1

Supporting Information Cell-type-specific inhibition of the dendritic plateau potential in striatal spiny

projection neurons Kai Du1,2,8, Yu-Wei Wu3,4,8, Robert Lindroos1,2, Yu Liu3,4, Balázs Rózsa5, Gergely Katona6, Jun B. Ding3,4,9**, and Jeanette Hellgren Kotaleski1,2,7,9**

1Stockholm Brain Institute, 2Department of Neuroscience, Karolinska Institute, Sweden, 3Department of Neurosurgery, 4Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA 5Laboratory of 3D functional imaging of neuronal networks and dendritic integration, Institute of Experimental Medicine, Hungarian Academy of Sciences, Hungary, 6MTA-PPKE ITK-NAP Two-photon measurement technology research group, Pázmány Péter University, Budapest, Hungary, 7Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Sweden, 8Co-first author, 9Co-senior author SI Materials and Methods Detailed SPN Model Summary

We have constructed a biophysically detailed and experimentally grounded SPN model. The

model contains 634 compartments. The SPN morphological data were obtained from the public

database “NeuroMorpho” (cell ID: NMO_04520) (1). The morphology parameters were

compensated for additional spine membrane surface (2). Only a few spines were explicitly

modeled and inserted at the locations of interest. The model was equipped with a large array of

experimentally verified ion channels (3) (Table S1-S3). Ion channel kinetics (Table S3) were

slightly modified from our previous published SPN models(4, 5). Channel parameters were tuned

to fit the present experimental conditions (Fig. S1) and previous published data (4, 6). The

calcium buffer was taken from(4, 5). The model contains GABAA (Table S4), AMPA and NMDA

receptors. The model also uses passive membrane parameters: specific axial resistance at 4

ohm*m, specific membrane resistance at 1.8 ohm*m2, reversal potential of leak channels at -70

mV, and membrane capacitance at 0.01 F/m2. All simulations were performed presumably near

physiology temperature as in our experimental conditions (30-31°C). Details related to synaptic

channels and how to design random synaptic input patterns can be found below.

The model is available on ModelDB (http://modeldb.yale.edu/231416).

Reconstructing Detailed Morphology

The SPN morphological data were obtained from the public database “NeuroMorpho” (cell ID:

2

NMO_04520)(1). The model SPN has 15 primary stems, 59 bifurcations and 133 branches with a

total dendritic length of 2,470 µm. The original SPN model contains 2014 compartments (7). To

reduce the computing costs, we merged short compartments without altering the

three-dimensional structure or dendritic lengths (7). Our current model has 634 compartments.

To account for the additional surface area contributed by dendritic spines, theoretical formulas

were introduced to adjust dendritic length and diameter (2):

L = l * F2/3

D = d * F1/3

where “l” and “d” stand for dendritic length and diameters of the original morphology,

respectively; and “L” and “D” are the adjusted values. “F” is an empirical factor obtained by

performing the following simulation. We designed two purely passive models with identical

morphology and intrinsic properties: one was covered by passive spines distributed from a

distance of 30 µm away from the soma to the tip of the dendrites with the average density of 1

spine/µm, while the second model had no dendritic spines. We then adjusted the morphology of

the model without spines using the F-factor so that the behavior of the two models matched each

other. The F-factor that gave the best result was F = 1.38 and this was thus used for all

simulations.

In addition, the model also contains a few explicitly modeled spines, which were only used for

inducing plateau potentials. The explicitly modeled spines were taken from our previous

published models (4, 5). In brief, the explicitly modeled spines contain two cylindrical

compartments: a spine head and a spine neck. The spine neck is purely passive, while the spine

head contains a calcium buffer, R-type and T-type calcium channels (CaV3.3 and CaV3.2), and

NMDA/AMPA receptors. The maximal permeability of the calcium channels in the spine head can

be found in table S2. For NMDA/AMPA channels, gmaxNMDA = 1880 pS, gmaxAMPA = 340 pS.

Ion Channels and Synaptic Conductance

The model contains a large set of ion channels: fast sodium channels (NAf), fast and slow A-type

potassium channels (KAf and KAs), Inward-Rectified potassium channels (Kir2),

Delayed-rectified potassium channels (Kdr), T-type (Cav3.2,Cav3.3), L-type (Cav1.2, Cav1.3) and

R-type (Cav2.3) calcium channels, and calcium-activated potassium channels (SK and BK). Ion

channel kinetics were slightly modified from our previous published SPN models (4, 5). In

particular, the channel conductances, scaling factors (Q-factor that accounts for temperature

3

effects) and distributions were tuned to fit available experimental data (Fig. S1). The calcium

buffer was taken from our previous models (4, 5).

