Connection-type-specific biases make uniform random network models consistent with cortical recordings.

Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a study of triplet connectivity statistics and a data set measuring neuronal responses to channelrhodopsin stimuli, to evaluate the fidelity of thousands of model networks. Network architectures comprised three neuron types (excitatory, fast spiking, and nonfast spiking inhibitory) and were created from a set of rules that govern the statistics of the resulting connection types.

Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex.

Synaptic interactions between nearby excitatory and inhibitory neurons in the neocortex are thought to play fundamental roles in sensory processing. Here, we have combined optogenetic stimulation, whole cell recordings, and computational modeling to define key functional microcircuits within layer 2/3 of mouse primary somatosensory barrel cortex. In vitro optogenetic stimulation of excitatory layer 2/3 neurons expressing channelrhodopsin-2 evoked a rapid sequence of excitation followed by inhibition.

Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms.

Cortical information processing originates from the exchange of action potentials between many cell types. To capture the essence of these interactions, it is of critical importance to build mathematical models that reflect the characteristic features of spike generation in individual neurons. We propose a framework to automatically extract such features from current-clamp experiments, in particular the passive properties of a neuron (i.

In vivo optogenetic stimulation of neocortical excitatory neurons drives brain-state-dependent inhibition.

Background: Synaptic interactions between excitatory and inhibitory neocortical neurons are important for mammalian sensory perception. Synaptic transmission between identified neurons within neocortical microcircuits has mainly been studied in brain slice preparations in vitro. Here, we investigate brain-state-dependent neocortical synaptic interactions in vivo by combining the specificity of optogenetic stimulation with the precision of whole-cell recordings from postsynaptic excitatory glutamatergic neurons and GFP-labeled inhibitory GABAergic neurons targeted through two-photon microscopy.

Membrane potential dynamics of GABAergic neurons in the barrel cortex of behaving mice.

Computations in cortical circuits are mediated by synaptic interactions between excitatory and inhibitory neurons, and yet we know little about their activity in awake animals. Here, through single and dual whole-cell recordings combined with two-photon microscopy in the barrel cortex of behaving mice, we directly compare the synaptically driven membrane potential dynamics of inhibitory and excitatory layer 2/3 neurons. We find that inhibitory neurons depolarize synchronously with excitatory neurons, but they are much more active with differential contributions of two classes of inhibitory neurons during different brain states.