Calcium-permeable AMPA receptors govern PV neuron feature selectivity – Nature

Mice and marmosets

All procedures were approved by the Johns Hopkins Animal Care and Use Committee and conducted per the guidelines of the National Institutes of Health and the Society for Neuroscience. Hippocampal imaging experiments were carried out according to German national and institutional guidelines and approved by the ‘Tierversuchskommission’ of the Regierungspräsidium Freiburg (license number G16/037). Marmoset post-mortem tissue was obtained from terminal experiments approved by NIH Institutional Animal Care and Use Committees. The following mouse lines were used: PV-Cre³⁰ (Jackson Laboratory (JAX), 008069), lsl-eGFP⁵¹ (JAX, 010701), lsl-eGFP-GluA2 (Extended Data Fig. 5), GluA2 KO³⁹ (JAX, 002913), and GluA1 KO⁵² (JAX, 024422). We generated the ROSA26-lsl-eGFP-GluA2 mouse line by electroporating mouse embryonic stem (ES) cells with an engineered construct containing ROSA26-CAG-loxP-STOP-loxP-eGFP-Gria2-WPRE (adapted from targeting vector used to generate Ai14 mice⁵³) and homologous recombination (Extended Data Fig. 5). We generated PV-Cre;lsl-eGFP-GluA2 (and PV-Cre;lsl-eGFP) mice from crosses with PV-Cre mice, born at Mendelian ratios. GluA2^–/– pups displayed lower body weight compared with wild-type littermates. They displayed occasional mortality, mitigated by separating the littermates from the parents to reduce litter sizes³⁹. All lines were maintained on a mixed background composed primarily of C57BL/6J, and mice of both sexes were used for experiments. We maintained all animals on a 12-h light–dark cycle at 20–26 °C and 30–70% relative humidity.

Constructs

We used Q/R and R/G RNA-edited flip-isoform short c-tail rat Gria2 cDNA sequences for mutant animal generation and viruses unless otherwise stated. SEP-GluA2 and GFP-GluA2 fusion constructs were generated by amino-terminal insertion of SEP or GFP at four amino acids after the signal peptide padded with linker sequences, as in previously published constructs⁵⁴. We generated the FUW-Cre construct by replacing the eGFP in FUGW with the Cre recombinase gene.

pAAV.Syn.Flex.NES-jRGECO1a.WPRE.SV40 (ref. ⁵⁵) was a gift from D. Kim and the GENIE Project (Addgene, plasmid 100853). The loxP/lox2272 sequences in the Flex cassette were inverted or exchanged with lox511/loxFAS to mitigate compatibility with other DIO AAVs. pAAV-CW3SL-eGFP⁵⁶ was a gift from B.-K. Kaang (Addgene, plasmid 61463).

To deliver large genes, such as the SEP-GluA2 fusion gene, with the high tropism and low cytotoxicity provided by AAV vectors, we heavily optimized vector components to allow larger transgene size. Using the short hSyn1 promoter (469 bp), abbreviated linker sequences and DIO sequences and an optimized WPRE+polyA signal (CW3SL, 425 bp)⁵⁶, we generated a pan-neuronal Cre-dependent AAV expression vector with a minimal backbone (1,350 bp from inverted terminal repeat (ITR) to ITR without cargo) and large cargo capacity size (about 3.65 kb; based on an earlier estimation of 5 kb AAV genome size limit⁵⁷; 3.85 kb when Cre dependency is not required). The loxP/lox2272 sites were spaced by a minimal 64 bp (5′ end-to-5′ end) to set the second recombination event distance (128 bp) above 118 bp, at which inefficient recombination has been reported, but at an exact multiple of the helical repeat length (10.6 bp). This repeat length allowed better-aligned loxP sites after DNA looping, thereby maximizing the efficiency of Cre-mediated excision⁵⁸.

As proof of principle, this study showed that SEP-GluA2 (3,378 bp), a large fusion protein previously only expressed through electroporation or lentiviral transfection, can be strongly expressed with this vector both in vitro and in vivo (Extended Data Fig. 7). The DIO-SEP-GluA2Q vector harboured Gria2 cDNA unedited at the Q/R editing site (R607Q)⁵⁹. GluA2 Q/R RNA editing occurs at the pre-mRNA stage and requires a hairpin structure in the adjacent intron, which is absent in this vector. This structure bypasses RNA editing and expression of a calcium-permeable GluA2Q subunit. The DIO-eGFP control virus was similarly generated, replacing SEP-GluA2 with eGFP, for use as a control. These plasmids have been deposited to Addgene for distribution to the scientific community.

AAV was produced by HHMI-Janelia Viral Tools using a PEI triple transfection protocol into AAV293T cells (an ITR-containing plasmid, 2/9 capsid helper from UPenn Vector Core and the E1-deleted pHelper plasmid from Agilent). The cells were grown under serum-free conditions (three 150 mm culture dishes at about 3 × 10⁷ cells per dish for each 100 µl batch), purified by two rounds of CsCl density gradient centrifugation and exchanged into storage buffer (1× PBS, 5% sorbitol and 350 mM NaCl). Virus titres (GC per ml) were determined by qPCR targeting the AAV ITRs.

Stereotaxic cranial surgeries

We used stereotaxic surgery to inject viruses and to implant 4 mm square cranial windows over the left V1. Mice of mixed sex (>6 weeks old) were given carprofen (5 mg kg^–1) or buprenorphine (sustained release; 0.5–1.0 mg kg^–1) and dexamethasone (4 mg kg^–1) for analgesia and were anaesthetized using avertin or isoflurane (1.5–2.5%). We made a craniotomy with a number 11 scalpel blade centred at 2.5 mm lateral and 3.4 mm posterior to bregma.

