Robustness of sensory-evoked excitation is increased by inhibitory inputs to distal apical tuft dendrites

Abstract

Cortical inhibitory interneurons (INs) are subdivided into a variety of morphologically and functionally specialized cell types. How the respective specific properties translate into mechanisms that regulate sensory-evoked responses of pyramidal neurons (PNs) remains unknown. Here, we investigated how INs located in cortical layer 1 (L1) of rat barrel cortex affect whisker-evoked responses of L2 PNs. To do so we combined in vivo electrophysiology and morphological reconstructions with computational modeling. We show that whisker-evoked membrane depolarization in L2 PNs arises from highly specialized spatiotemporal synaptic input patterns. Temporally L1 INs and L2-5 PNs provide near synchronous synaptic input. Spatially synaptic contacts from L1 INs target distal apical tuft dendrites, whereas PNs primarily innervate basal and proximal apical dendrites. Simulations of such constrained synaptic input patterns predicted that inactivation of L1 INs increases trial-to-trial variability of whisker-evoked responses in L2 PNs. The in silico predictions were confirmed in vivo by L1-specific pharmacological manipulations. We present a mechanism-consistent with the theory of distal dendritic shunting-that can regulate the robustness of sensory-evoked responses in PNs without affecting response amplitude or latency.echanistic understanding of the principles that underlie sensory-evoked neuronal responses remains a key challenge in neuroscience research. Although electrophysiological and optical imaging techniques provide access to activity patterns of individual and/or populations of neurons in live animals, information about the organization of the underlying synaptic input patterns that drive neuronal activity remains scarce. Here, we investigate the mechanisms underlying whisker deflection-evoked responses in pyramidal neurons (PNs) in the vibrissal part of rat primary somatosensory cortex (vS1, i.e., barrel cortex) (1). Specifically, we wanted to know whether and how L1 interneurons (INs) shape responses of L2 PNs. L1 is densely populated by apical tuft dendrites from multiple types of excitatory PNs and a sparse population of GABAergic INs (2). Recent studies in acute parasagittal (3) and coronal (4) brain slices in vitro have shown that L1 INs have axonal projections largely confined to L1, where they form synaptic connections with the dendrites from PNs located in L2/3 (5) and L5 (4). These connections place L1 INs in a perfect position to manipulate the activity of PNs, for example, by feed-forward inhibition and/or more indirect mechanisms such as disinhibition (4, 6). However, the influence of L1 INs on the sensory-evoked responses of PNs remains unclear.To address this, we performed whole-cell patch-clamp recordings in vivo and reconstructed the 3D morphologies of the recorded L1 INs. These data, acquired under the same experimental conditions as previously used to determine whisker-evoked spiking and 3D morphologies for PN cell types (7), were used to inform and constrain simulation experiments. Specifically, we...

All-optical electrophysiology reveals excitation, inhibition, and neuromodulation in cortical layer 1

“…S5). Consistent with prior reports of in vivo patch clamp measurements in L1 interneurons (27,28), the firing patterns did not clearly resolve into distinct sub-classes. We therefore treated all measured neurons as a single population.…”

“…Prior studies have characterized the anatomical and electrophysiological properties of L1 35 interneurons in detail (2,5,14,32), and modeling efforts have led to anatomically and biophysically detailed simulations (27,33). We sought to develop a numerical model of the L1 microcircuit that incorporated our data and prior information.…”

Reliable sensory processing in mouse visual cortex through inhibitory interactions between Somatostatin and Parvalbumin interneurons

“…This delayed inhibitory input is believed to quench stochastic excitatory inputs by limiting integration to a small window during which reliable spiking can occur 15 . Moreover, chronically blocking inhibition sharply decreases response reliability 16,17 . However, given their computationally diversity 18 In all imaged populations (10 mice, 1101 neurons), EXC activity patterns spanned a range from highly stereotyped and reliable responses to weak and variable activity.…”

Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

“…The location of synapses between connected neurons has been demonstrated to have different patterns depending on the neuronal classes (Thomson and Lamy, 2007;Egger et al, 2015;Narayanan et al, 2015). Although, unfortunately, the available information is sparse, it does delineate trends that may be generalizable, and thus we used these data to implement the rules described below.…”

“…All excitatory-to-excitatory connection avoided the soma and targeted the apical and basal dendrites. For layers 2/3, and 4, the connections were within 200 μm from the soma while for layers 5 and 6, the synapses could form anywhere along the dendrites (Thomson and Lamy, 2007;Egger et al, 2015;Narayanan et al, 2015). Note that the literature sources are mostly measurements from rat somatosensory cortex.…”