Division and subtraction by distinct cortical inhibitory networks in vivo

Abstract

Brain circuits process information through specialized neuronal subclasses interacting within a network. Revealing their interplay requires activating specific cells while monitoring others in a functioning circuit. Here we use a new platform for two-way light-based circuit interrogation in visual cortex in vivo to show the computational implications of modulating different subclasses of inhibitory neurons during sensory processing. We find that soma-targeting, parvalbumin-expressing (PV) neurons principally divide responses but preserve stimulus selectivity, whereas dendrite-targeting, somatostatin-expressing (SOM) neurons principally subtract from excitatory responses and sharpen selectivity. Visualized in vivo cell-attached recordings show that division by PV neurons alters response gain, whereas subtraction by SOM neurons shifts response levels. Finally, stimulating identified neurons while scanning many target cells reveals that single PV and SOM neurons functionally impact only specific subsets of neurons in their projection fields. These findings provide direct evidence that inhibitory neuronal subclasses have distinct and complementary roles in cortical computations.

Figure 1. All-optical network dissection of cortical subclasses during visual computations

a, Directed light, for optical recording and stimulation, was targeted to the V1 of an animal being shown visual stimuli. b, A traditional raster scan located cells in the network that were bulk-loaded with the calcium indicator dye OGB. c, Cells that were automatically identified were then imaged at high speed using targeted two-photon scanning along an arbitrary scan path. d, This enabled detection of robust cellular activity in response to episodically presented oriented drifting gratings, and this activity could be analysed as primary signals or deconvolved to estimate action potentials (red lines). e, Evoked optical traces were highly consistent over repeated presentations of visual stimuli. f, The clear, repeatable responses enabled the resolution of well-defined responses (dots), fit by dual Gaussian curves (lines). Data are shown as mean ± s.e.m. g, Image of an mCherry-ChR2+ PV+ (channelrhodopsin-2-and PV-positive) cell targeted in vivo for cell-attached recording. h, Evoked action potentials from an mCherry-ChR2+ PV+ cell. PV+ neurons were activated at the onset (3.9 s) of 4 s of visual stimulation, through 10-Hz stimulation of the PV+ neuron. i, j, Recording of the visual response of a PV− neuron in the control condition (i) and with PV activation (j). k, Left, cell population loaded with OGB dye (top), and responses of a cell marked by an asterisk in the network shown in the top right panel (bottom). Right, cells colour-coded by the magnitude of their visual response in the control condition (top) and when PV+ neurons were activated (bottom; colour bar, ΔF/F %). A ChR2+ PV neuron in the network is circled. +PV, optical PV activation; Ctrl, control; PV−, PV-negative; PV+, PV-positive.

Figure 2. Impact of PV- and SOM-driven inhibition on the tuning of neuronal responses

a, An imaging site showing neurons loaded with calcium indicator (OGB1-AM, green) and two PV+ neurons expressing mCherry-ChR2 (PV-ChR2, red) in visual cortex in vivo. b, Optical responses to visual stimuli, either without (Control) or with (+PV) simultaneous optical PV activation in interleaved trials, recorded during episodically presented oriented drifting gratings (see Fig. 1d). The photo-artefact from ChR2 stimulation has been removed from these trials. c, d, Same as (a and b), but from an experiment with mCherry-ChR2 expression in SOM neurons. e, f, Control tuning curves (black) were suppressed with ChR2 activation of PV neurons (blue) or SOM neurons (pink) in four example cells. g, h, The normalized suppression plotted as a function of control response strength in two example cells (blue, PV; pink, SOM). i, Cumulative density functions of the distributions of suppression versus response slopes for all cells suppressed by PV (blue) and SOM (pink) activation. j, k, Population data showing the amount of PV- and SOM-mediated suppression at different response levels. l, m, Average tuning curves showing control responses and effects of PV (blue) or SOM (pink) activation. n–r, Effects of PV and SOM activation on tuning, including baseline, peak-baseline amplitude, OSI, tuning width and DSI. NS, not significant. †P < 0.10; *P < 0.05; **P < 0.01; ***P < 0.001. Data are shown as mean ± s.e.m.