For AAV injections, viruses were diluted with sterile PBS to 1–5 × 10¹³ GC per ml. We injected the solution at 5–10 sites spanning the posterior central area of the craniotomy (corresponding to the V1) with about 100 nl injections at each site at 250 μm below the dura surface. Injections were made using a bevelled glass pipette and a custom mineral oil-based injection system over 2–4 min. We left the pipette in place for another 2–3 min to allow diffusion and to prevent backflow.

We placed a 4 mm square glass coverslip over the craniotomy and attached a stainless-steel head bar to the skull during surgery to allow rigid head-fixation during imaging. We allowed mice to recover for 1–2 weeks before imaging and handled them extensively to alleviate experiment-related stress.

For hippocampal experiments, virus injections and cortical excavation or window implantation were done in separate surgeries. We made a small craniotomy over the hippocampus and injected 500 nl of AAV into the CA1 (anterior–posterior (AP): −2.0 mm; medial–lateral (ML) 2.0 mm; dorsal–ventral (DV): −1.4 mm). In the same surgical session, we implanted mice with a stainless-steel head plate (25 × 10 × 0.8 mm with an 8 mm central aperture) horizontally. We allowed mice to recover from surgery for at least 5 days before training sessions. We continued postoperative analgesic treatment with carprofen (5 mg kg^–1 body weight) for 3 days after surgery.

Cortical excavation and hippocampal imaging window implantation were performed >10 days after the initial virus injection per published protocols⁴¹. We made a craniotomy (diameter 3 mm) centred at AP −1.5 mm and ML −1.5 mm. Parts of the somatosensory cortex and posterior parietal association cortex were gently aspirated while irrigating with chilled saline. We continued aspiration until the external capsule was exposed. We then gently peeled away the outer part of the external capsule using fine forceps, leaving the inner capsule and the hippocampus undamaged. The imaging window implant consisted of a 3 mm diameter coverslip (CS-3R, Warner Instruments) glued to the bottom of a stainless-steel cannula (3 mm diameter 1.2–1.5 mm height). The window was gradually lowered into the craniotomy using forceps until the glass was in contact with the external capsule. The implant was then affixed to the skull using cyanoacrylate. We allowed mice to recover from window implantation for 2–3 days.

Awake in vivo 2P fluorescence imaging

We performed retinotopic mapping^60,61 to verify the location of the V1 using optimized protocols and software (https://github.com/ingiehong/retinotopy). We conducted awake in vivo 2P imaging with a custom-built, resonant/galvo 2P laser-scanning microscope (Sutter Instrument) controlled by ScanImage (Vidrio Technologies) and light-proofed to allow imaging in ambient light during visual stimulation. The designs for the head-fixed imaging platform and lightproofing apparatus are available online (https://github.com/ingiehong/StackGPS). We imaged neurons in the L2/3 of monocular V1 expressing eGFP or SEP and jRGECO1a using a ×20/1.0 NA water-immersion objective (Zeiss) and a Ti:Sapphire laser (Coherent Chameleon Ultra; Spectra-Physics Insight X3) tuned at 930 nm or 1,040 nm, respectively, with 20–100 mW of power delivered to the back-aperture of the objective.

We corrected the lateral motion of acquired image sequences using a rigid motion correction algorithm (NoRMCorre⁶²). Neuronal somata with calcium transients were segmented using a constrained non-negative matrix factorization algorithm⁶³. The source-separated GCaMP or jRGECO1a signal from each neuron was used to estimate various visual response properties of L2/3 neurons.

Visual stimulation

Visual stimuli were presented on a gamma-corrected 27″ LED monitor placed 22 cm in front of the centre of the eye contralateral to the hemisphere in which imaging was performed. The visual stimuli consisted of full-screen drifting gratings (4 s of duration, sinusoidal, 0.05 cycles per degree, 1 Hz, 100% contrast) following a 4-s iso-luminant grey screen. Six orientation gratings spaced at 30° were presented drifting in both directions orthogonal to the gratings (total of 12 directions) in a pseudo-randomized order to characterize sensory tuning using Psychtoolbox-3 (ref. ⁶⁴) and FocusStack/Stimserver⁶⁵. We used the average response during the 4 s of stimuli across 9–11 presentations to calculate visual responsiveness and orientation and direction selectivity. Visually responsive neurons were defined as cells with significant stimulus-related fluorescence changes (ANOVA across blank and 12 direction periods, P < 0.05)⁶⁶.

The orientation and direction tuning curve was constructed by measuring the mean ΔF/F, averaged over the stimulus period for each grating drifting direction θ, denoted as R(θ). The OSI was calculated for visually responsive units^21,66,67 with slight modifications on previous definitions⁶⁷ to avoid values outside the intended interval ([0 1]) and to accommodate occasional bona fide negative responses^68,69,70. The preferred drifting direction (θ_pref) of the cell was determined as the stimuli that induced the greatest responses, \(R({\theta }_{{\rm{pref}}})\) and \({R(\theta }_{{\rm{oppo}}})\), as a sum where \({\theta }_{{\rm{oppo}}}={\theta }_{{\rm{pref}}+18{0}^{^\circ }}\), \(R({\theta }_{{\rm{pref}}}) > R({\theta }_{{\rm{oppo}}})\). The OSI was defined as follows:

where θ_orth+ = θ_pref+90°, θ_orth– = θ_pref–90°. All response values were subtracted by the most negative R(θ) when negative responses were present (R_corrected), which effectively ensured that the relative dynamic range of responses were reflected in the index for which they would otherwise distort the index (leading to values outside [0 1]), or be clipped (when negative values were discarded). Formally,

Empirically, this modified index correlates tightly with the OSI calculated using the previous definition⁶⁷ of orientation index and OSI, is bounded by [0 1] and accommodates tuning curves that are partially or entirely negative. Notably, the trends and results of statistical comparisons in this work did not change with the choice of index definition. The DSI, global OSI (gOSI) and global DSI (gDSI) were defined as follows:

gOSI and gDSI gave the same conclusions as OSI and DSI (data not shown). Note that \({R}_{{\rm{corrected}}}\left(\theta \right)\) can also be used in gOSI and gDSI, with the same benefits.