Details regarding channel parameters can be found in the tables below:

Table S1: Maximal conductance of non-calcium channels in the model (S/m2)

Name soma/axon proximal dendrite distal dendrite

NaF 108000 292.5 97.5

NaP 0.4 0.4 0.4

KaF 5785.2 562.5 375

KaS 554 22.9 22.9

Kir 16.8 12.6 12.6

Kdr 21.75 7.25 7.25

Sk 10 10 10

Bk 500 150 150

Table S2: Maximal permeability of calcium channels in the model (m/s)

Name soma/axon proximal dendrite distal dendrite spine

N-type 5.00E-07

R-type 6.50E-07 6.50E-07 6.50E-07 7.80E-07

Cav3.2, T-type 1.76E-09 9.40E-09

Cav3.3, T-type 1.76E-09 2.35E-08

Cav1.2, L-type 8.38E-08 8.38E-08 8.38E-08

Cav1.3, L-type 1.06E-08 1.06E-08 1.06E-08

Table S3: Ion channel kinetics summary

Name Reference Gate Scale factor Tau (ms)

Vhalf (mV)

Slope (mV)

4

Cav3.3 (8) m2 3/2 Table used -78.01 -5.472 h 3 Table used -78.3 6.5

CaV3.2 (8) m 3 Table used -37.9 -6.2 h 3 Table used -59.2 8.8

CaR (9) m3 1 1.7 -8.46 -25.98 h 3

KaS (10) m2 2.8

h 2.8 1/[(α +β)*0.6 +

0.4]

KaF (11) 1

2

NaF (12) 2 2

Kdr (13)

3

Kir (14)

CaN (15 , 16, 17)

CaV1.2 (15 , 16, 18)

CaV1.3 (15 , 18, 19)

SK (20)

NaP (21)

BK (22)

Experimental references for ion channels are included. All missing values in the S3 table are the

same as in our previously published model (4), except the “NaP” and “BK” channels taken

from(5). The rates of open gates for the updated channels are given from the equation: value = 1

/ (1 + exp [ (V – Vhalf) / Slope ] ).

Synaptic Receptors

The model contains GABAA, AMPA and NMDA receptors. These synaptic channels adopted the

classic “dual exponential” function using the “synchan” object in GENESIS (23). The

conductance (Gk) of the GABAA and AMPA receptors were modeled as:

!"(!) = !∗!"#$!!!!! (!!

!!! − !!

!!!) 1.1

where A is the normalized constant, gmax is the maximal conductance, τ1 and τ2 are rise/decay

5

time constants, respectively. By varying τ1 and τ2, we were able to model different types of

GABAergic inputs onto SPNs, including collateral inhibitions from neighboring SPNs, somatic

inhibition from fast-spiking interneurons and slow-GABAA inhibition which could originate from

NPY-NGF interneurons, etc.

For the NMDA receptor, the conductance was modeled as the Gk function multiplied by a factor

( fMg_block ) representing a voltage dependent blocking of the core by Magnesium (Mg).

GkNMDA = Gk* fMg_block 1.2

The blocking effect of Mg was implemented using the “Mg_block” object in GENESIS (23), which

follows the equation:

fMg_block = !

!!ŋ[!"!!]!!!" 1.3

[Mg2+] = 1 (mM), ŋ = 2.992, r = 0.01369 (24)

Details for all synaptic receptors can be found in table S4:

Table S4: synaptic receptor kinetics

Synaptic channel type τ 1 (ms) τ 2 (ms) Erev (mV)

Collateral inhibition (25)

1 10 - 60

Somatic FS inhibition (26)

0.25 3.75 - 60

Slow GABAA Receptor (27)

10 80 - 60

NMDA Receptor (28)

5.63/2 231/2 0

AMPA Receptor (29)

1.9 4.8 0

NMDA/AMPA receptors were placed on both spine heads (for those spines we simulated

explicitly) and dendrites. To fit both dendritic plateaus and spontaneous synaptic activity

measured in experiments, maximal conductance of the excitatory synaptic channels on spine

head were gmaxNMDA = 1880 pS, gmaxAMPA = 340 pS, while the maximal conductance on

dendrites were gmaxNMDA = 705 pS, gmaxAMPA = 255 pS, respectively. The maximal

conductances were tuned to match amplitudes of the recorded uEPSPs and sEPSPs in our

experimental conditions (Fig. S1A,B). The NMDA/AMPA ratio of maximal conductance ranged

6

from 2.5 : 1 to 5 : 1, the amplitude of single EPSP recorded in the soma ranged from ~0.5 mV to

~0.8 mV, the maximal conductance of all unitary GABAergic synapses were set to 1,500 pS,

which is in the range of previously reported experimental data (27, 30).