Figure 3. Electrophysiological analysis of PV-and SOM-driven inhibition

a, PV or SOM neurons were optogenetically activated while recording visual responses (measured in volts (V)) using targeted in vivo cell-attached recordings. b, A pyramidal neuron was patched under two-photon guidance and filled with Alexa 488 dye. c, Representative spikes recorded. d, e, Spike rasters depict responses of target cells in time over 18 directions, pooled over multiple trials in control conditions (black) and during activation of PV (blue) or SOM (pink) neurons. f, g, Control tuning curves (black) were suppressed with ChR2 activation of PV neurons (blue) or SOM neurons (pink) in four example cells with very different response levels. h, Representative examples of the relationship between PV-mediated (blue) and SOM-mediated (pink) suppression and control response strength. i, Bar graph of mean suppression versus response slopes during PV (blue) and SOM (pink) activation. j, k, Population data for the amount of PV- and SOM-mediated suppression seen at different response levels. l, m, Average tuning as in Fig. 2l, m, before (black) and during PV (blue) or SOM (pink) activation. n–r, Effects of PV and SOM activation on tuning-curve parameters, as in Fig. 2n–r. †P < 0.10; *P < 0.05; **P < 0.01; NS, not significant. Data are shown as mean ± s.e.m.

Figure 4. Modulation of response gain by PV and SOM cells during targeted cell-attached recordings

a, Example responses from a neuron stimulated with drifting gratings of increasing contrast, at the cell’s preferred orientation. b, Naka–Rushton curve describing the increase in responses with increasing contrast. c, d, Four example cells whose control response–contrast curves (black) were suppressed by PV (blue) or SOM (pink) activation. C₅₀ values are marked (green circles). e, Relationship between suppression and response when a PV cell was activated (slope = 0.15). f, The mean suppression versus response slope was larger when PV cells were activated than when SOM cells were activated. g, h, Population data for the amount of PV- and SOM-mediated suppression observed at different response levels. i, j, Effects of PV and SOM activation on response–contrast function parameters R_max and C₅₀ (see b). *P < 0.05; **P < 0.01; ***P < 0.001; NS, not significant. Data are shown as mean ± s.e.m.

Figure 5. Dual-laser optical mapping of network connections to reveal maps of functional inhibition by single PV and SOM neurons

a, Two laser systems are controlled independently and synchronized for concurrent high-speed imaging and targeted spatial stimulation of a ChR2-expressing inhibitory neuron. b, A SOM ChR2+ neuron in vivo was patched and recorded while systematically mapping target locations with a focused 473-nm laser; spikes were elicited only when stimulation was targeted on or very near to the soma. The colour bar indicates the spike probability at each location (0–100%). c, All-optical circuit mapping. Optical activation of one neuron (blue spot) during targeted recording of another neuron (red spot). Activity in the recorded neuron was measured with no stimulation (Control, black), full-field activation of PV neurons (blue), targeted activation of the PV neuron (red), and while aiming the stimulation beam 100 μm off-target (grey). Visual responses were suppressed by both full-field and single PV-cell activation. d, Mapping the influence of targeted PV activation on neighbouring cells revealed some cells that were significantly suppressed by PV activation (red, 1–3) which were clearly intermixed with other nearby cells that were not affected (white, 4–6). e, Tuning curves for cells 1–6 comparing cell responses during control (black) or targeted PV-cell activation (blue). f, Spatial distribution of all cells in the network that were either significantly suppressed (red) or not (black). g, Population data for the amount of PV- and SOM-mediated suppression observed at cells’ different response levels across all single cell networks. h–k, Same as d–g, in experiments in which focal stimulation was targeted to single SOM neurons. *P < 0.05; **P < 0.01; NS, not significant. Data are shown as mean ± s.e.m.

Figure 6. Spatial and functional analysis of targeting by single PV and SOM neurons

a, The amount of suppression in significantly suppressed cells is plotted against their distance from the stimulated PV cell (top) or SOM cell (bottom). Best fit coloured lines show individual networks; black line depicts pooled data. b, The orientation preferences of significantly suppressed cells are colour-coded for two example networks (top, PV (blue); bottom, SOM (pink)). c, Distribution of preferred orientations of target cells (black lines) for these example networks, superimposed with the orientation tuning curve of the stimulated PV (blue line, top) or SOM (pink line, bottom) neuron. d, Bar graphs show the mean proportion of neurons that matched the preferred orientation of stimulated PV neurons (blue bars) or SOM neurons (pink bars), and the orthogonal, non-preferred orientation. Grey bars show the same comparison when the preferred orientations were randomly re-sampled among the neurons in the fields of view (Random PO and Random nonPO). For PV but not SOM, the actual percentage of targets at the PO was greater than expected with random targeting, indicating that PV cells may preferentially suppress cells with similar functional response properties. *P < 0.05; **P < 0.01; NS, not significant. Data are shown as mean ± s.e.m.