Head-fixed navigation and hippocampal imaging

Mice implanted with hippocampal imaging windows were subjected to a custom head-fixed virtual reality environment as previously described⁴¹. It consisted of a spherical treadmill monitored by an optical sensor that translated motion on the treadmill into forward motion through the virtual environment. We adjusted the forward gain so that 4 m of distance travelled along the circumference of the treadmill equalled one full traversal along a simulated linear track displayed on monitors surrounding the mouse. The track consisted of textured walls, floors and other 3D-rendered objects at the sides of the track as visual cues. To motivate consistent behaviour, we administered soy-milk rewards (4 µl) when the animal traversed certain locations that were spread at fixed distances along the track, and animals were trained for 5–10 days until they displayed consistent running behaviour before commencing imaging experiments.

Imaging was performed using a resonant/galvo high-speed laser scanning 2P microscope (Neurolabware) with a frame rate of 30 Hz for bidirectional scanning and a power of 5–20 mW measured at the objective front aperture. The microscope had an electrically tunable, fast z-focusing lens (Optotune, Edmund optics) to switch between z planes within less than a millisecond. Images were acquired through a ×16 objective (Nikon, 0.8 N.A., 3 mm WD). eGFP and jRGECO1a were excited at 930 nm or 1,040 nm, respectively, with a femtosecond-pulsed 2P laser (Mai Tai DeepSee, Spectra-Physics). We scanned 3 imaging planes (about 25 µm z spacing between planes) in rapid alternation so that each plane was sampled at 10 Hz. The planes spanned 300–500 µm in the x/y direction and were placed so that as many labelled neurons as possible were captured. We attached the animal’s head plate to the bottom of an opaque imaging chamber before each experiment to block ambient light from the photodetectors. We fixed the chamber in the behavioural apparatus with the animal. A ring of black foam rubber between the imaging chamber and the microscope objective blocked any remaining stray light.

Spatial tuning analysis

We motion-corrected all imaging data line-by-line⁷¹ with a 2D hidden Markov model using the software package SIMA⁷¹ or with block-wise non-rigid registration through the software package Suite2P⁷². If no suitable motion correction could be achieved, we discarded the data. To segment interneuron somata, regions of interest (ROIs) were manually drawn using ImageJ (NIH) or automatically drawn by applying Suite2P⁷². For automated ROI settings, the experimenter subsequently inspected individual ROIs. The average jRGECO1a signal over time was then obtained from each ROI for all runs. We restricted our analysis to mouse running periods with a minimum speed of 5 cm s⁻¹. To obtain baseline-normalized ΔF/F calcium traces, we examined the fluorescence value distribution of the jRGECO1a signal and subtracted and divided the entire trace by the eighth percentile value of this distribution⁷³. In rare instances, individual datapoints were below zero after baseline subtraction, and we set these negative values to zero for further calculations.

To compute spatial vector tuning, we plotted the mean activity (ΔF/F) of each spatial bin at its respective angle from the start position on the circular track into a polar coordinate system (Fig. 4e and Extended Data Fig. 15c). We then computed the circular mean of this distribution to obtain the mean tuning vector length and angle of the cell. Spatial coherence (Fig. 4f) was determined as the correlation (Pearson’s R) between the mean fluorescence value in each 5-cm bin on the track and its two nearest neighbours, measuring the local smoothness of the spatial tuning curve⁷⁴. To calculate spatial information (SI; Extended Data Fig. 15e), we computed the average calcium activity (mean ΔF/F) for each 5-cm-wide bin along the linear track to approximate the average firing rate of neurons in that location. SI was then calculated for each cell as \({\rm{SI}}=(\,{\sum }_{i=1}^{N}{\lambda }_{i}{\log }_{2}\frac{{\lambda }_{i}}{\lambda }{p}_{i})\) / λ, where λ_i and p_i are the average calcium activity and fraction of time spent in the ith bin, respectively, λ is the overall calcium activity averaged over the entire linear track, and N is the number of bins on the track. Given the distribution of the underlying values, we plotted the log₁₀ of SI values and compared them statistically (Extended Data Fig. 15e).

To assess the stability of the spatial representation of a cell within a session, we divided the track into 5-cm bins and calculated the mean ΔF/F value for each bin while the animal was moving on the track with a speed >5 cm s^–1 to obtain activity maps for each individual cell. This mapping was done separately for the first and second half of the recording session. We then computed the within-session stability as the cross-correlation between the mean activity maps of the first and second half of the session (Extended Data Fig. 15b,f). We also computed population vector correlations as a function of position in the first and second half of the recording (Extended Data Fig. 15g) to visualize the local similarity of population activity across time. Before computing these correlations, we re-normalized the map of each neuron by subtracting the mean over space and dividing by the standard deviation (z scoring) to mitigate the potential effects of mean rate differences between cells on assessing local population vector similarity.

Quantification of Gria2 mRNA A-to-I editing rates

We mapped the raw sequencing reads from a mouse brain scRNA-seq dataset (n = 1,679)¹⁴ to the mouse reference genome (GRCm38) with a gene annotation, GENCODE (v.M16)⁷⁵, using STAR⁷⁶. The uniquely mapped reads whose sequencing qualities (Phred score) were greater than 20 were counted for the QR and RG RNA-editing sites in Gria2. We filtered out samples if the proportions of the sequencing read with A or G alleles together accounted for less than 95% to avoid potential sequencing errors. We defined the RNA-editing rate for a given site as a ratio of the number of sequencing reads showing G relative to the number of reads with either A or G.

FACS-assisted RNA-seq of PV interneurons

To assess transcriptional changes specifically in PV interneurons after removing CP-AMPARs with RNA-seq, we sorted dissociated cortical PV interneurons by their GFP fluorescence using FACS. Dissociation of adult mouse brain neurons leads to a rapid decimation of viable PV interneurons^77,78,79, which potentially biases downstream analyses to a select subpopulation of PV interneurons. Various proposed methods to mitigate PV interneuron loss failed to recover them at native cell frequencies in adult mice⁸⁰. Several fixation-based FACS approaches have been proposed to target immune cells and neurons, but crosslinking leads to lower RNA yield for RNA-seq.