Modeling Spontaneous Synaptic Activities

The spontaneous activities (‘synaptic noise’ in Fig. 2) include 400 excitatory synapses

(gmaxNMDA = 705 pS, gmaxAMPA = 255 pS) and 100 GABAergic synapses (gmaxGABA = 1,500

pS). The number of excitatory and inhibitory synapses was set at a 4:1 ratio, which is consistent

with published data (31). The input patterns follow a Poisson distribution at 1 Hz for excitatory

synapses and 0.5 Hz for inhibitory synapses. All spontaneous active synapses were randomly

distributed during all simulations. Such spontaneous activities could induce a small

depolarization from the resting membrane potential (-86 mV) of the model. The SPN membrane

potential fluctuated to around -82 to -78 mV. In this case, the model was always pre-run for 500

ms before inducing any plateau potentials.

Modeling High Frequency Excitatory Inputs

In addition to the spontaneous activity, a group of 20 excitatory synapses (gmaxNMDA = 705 pS,

gmaxAMPA = 255 pS) receiving high-frequency random activation were added to the model

(independent Poisson trains, 10 Hz for 200 ms). The high frequency inputs should resemble

specific cortical inputs to SPNs. To avoid the situation that the locations of these excitatory

synapses may significantly bias our conclusions, we generated 1,000 groups of spatial patterns

with the following procedure: We first generated a large sample pool consisting of 100,000

spatial patterns. In each pattern, all synapses were randomly distributed throughout the whole

dendritic tree except for the branch containing the clustered inputs. In this “pool”, the averaged

distance to soma (along the dendritic path) of the 20 synapses for each pattern was calculated

and plotted as histogram of the mean distance. Then we randomly picked 1,000 patterns from

the pool based on the histogram. The “mean distance to soma” of these selected patterns

followed a uniform distribution in their distribution histogram (100 samples per bin, 10 bins). The

selected 1,000 spatial patterns were used for all simulations with high-frequency excitatory

inputs. During each trial, we used distinct spatial and temporal patterns for the excitatory inputs.

Modeling Dendritic and Somatic Inhibitory Inputs

7

When simulating dendritic inhibitory inputs, a group of 20 GABAergic synapses receiving random

input were inserted to the model (independent Poisson trains, 5 Hz for 200 ms) to mimic

inhibitory input patterns. We designed three different types of spatial distributions for these

GABAergic synapses: (1) GABA proximal, (2) GABA distal, and (3) GABA inside input branch

(Fig. S3). In order to generate unbiased distribution patterns, we followed a similar procedure as

when generating high-frequency excitatory inputs: we first generated large pools for each of the

four types of distributions, respectively. The mean distance-to-soma was less than 60 µm for all

samples in the proximal pool, and larger than 60 µm for the distal pool. Then we randomly

picked 1,000 patterns from each pool in the same way as mentioned above. The somatic

inhibitory trains were used to mimic inhibition from FS interneurons (independent Poisson trains

at 30 Hz for 200 ms). They were set to exclusively target perisomatic regions. To capture realistic

synaptic dynamics, short-term depression was also added to the somatic GABAergic synapses

since striatal FS interneurons show this characteristics (30) .

Random Pattern Generation in the Simulations

All Poisson trains were generated using the timetable object in GENESIS. We first exported them

to files, which were later imported by the simulator using the timetable object. Thus, in total, we

created millions of files containing the timing of Poisson trains, in which each Poisson train used

in our simulations was documented and could be back-tracked.

Simulation Platform and Numerical Accuracy

We used GENESIS (version 2.3) (23) as the main simulation platform. We ran GENESIS on a

HP Z820 work station (CPU, Xeon 5675, 3G Hz; RAM, 8 GB) or Parallel GENESIS (23) on a

super-computer (Clay X30, ~4,000 CPUs) at PDC, KTH Royal Institute of Technology for all

simulations. To balance the computational cost and numerical accuracy, we used

“Crank-Nicolson” (second order) method (23) throughout our simulations and checked the

numerical accuracy in our simulation. We found that the precision of running simulations with a

time step of 20-50 µs was almost identical to those of 1 µs. Therefore, we used 20 µs (for

simulation without random inputs) and 50 µs (for simulations with random synaptic inputs) as

“time-step” in our simulations.

