Complementary networks of cortical somatostatin interneurons enforce layer specific control.

The neocortex is functionally organized into layers. Layer four receives the densest bottom up sensory inputs, while layers 2/3 and 5 receive top down inputs that may convey predictive information. A subset of cortical somatostatin (SST) neurons, the Martinotti cells, gate top down input by inhibiting the apical dendrites of pyramidal cells in layers 2/3 and 5, but it is unknown whether an analogous inhibitory mechanism controls activity in layer 4. Using high precision circuit mapping, in vivo optogenetic perturbations, and single cell transcriptional profiling, we reveal complementary circuits in the mouse barrel cortex involving genetically distinct SST subtypes that specifically and reciprocally interconnect with excitatory cells in different layers: Martinotti cells connect with layers 2/3 and 5, whereas non-Martinotti cells connect with layer 4. By enforcing layer-specific inhibition, these parallel SST subnetworks could independently regulate the balance between bottom up and top down input.

Two-Photon Optogenetic Mapping of Excitatory Synaptic Connectivity and Strength.

The development of optical methods to activate neurons with single-cell resolution has enabled systematic mapping of inhibitory connections. In contrast, optical mapping of excitatory connections between pyramidal neurons (PCs) has been a major challenge due to their high densities in cortical tissue and their weak and stochastic connectivity. Here we present an optogenetic two-photon mapping method in mouse neocortical slices by activating PCs with the red-shifted opsin C1V1 while recording postsynaptic responses in whole-cell configuration. Comparison of delays from triggered action potentials (APs) with those from synaptic inputs allowed us to predict connected PCs in three dimensions. We confirmed these predictions with paired recordings, and used this method to map strong connections among large populations of layer 2/3 PCs. Our method can be used for fast, systematic mapping of synaptic connectivity and weights.

Bayesian methods for event analysis of intracellular currents.

BACKGROUND: Investigation of neural circuit functioning often requires statistical interpretation of events in subthreshold electrophysiological recordings. This problem is non-trivial because recordings may have moderate levels of structured noise and events may have distinct kinetics. In addition, novel experimental designs that combine optical and electrophysiological methods will depend upon statistical tools that combine multimodal data. NEW METHOD: We present a Bayesian approach for inferring the timing, strength, and kinetics of post-synaptic currents (PSCs) from voltage-clamp electrophysiological recordings on a per event basis. The simple generative model for a single voltage-clamp recording flexibly extends to include additional structure to enable experiments designed to probe synaptic connectivity. RESULTS: We validate the approach on simulated and real data. We also demonstrate that extensions of the basic PSC detection algorithm can handle recordings contaminated with optically evoked currents, and we simulate a scenario in which calcium imaging observations, available for a subset of neurons, can be fused with electrophysiological data to achieve higher temporal resolution. COMPARISON WITH EXISTING METHODS: We apply this approach to simulated and real ground truth data to demonstrate its higher sensitivity in detecting small signal-to-noise events and its increased robustness to noise compared to standard methods for detecting PSCs. CONCLUSIONS: The new Bayesian event analysis approach for electrophysiological recordings should allow for better estimation of physiological parameters under more variable conditions and help support new experimental designs for circuit mapping.