We developed and used a brain-slice optimized workflow, FICSR-seq (Extended Data Fig. 11a), which recovers PV interneurons vulnerable to dissociation at native cell frequencies. We cut brain slices from adult mice (113.1 ± 11.6 days old) in NMDG cutting solution + trehalose⁷⁷ and diced them into small pieces <1 mm³. Extracellular proteins were digested with pronase (2 mg ml^–1; 8 U µl^–1) at 34–37 °C, after which the slice pieces were fixed in 4% paraformaldehyde (PFA) in PBS (with 0.1 U ml^–1 RNase inhibitor, Promega) for 15 min and dissociated into single cells through careful trituration. We filtered the single cells through a 40-μm filter, labelled them with the cell-permeable nuclear dye DRAQ5 (1:1,000 dilution) to identify nuclei-containing cells and then subjected them to FACS. DRAQ5⁺GFP⁺ or DRAQ5⁺GFP^– cells were sorted, and more than 20,000 cells were collected per mouse cortex to provide extensive coverage of low-expressing PV interneuron transcripts.

We treated the fixed cells with proteinase K before RNA extraction (RecoverAll Total Nucleic Acid Isolation kit for FFPE, Thermo Fisher Scientific) to liberate RNA from protein–protein and protein–nucleic acid crosslinks generated by fixation. We prepared cDNA libraries from GFP⁺ and GFP^– samples (NEBNext Ultra RNA Library Prep kit for Illumina, NEB) from RNA enriched with mRNA through bead-based polyA selection. cDNA libraries were barcoded and sequenced together on an Illumina Hiseq 2500 sequencer, generating 150-bp paired-end reads. We processed RNA-seq reads with bcbio-nextgen (v.1.2.3; https://doi.org/10.5281/zenodo.3564938)⁸¹, aligning to GRCm38 with the STAR aligner⁷⁶ and quantifying counts per gene with Sailfish⁸² using the Ensembl annotation. We used DESeq2 (ref. ⁸³) to analyse differential expression.

Brain slice preparation and whole-cell patch-clamp recordings

To test post-critical period electrophysiological properties and to maintain consistency within experiments, we used mice of either sex, aged postnatal day 32 (P32)–P62 for studies of synaptic properties and aged P69–P77 for studies of intrinsic properties. We first anaesthetized mice of either sex using isoflurane. We rapidly removed their brains in an ice-cold sucrose solution containing the following (in mM): 76 NaCl, 25 NaHCO₃, 25 glucose, 75 sucrose, 2.5 KCl, 1.25 NaH₂PO₄, 0.5 CaCl₂ and 7 MgSO₄, pH 7.3, 315 mOsm. We hemisected the brain along the midline and mounted one or both hemispheres on a 30° ramp. We then sectioned acute parasagittal slices of the visual cortex, 300-μm thick, in the same ice-cold sucrose-cutting solution using a vibratome (VT-1200s, Leica). Slices were incubated in warm (32–35 °C) sucrose solution for 30 min and then transferred to warm (32–35 °C) artificial cerebrospinal fluid (aCSF) composed of the following (in mM): 125 NaCl, 26 NaHCO₃, 2.5 KCl, 1.25 NaH₂PO₄, 1 MgSO₄, 20 d-(+)-glucose, 2 CaCl₂, 0.4 ascorbic acid, 2 pyruvic acid and 4 l-lactic acid, pH 7.3, 315 mOsm. Slices were then allowed to cool to room temperature. For rectification measurements, we cut coronal slices with a NMDG-based cutting solution and incubated them for >15 min. Then we transferred them to aCSF (see the section ‘Analysis of AMPAR rectification’). All solutions were continuously equilibrated with 95% O₂ and 5% CO₂.

We transferred slices to a submersion chamber on an upright microscope (Zeiss AxioExaminer; ×40 objective, 1.0 NA) and continuously superfused (2–4 ml min^–1) them with warm (about 32–34 °C) oxygenated aCSF. We visualized neurons with a CCD camera (Sensicam QE, Cooke) using either infrared differential interference contrast (IR-DIC) microscopy or epifluorescence. The visual cortex was identified based on the relative position of the cortex and hippocampus and the anatomical borderline between the visual cortex and retrosplenial dysgranular cortex. We selected slices in which the apical dendrites of infragranular pyramidal neurons ran parallel to the plane of the slice up through L2/3 in the area targeted for recording. PV interneurons were targeted for recording based on eGFP or SEP-GluA2 expression along with unlabelled L2/3 pyramidal neurons. We filled patch pipettes (2–4 MΩ) pulled (P-97, Sutter Instrument) from borosilicate capillary glass (Sutter Instrument) with an internal solution containing (in mM): 2.7 KCl, 120 KMeSO₃, 9 HEPES, 0.18 EGTA, 4 ATP magnesium salt, 0.3 GTP sodium salt and 20 phosphocreatine disodium salt, adjusted to pH 7.3, 295 mOsm. For recordings of PV interneurons, the internal solution included 0.25% w/v biocytin. Whole-cell patch-clamp recordings were obtained using Multiclamp 700B amplifiers (Molecular Devices) and digitized using an Instrutech ITC-18 (HEKA) and software written in Igor Pro (Wavemetrics). All signals were low-pass filtered at 10 kHz and sampled at 20–100 kHz. Neurons with an access resistance >30 MΩ or a resting membrane potential greater than −60 mV were not used for further recordings or analyses. The access resistance was not compensated in current clamp, and recordings were not corrected for the liquid junction potential.

