Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys.

The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low-/high-rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.

Response nonlinearities in networks of spiking neurons.

Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities are inconsistent with the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the diversity of non-linearities observed in cortical networks.

Inhibition stabilization is a widespread property of cortical networks.

Many cortical network models use recurrent coupling strong enough to require inhibition for stabilization. Yet it has been experimentally unclear whether inhibition-stabilized network (ISN) models describe cortical function well across areas and states. Here, we test several ISN predictions, including the counterintuitive (paradoxical) suppression of inhibitory firing in response to optogenetic inhibitory stimulation. We find clear evidence for ISN operation in mouse visual, somatosensory, and motor cortex. Simple two-population ISN models describe the data well and let us quantify coupling strength. Although some models predict a non-ISN to ISN transition with increasingly strong sensory stimuli, we find ISN effects without sensory stimulation and even during light anesthesia. Additionally, average paradoxical effects result only with transgenic, not viral, opsin expression in parvalbumin (PV)-positive neurons; theory and expression data show this is consistent with ISN operation. Taken together, these results show strong coupling and inhibition stabilization are common features of the cortex.

Finding patterns in cortical responses.

Simulations predict a paradoxical effect that should be revealed by patterned stimulation of the cortex.

Progressive recruitment of distal MEC-4 channels determines touch response strength in C. elegans.

Touch deforms, or strains, the skin beyond the immediate point of contact. The spatiotemporal nature of the touch-induced strain fields depend on the mechanical properties of the skin and the tissues below. Somatosensory neurons that sense touch branch out within the skin and rely on a set of mechano-electrical transduction channels distributed within their dendrites to detect mechanical stimuli. Here, we sought to understand how tissue mechanics shape touch-induced mechanical strain across the skin over time and how individual channels located in different regions of the strain field contribute to the overall touch response. We leveraged Caenorhabditis elegans' touch receptor neurons as a simple model amenable to in vivo whole-cell patch-clamp recording and an integrated experimental-computational approach to dissect the mechanisms underlying the spatial and temporal dynamics we observed. Consistent with the idea that strain is produced at a distance, we show that delivering strong stimuli outside the anatomical extent of the neuron is sufficient to evoke MRCs. The amplitude and kinetics of the MRCs depended on both stimulus displacement and speed. Finally, we found that the main factor responsible for touch sensitivity is the recruitment of progressively more distant channels by stronger stimuli, rather than modulation of channel open probability. This principle may generalize to somatosensory neurons with more complex morphologies.

Somatosensory neurons integrate the geometry of skin deformation and mechanotransduction channels to shape touch sensing.

Touch sensation hinges on force transfer across the skin and activation of mechanosensitive ion channels along the somatosensory neurons that invade the skin. This skin-nerve sensory system demands a quantitative model that spans the application of mechanical loads to channel activation. Unlike prior models of the dynamic responses of touch receptor neurons in Caenorhabditis elegans (Eastwood et al., 2015), which substituted a single effective channel for the ensemble along the TRNs, this study integrates body mechanics and the spatial recruitment of the various channels. We demonstrate that this model captures mechanical properties of the worm's body and accurately reproduces neural responses to simple stimuli. It also captures responses to complex stimuli featuring non-trivial spatial patterns, like extended or multiple contacts that could not be addressed otherwise. We illustrate the importance of these effects with new experiments revealing that skin-neuron composites respond to pre-indentation with increased currents rather than adapting to persistent stimulation.

Tissue mechanics govern the rapidly adapting and symmetrical response to touch.

Interactions with the physical world are deeply rooted in our sense of touch and depend on ensembles of somatosensory neurons that invade and innervate the skin. Somatosensory neurons convert the mechanical energy delivered in each touch into excitatory membrane currents carried by mechanoelectrical transduction (MeT) channels. Pacinian corpuscles in mammals and touch receptor neurons (TRNs) in Caenorhabditis elegans nematodes are embedded in distinctive specialized accessory structures, have low thresholds for activation, and adapt rapidly to the application and removal of mechanical loads. Recently, many of the protein partners that form native MeT channels in these and other somatosensory neurons have been identified. However, the biophysical mechanism of symmetric responses to the onset and offset of mechanical stimulation has eluded understanding for decades. Moreover, it is not known whether applied force or the resulting indentation activate MeT channels. Here, we introduce a system for simultaneously recording membrane current, applied force, and the resulting indentation in living C. elegans (Feedback-controlled Application of mechanical Loads Combined with in vivo Neurophysiology, FALCON) and use it, together with modeling, to study these questions. We show that current amplitude increases with indentation, not force, and that fast stimuli evoke larger currents than slower stimuli producing the same or smaller indentation. A model linking body indentation to MeT channel activation through an embedded viscoelastic element reproduces the experimental findings, predicts that the TRNs function as a band-pass mechanical filter, and provides a general mechanism for symmetrical and rapidly adapting MeT channel activation relevant to somatosensory neurons across phyla and submodalities.