connections mostly locally and sparsely [78] , we a priori favored solutions with sparse partial correlations.

Coactivation patterns and pairwise connectivity are not mutually exclusive since assemblies arise from patterns of synaptic connectivity .

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

In addition, noise correlations and correlations in spontaneous activity have been hypothesized to reflect aspects of synaptic connectivity [12].

Interest in neural correlations has been sustained by a series of discoveries of their nontrivial relationships to various aspects of circuit organization such as the physical distances between the neurons [13, 14], their synaptic connectivity [15], stimulus response similarity [3—5, 15—22] , cell types [23], cortical layer specificity [24, 25], progressive changes in development and in learning [26—28], changes due to sensory stimulation and global brain states [21, 29—33].

Indeed, the relationships between patterns of positive and negative connectivities inferred by the estimator resembled the properties of excitatory and inhibitory synaptic connectivities with respect to distance, cortical layers, and feature tuning [23, 78, 93—98].

To further investigate the link between synaptic connectivity and inferred functional connectivity, in future experiments, we will use molecular markers for various cell types with followup multiple whole-cell in vitro recordings [23, 28] to directly compare the inferred functional connectivity graphs to the underlying anatomical circuitry.

In addition, we assumed that the synaptic connections from Background-neurons to output neurons are fixed because they showed little weight change in the simulation (orange lines in Fig 2B).

In the diagram, blue wavy lines represent intrinsic correlation, and arrows are synaptic connections .

In our model, although inhibitory neurons are not directly projected from input sources, as excitatory neurons learn a specific input source (Fig 5D, left panel), inhibitory neurons acquire feature selectivity through Hebbian STDP at synaptic connections from those excitatory neurons (Fig 5D, middle panel).