Analysis of intrinsic excitability, synaptic connectivity and synaptic plasticity

We measured the resting membrane potential (RMP) shortly after establishing the whole-cell current-clamp recording configuration. A 1-s hyperpolarizing current (−100 pA) pulse was used to calculate the input resistance of recorded neurons. To assess the spiking behaviour of the cell, we injected 1-s depolarizing current steps into the recorded neurons. We measured the current–spike frequency relationship with a range of depolarizing current steps presented in pseudorandom order (1-s long, 40-pA increments, 5-s inter-stimulus intervals). Each current intensity was tested three times. For each current intensity, we counted the total number of action potentials exceeding an amplitude of 0 mV generated during each current step, then averaged the values across the three trials. We determined the rheobase by first probing the response of the neuron with 1-s-long depolarizing steps (5-s inter-stimulus intervals) to define a small range of current steps that bounded the rheobase. We then tested the neuron response within this range using 1-s-long depolarizing steps with 1-pA increments. We measured action potential properties from single spikes evoked by rheobase current injections. To compare the current–spike frequency relationship and rheobase between cells from the same baseline, we held cell membrane potentials at −70 mV when injecting depolarizing current steps. We performed all electrophysiological recordings that were assessing the intrinsic properties of PV interneurons in the presence of the following blockers of glutamate and GABA receptors (all from Tocris Bioscience): 5 µM NBQX (AMPA receptor antagonist); 5 µM (RS)-3-(2-carboxypiperazin-4-yl)-propyl-1-phosphonic acid (NMDA receptor antagonist); and 10 µM 6-imino-3-(4-methoxyphenyl)-1(6H)-pyridazinebutanoic acid hydrobromide (SR95531; GABA_A receptor antagonist).

To determine the properties of unitary synaptic connections among neurons, we generated two action potentials in the presynaptic neuron by injecting short, depolarizing current steps (3-ms pulse duration, 20 Hz, 10-s inter-trial interval). We held pyramidal neurons and PV interneurons at approximately −55 mV and −70 mV during synaptic connectivity tests to detect inhibitory postsynaptic potentials (IPSPs) and EPSPs, respectively. We assessed synaptic connectivity (EPSP or IPSP) with an average of 10–50 trials. A synaptic connection was detected if the first response amplitude of the average trace was >3 times the root mean squared of the average trace during baseline conditions and visually verified. We calculated the paired-pulse ratio by dividing the amplitude of the second postsynaptic potential by the first.

We subjected a subset of connected pyramidal→PV pairs, all of which exhibited an average EPSP amplitude of >0.3 mV at baseline, to an anti-Hebbian protocol. After recording 50 traces (6 Hz) as a baseline, we induced synaptic plasticity by pairing 400 presynaptic action potentials delivered at 5 Hz with continuous hyperpolarization of the postsynaptic PV interneuron to –90 mV^25,84. After induction, EPSPs were recorded under the same conditions as the baseline measurement (50 traces in response to presynaptic action potentials, 6 Hz).

Analysis of AMPAR rectification

To measure AMPAR rectification^85,86,87,88, we cut coronal brain slices in ice-cold cutting solution containing (in mM) 96 NMDG, 2.5 KCl, 1.25 NaH₂PO₄, 25 NaHCO₃, 25 d-(+)-glucose, 10 MgSO₄, 0.5 CaCl₂, 96 HCl, 20 HEPES, 12 N-acetylcysteine and 5 sodium l-ascorbate, and oxygenated with carbogen gas (95% O₂ and 5% CO₂). The 300-µm-thick slices were kept in aCSF (125 NaCl, 2.5 KCl, 2 MgCl₂, 2 CaCl₂, 1.0 NaH₂PO₄, 26.2 NaHCO₃ and 11 glucose) and oxygenated with carbogen gas at 23–25 °C until they were transferred for recording to a submerged chamber superfused with aCSF (1–3 ml min^–1) supplemented with about 50 µM picrotoxin and 100 μM APV (2-amino-5-phosphonovaleric acid) to isolate AMPAR-mediated excitatory synaptic transmission.

We made targeted whole-cell recordings of eGFP/SEP-GluA2-positive L2/3 PV interneurons using pipettes of 3–5 MΩ resistance. The intracellular solution contained (in mM): 115 CsMeSO₄, 0.4 EGTA, 5.0 TEA-Cl, 1 QX314, 2.8 NaCl, 20 HEPES, 3.0 ATP magnesium salt, 0.5 GTP sodium salt, 10 phosphocreatine disodium salt and 0.1 spermine and was adjusted to pH 7.2, 285–290 mOsm. When we achieved whole-cell mode, we allowed >5 min for dialysis of the intracellular solution before collecting data. We held cells at −70 mV holding potential and recorded them at room temperature. We left the junction potential (about 11 mV) uncorrected. Signals were measured with a MultiClamp 700B amplifier, digitized using a Digidata 1440A digitizer (Molecular Devices) at 20 kHz and acquired with pClamp 10 software (Molecular Devices). We recorded AMPAR currents at 11 membrane potentials to construct a current–voltage (I–V) plot (V_h = −60 to +60 mV, except for a subset of pyramidal neurons recorded for comparison up to +50 mV). We calculated the rectification index as a weighted ratio of negative (−60 mV) and positive (+60 mV) currents. We compensated for the junction potential (11 mV): rectification index (RI) = (I_{–60 mV}/–71)/(I_{+60 mV}/49). An AMPAR rectification index of 1 represented perfect linearity, whereas values <1 indicate inward rectification. We estimated the reversal potential (E_rev) by cubic polynomial regression that fitted the linear, rectifying and double-rectifying AMPAR I–V curves well.

Immunohistochemistry

We deeply anaesthetized mice with isoflurane then transcardially perfused them with PBS and 4% PFA. We removed and post-fixed the brain in 4% PFA–PBS for >2 h. We sectioned the brain coronally into 25 μm slices using a vibratome (VT-1000, Leica). We acquired marmoset brains post-mortem from terminal experiments and sliced them into 40 µm sections. Free-floating sections underwent antigen retrieval using LAB solution (Polysciences) when necessary and were blocked and permeabilized in 3% BSA with 0.3% Triton X-100 in PBS for 1 h at room temperature. We incubated sections with primary antibodies overnight at 4 °C, washed them with PBS 3 times for 5 min, and then incubated them with secondary antibodies for 2 h at room temperature. After another round of washes, we mounted the slices on glass slides in PermaFluor mounting medium (Thermo Fisher Scientific) and imaged them using a laser scanning confocal microscope (Zeiss LSM880). Controls were carefully carried out, including antibody staining of homozygous knockout mice (Extended Data Fig. 2) to ensure antibody specificity. For GluA1 and GluA2 quantification, ROIs were made around cell somas, and the background signal was subtracted to estimate protein levels.

The following primary antibodies were used: rabbit anti-parvalbumin (1:2,000, PV25, Swant); goat anti-parvalbumin (1:1,000, PVG-213, Swant); rat anti-somatostatin (1:200, MAB354, Chemicon); mouse anti-CaMKIIα (1:1,000, sc-32288, Santa Cruz); rabbit anti-GluA1 (1:1,000, JH4294, generated in-house); mouse anti-GluA2 (1:5,000; clone 15F1, gift from E. Gouaux); chicken anti-GFP (1:1,000, GFP-1020, Aves); and rabbit anti-dsRed2 (1:1,000, 632496, Clontech). The following secondary antibodies were used: Alexa Fluor 405 donkey anti-goat (1:1,000, ab175665, Abcam); Dylight 405 goat anti-mouse IgG2a (1:1,000, 115-477-186 Jackson ImmunoResearch); Alexa Fluor 488 goat anti-mouse IgG2a (1:1,000, A-21131, Thermo Fisher Scientific); Alexa Fluor 488 goat anti-chicken (1:1,000, A-11039, Thermo Fisher Scientific); Alexa Fluor 546 goat anti-rabbit (1:1,000, A-11035, Thermo Fisher Scientific); Alexa Fluor 568 goat anti-mouse IgG1 (1:1,000, A-21124, Thermo Fisher Scientific); Alexa Fluor 568 goat anti-rabbit (1:500, Thermo Fisher Scientific); Texas Red donkey anti-goat (1:1,000, SAB3700332, Millipore Sigma); Alexa Fluor 647 goat anti-rabbit (1:1,000, A-21245, Thermo Fisher Scientific); Alexa Fluor 647 goat anti-mouse IgG2a (1:1,000, A-21241, Thermo Fisher Scientific); Alexa Fluor 647 donkey anti-goat (1:1,000, A-21447, Thermo Fisher Scientific); and Alexa Fluor 647 goat anti-rat (1:500, A-21247, Thermo Fisher Scientific).

Computational modelling

The low feature selectivity of PV neurons^{17,18,19,20,21,89} (but see refs. ^22,90,91,92) and the enhancement in PV-Cre;lsl-eGFP-GluA2 mice could result from several mechanisms. We used computational models to identify which mechanisms are consistent with the observed link between CP-AMPARs and feature selectivity. We examined the impact of three observed electrophysiological circuit changes: (1) increased intrinsic excitability (Extended Data Fig. 10o); (2) the loss of inward-rectifying AMPARs (Extended Data Fig. 7e,f); and (3) enhanced LTD (Extended Data Fig. 10l). Each mechanism was incorporated into a variation of a common base model. This model comprises a single PV neuron receiving excitatory inputs from a set of presynaptic pyramidal neurons with predefined stimulus tuning (Fig. 5a). The output of the PV neuron is a firing rate that is computed as a weighted sum of the inputs. Negative inputs are rectified to ensure a positive firing rate. To endow the PV neuron with stimulus tuning, pyramidal–PV connectivity was modelled as bell-shaped around the preferred orientation of the PV neuron (Fig. 5b), which enabled these neurons to inherit their tuning from pyramidal cells (Fig. 5c). We adjusted the parameters of pyramidal selectivity and connectivity to match the observed PV (and pyramidal) selectivity in the data.

Modelling increased intrinsic excitability

PV interneurons without CP-AMPARs showed increased intrinsic excitability (Extended Data Fig. 10o). PV neuron activation typically requires the coincident activation of multiple excitatory synaptic inputs^93,94. However, the reduced rheobase, and increased RMP and input resistance in PV-Cre;lsl-eGFP-GluA2 mice suggested some strong synapses may reach the activation threshold unilaterally, which may increase selectivity⁹⁵. To test whether this alone could account for the increased stimulus selectivity of PV interneurons, we increased the excitability of the PV model neuron by introducing a positive baseline current to the PV cell, mirroring the empirical shift of the frequency–current (F–I) curve (Extended Data Fig. 16a,b). We discovered that increased excitability reduced stimulus selectivity, contradicting the experimental observation. The response of the PV neuron was increased for all stimuli, thereby reducing the relative magnitude of the preferred response when compared with non-preferred responses (Extended Data Fig. 16c). This held for any rise in intrinsic excitability, regardless of a potential reduction in unitary EPSP amplitude (Extended Data Fig. 10f) when implemented as synaptic scaling (Extended Data Fig. 16d). We also simulated a scenario whereby enhanced intrinsic excitability was adjusted such that it homeostatically maintained the mean rate of the neuron by compensating for a multiplicative decrease in EPSPs (Extended Data Fig. 16e). In this scenario, stimulus selectivity was also reduced (Extended Data Fig. 16f–h). In conclusion, nonselective mechanisms such as increased intrinsic excitability and synaptic downscaling are insufficient to increase stimulus selectivity in the model.

Modelling removal of inward-rectifying AMPAR current

CP-AMPARs are inward-rectifying, which means that their conductance decreases with increasing postsynaptic potential (Extended Data Fig. 7e,f). This implies that they could become less effective for coincident stimuli that induce a strong postsynaptic response. To model this effect, we introduced a dependence of synaptic weights on the postsynaptic potential of the PV interneuron. In this model, we used conductance instead of current-based synapses to allow for a better comparison with experimentally measured current–voltage relationships. We modelled each synaptic weight as the sum of two components (Fig. 5d). The first represents CP-AMPARs and weakens with increasing postsynaptic potential. The second symbolizes other calcium-impermeable AMPARs unaffected by postsynaptic potential (Fig. 5d, dashed line), except due to changes in synaptic drive. We systematically varied the amount of CP-AMPARs relative to calcium-impermeable AMPARs and the membrane potential at which they inactivate (inactivation threshold). The intuition behind CP-AMPARs influencing stimulus selectivity is that they should remain open for weak (that is, non-preferred) stimuli but deactivate for strong (that is, preferred) stimuli. PV neurons fire at high frequencies, which makes this more relevant, and compartmentalized dendritic depolarizations could further exacerbate this effect. This would selectively enhance the response to non-preferred stimuli, thus reducing stimulus selectivity. Conversely, eliminating CP-AMPARs would enhance stimulus selectivity. Indeed, we observed that removing the CP-AMPAR component reduced the response to non-preferred stimuli without affecting preferred stimuli, thereby increasing stimulus selectivity (Fig. 5e,f and compare with Extended Data Fig. 9c and Fig. 2e). This effect was robust to variations in the relative abundance of the CP-AMPARs and their inactivation threshold (Fig. 5g).

A qualitatively similar outcome emerged from applying a previously measured empirical I–V curve from Gria2^–/– mice⁹⁶ to estimate inward rectification (Extended Data Figs. 16 and 7e,f). Systematically varying the proportion of CP-AMPARs in the PV neuron model revealed that orientation selectivity monotonically decreases as the proportion of CP-AMPARs increases (Extended Data Fig. 16f). Two previous papers have examined the potential impact of CP-AMPARs on postsynaptic activation from slightly different perspectives of EPSC kinetics and dendritic summation sublinearity^93,97, and both arrived at conclusions similar to ours. In conclusion, increased stimulus selectivity may be due to the removal of CP-AMPAR-mediated inward rectification.

Modelling increased LTD

Pyramidal–PV connections exhibited exaggerated LTD in PV-Cre;lsl-eGFP-GluA2 mice compared with control mice (Extended Data Fig. 10l). This could enhance selectivity by weakening synaptic inputs from pyramidal cells tuned to non-preferred stimuli. We modelled this scenario by introducing synaptic plasticity in the pyramidal–PV synapses. Synaptic weights changed according to a Bienenstock–Cooper–Munro (BCM) rule, which has been broadly studied as a model for the development of stimulus selectivity⁹⁸. The BCM learning rule is an associative rule that changes synapses when the presynaptic (pyramidal) neuron and the postsynaptic (PV) neuron are simultaneously active. However, the direction of the change is determined by the postsynaptic firing rate. When PV activity is below a threshold, synaptic efficacy decreases. If PV activity surpasses the threshold, synaptic efficacy increases (Fig. 5h). Here we used a fixed instead of the typical activity-dependent threshold in the classical BCM model. This allowed us to test the effect of increased LTD by varying the threshold. Specifically, we increased the LTP–LTD threshold to model the exaggerated LTD in PV-Cre;lsl-eGFP-GluA2 mice (Fig. 5h and Extended Data Fig. 10l). This weakened synapses from pyramidal cells activated for stimuli that elicit only a weak response in the PV cell (Fig. 5i). The exaggerated LTD consequently reduced the PV response to non-preferred stimuli (Fig. 5j) while enhancing its response to preferred stimuli. The resulting boost in selectivity was observable across a wide range of LTD–LTP thresholds as long as the threshold was within the range of PV responses (Fig. 5k). We conclude that increased selectivity could arise from changes in synaptic plasticity if this plasticity, in a BCM-like manner, can generate both potentiation and depression, and if depression is exaggerated after the removal of CP-AMPARs.

Conclusions of modelling studies

These modelling studies demonstrate that the inward-rectifying nature of the CP-AMPAR ion channel and the exaggerated LTD observed in PV-Cre;lsl-eGFP-GluA2 mice can both effectively reduce responses to non-preferred stimuli, thereby accounting for the increases in orientation selectivity. However, neither the rise in intrinsic excitability nor a potential general reduction in excitatory input in PV interneurons due to GluA2 expression can explain the increase in orientation selectivity. These modelling findings imply that acute rectification and cumulative plasticity triggered by resident CP-AMPARs may sufficiently account for their role in maintaining low selectivity. Determining the extent of contribution of these two mechanisms to sensory selectivity in vivo poses a challenging question, which will necessitate rigorous empirical investigation in the future.

Network modelling architecture

The model was a feed-forward rate network of n presynaptic pyramidal neurons and a single postsynaptic PV neuron. We first describe the base model and then its elaborations. The presynaptic pyramidal neurons were tuned to stimulus direction and orientation according to a mixture of von Mises distributions. Specifically, the response of the ith pyramidal cell to a moving grating with direction θ was given by the following:

The proportionality sign indicates a normalization between a minimum of 0 and a maximum of 1 across stimuli. Here θ_i is the preferred direction of the cell, κ determines its tuning width and α controls the strength of direction tuning (κ = 2 and α = 0.5). The preferred directions of the pyramidal cells were equally spaced in the interval [0,2π). The tuning of the PV cell was determined by the pyramidal tuning and the pyramidal-to-PV connectivity. Without loss of generality, we defined the preferred orientation of the PV cell to be 0°. The connectivity from the ith pyramidal cell onto the PV cell was given by a single von Mises distribution:

Weights were normalized across presynaptic cells, such that the minimum and maximum weights were equal to 0 and 1, respectively. The connectivity and pyramidal response together defined the PV voltage and rate using the following equations:

Here, τ = 10 ms denotes the membrane time constant. To simulate the PV activity from these equations, we used forward Euler discretization with a time step Δt = 1 ms. We simulated a time T = 100 ms unless specified otherwise and confirmed that the system had reached its steady state. This steady-state activity was used to compute tuning curves.

Intrinsic excitability

We fitted the change in the empirical I–F curve by numerically finding the shift that minimized the squared difference between the PV-Cre;lsl-eGFP-GluA2 and the PV-Cre;lsl-eGFP mean values. This was done using the minimize_scalar method of SciPy⁹⁹ with the shift as the optimization parameter. In the model, we increased the intrinsic excitability by adding an untuned positive baseline input I₀:

We varied I₀ between 0 and 10. Note that firing rates, membrane potential, weights and currents are unitless in our model. This does not alter the results, because orientation tuning is assessed based on relative rates. Decreases in unitary EPSPs were modelled by downscaling the synaptic weights with a factor p:

We downscaled the weights in two different ways. In Extended Data Fig. 16d, we used p = 0.62, reflecting the mean empirical reduction in EPSPs (Extended Data Fig. 10f). To investigate the effect of homeostatic increases in excitability, we used the minimize_scalar function to find the scaling that would keep the average PV rate constant given a specific increase in its excitability I₀.

Inward rectification

We modelled the inward-rectifying calcium currents by adding a voltage-dependent weight scaling p(u) to the PV dynamics. We also introduced conductance-based synapses to allow for a better comparison with experimental data:

Here, u₀ = 30 is the reversal potential. In our simulations, the precise value of u₀ and the choice for conductance versus current-based synapses scale the postsynaptic responses without strongly affecting relative stimulus tuning in different conditions. The scale p smoothly increases for decreasing voltages:

This is a decreasing sigmoid function between 1 and A, with a slope β and a midpoint M. The midpoint M describes the threshold potential at which the CP-AMPARs deactivate, and β how sensitive the inactivation is to the membrane potential. A quantifies the abundance of rectifying AMPARs relative to the number of non-rectifying AMPARs. We varied A between 0 and 3 and M between 0 and 5; we fixed β to 0.5. The removal of CP-AMPARs was modelled by fixing p to 1. We increased the width of the presynaptic tuning to κ = 3.6 to achieve approximately equal selectivity in the presence of rectification.

In addition to this idealized model of inward rectification, we also simulated a data-driven model. Our starting point were previously measured current–voltage relationships⁹⁶ (Extended Data Fig. 16i). These data were collected in excitatory cells of wild-type and Gria2^–/– mice, which allowed for a direct comparison of calcium permeable (CP) and calcium-impermeable (CI) receptors. Specifically, we used these published measurements⁹⁶ to estimate the normalized conductance at each voltage as the ratio I/V (Extended Data Fig. 16j). We did this for both wild-type and GluA2 traces, and normalized each between 0 and 1. This resulted in scaling factors \({p}_{{\rm{CP}}}\left(u\right)\) and \({p}_{{\rm{CI}}}\left(u\right)\) that represent the strength of CP and CI receptors, respectively, in our model (Extended Data Fig. 16k). Their convex sum determined the total synaptic rectification:

We found that orientation selectivity slowly but monotonically decreased with increasing λ (Extended data Fig. 16l–n). In the data-driven model, neurons with a larger relative abundance of CP receptors therefore have a weaker orientation selectivity, consistent with the idealized model and with our experimental findings.

Plasticity

We modelled synaptic plasticity using a plasticity rule inspired by BCM theory⁹⁸. According to BCM, the change in synaptic efficacy is given by:

Here η = 0.02 is a small learning rate that controls the speed of learning but does not affect the outcome. r_pre and r_post are the presynaptic and postsynaptic rates, respectively, and θ_BCM is the threshold between LTD and LTP. In most applications of the BCM rule, this threshold is adaptive and depends on the recent PV activity. Here we fixed it to a single value per experiment to allow full control over the amount of LTD. Specifically, LTD was implemented by increasing the threshold from 8 to 10 Hz. We further varied the threshold between 6.5 and 11 Hz. As the empirical response distribution seems to be largely unaffected by CP-AMPAR removal, we added synaptic scaling¹⁰⁰ to keep the mean postsynaptic rate constant:

Here r^* is the target mean rate, which we fixed to the mean rate across stimuli before the onset of plasticity. The mean rate \(\bar{r}\) was computed after every weight update by averaging across all stimuli. In the plasticity experiments, we first simulated T = 100 time steps without plasticity to allow the system to reach a steady state. At subsequent time steps, we computed Δw for each individual stimulus, and used the average Δw across stimuli to update the weights. This continued until the weights and rates converged to a new steady state.

Statistical analysis and reproducibility

We performed statistical tests in Matlab (Mathworks), Prism (GraphPad) or R. Data distributions were tested for normality using Shapiro–Wilk test. We used parametric tests if the data were normally distributed and nonparametric otherwise, as detailed in the text describing each comparison. For parametric tests, we used unpaired or paired t-tests and one-way or two-way ANOVA tests with Tukey’s post hoc multiple comparison correction (all two-sided). For data that did not follow normal or log-normal distributions, we used the following statistical tests where appropriate: Mann–Whitney U-test (Wilcoxon rank-sum test), Kruskal–Wallis one-way ANOVA with Dunn’s post hoc multiple comparison correction (all two-sided). For categorical data, we used Fisher’s test or χ² with or without Yates correction according to degrees of freedom and sample size. We report centre and spread values as the mean ± s.e.m. or median ± interquartile range unless otherwise stated. We did not use statistical methods to plan sample sizes, but used sample sizes similar to those frequently used in the field. The text or figure legends include the number of animals and cells. We did not use randomization, and data collection and analyses were not performed blind to the conditions of the experiments unless otherwise stated. P values < 0.05 were considered to be significant. When we used a statistical test, the P value is noted either in the manuscript text or depicted in figures and legends as follows: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, NS, not significant, P ≥ 0.05. Representative examples such as traces and micrographs were chosen from at least three or more independent experiments.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.