Animals and Brain Slice Preparation

8

Adult mice (5-9 weeks, male and female) were used for this study. PV-Cre mice (Jackson

Laboratory, JAX # 008069) and A2A-Cre mice (B6.FVB(Cg)-Tg(Adora2acre)KG139Gsat/Mmucd,

MMRRC stock number 036158-UCD) were used for viral injections and optogenetics

experiments. The mice were bred and maintained according to Stanford University School of

Medicine Animal Research Requirements, and all procedures were approved by Stanford

University's Administrative Panel on Laboratory Animal Care. Oblique horizontal brain slices (300

µm) containing the dorsal striatum were obtained from mice of both gender using standard

techniques (6). Briefly, animals were anesthetized with isoflurane and decapitated. The brain was

exposed, chilled with ice-cold artificial CSF (ACSF) containing 125 mM NaCl, 2.5 mM KCl, 2 mM

CaCl2, 1.25 mM NaH2PO4, 1 mM MgCl2, 25 mM NaHCO3, and 15 mM D-glucose (300-305

mOsm). Then brain slices were prepared with a vibrating microtome (Leica VT1200 S, Germany)

and left to recover in ACSF at 34°C for 30 min and then at room temperature (20–22 °C) for at

least additional 30 min before transferring to recording chamber. The slices were recorded within

5 hours after recovery. All solutions were saturated with 95% O2 and 5% CO2.

Viral Expression of Channelrhodopsin-2 (ChR2)

Stereotaxic injections were performed on P18-P21 PV-Cre or A2a-Cre mice under isoflurane

anesthesia. A total volume of 600-700 nl of concentrated (titer: 1.3e13 GC/ml) virus

(AAV5.EF1.dflox.hChR2(H134R)-mCherry.WPRE.hGH, UPenn Vector Core #AV-5-20297P) (32)

solution was injected unilaterally (right hemisphere) into the dorsolateral striatum (at AP 1.2 mm,

ML 2.0 mm, and DV 3.2 mm from bregma, Fig. S5). Injection was performed using a micropipette

(VWR) pulled with a long narrow tip (size ~10-20 µm) using a micropipette puller (Sutter

Instrument). The glass micropipette was inserted into the brain and left for 6 min before virus was

injected at an infusion rate of 100 nl/min. The pipette was gently withdrawn 6 min after the end of

infusion. Following surgery, the scalp was sutured. Animals were used at 4-6 weeks after AAV

injections. Injection sites were verified in slices fixed after each recording session (Fig. S5A-C).

Electrophysiological Recording

Individual slices were transferred to a recording chamber mounted to an upright microscope

(Olympus BX-51) and was continuously superfused with ACSF at a rate of 2-4 ml/min (30 -

31°C). Striatal SPNs were visually identified under infrared differential interference contrast (DIC)

optics with a water-immersion objective lens (60×, NA = 1.0; Olympus, Japan). SPNs were then

9

further confirmed by their spiny morphological signature with two-photon imaging. Whole-cell

voltage- and current-clamp recordings were obtained with a patch pipette (3–5 MΩ) filled with a

solution containing 135 mM KCH3SO3, 5 mM KCl, 10 mM HEPES, 8 mM Na2-Phosphocreatine,

0.3 mM Na2GTP, 4 mM MgATP, 0.1 mM CaCl2, 1 mM EGTA (pH 7.2-7.3, 285-290 mOsm, pH was

adjusted with KOH). For voltage clamp recording of IPSCs, 2 mM QX-314 Cl was added to the

internal solution to prevent spiking. Series resistance of the cells was measured by injection of

hyperpolarizing pulses (−5 mV, 100 ms). The series resistances were < 20 MΩ. In current clamp

recording mode, bridge balance was applied to compensate series resistance. Resting

membrane potential was adjusted to –75 mV via somatic current injection to facilitate dendritic

plateau induction for all current clamp recordings. Recordings were obtained with a Multiclamp

700B (Molecular Devices, USA). Signal were filtered at 2.2 kHz and digitized at 10 kHz with NI

PCIe-6259 card (National Instruments). The data were recorded with custom-made software

written in Matlab (Mathworks) described previously (33).

Two-Photon Imaging

SPNs were filled with Alexa Fluor 594 (50 µM; Invitrogen, USA) or Alexa Fluor 488 (100 µM) to

reveal the dendritic morphology via whole-cell patch-clamp pipettes for at least 10 min before

imaging. Two-photon imaging was performed with a custom built 2-photon laser-scanning

microscope as described previously (34) equipped with a mode-locked tunable (690–1040 nm)

Ti:sapphire laser Mai Tai eHP DS (Spectra-Physics, USA) tuned to 830, 925, and 760 nm for

imaging Alexa Fluor 594, Alexa Fluor 488, and mCherry, respectively.

Local Electrical Stimulation

Local bipolar stimulation for synaptic release was achieved with an extracellular theta glass

pipette (O.D./I.D. = 1.50/1.17 mm; Sutter, USA) placed adjacent to the dendrites. The pipette had

a tip diameter 2–3 µm and was filled with ACSF containing Alexa Fluor 594 (5 µM) for identifying

the pipette position. Constant voltage (5 to 40 V) stimulation was delivered via an isolated

stimulator (Iso-Flex, AMPI, Israel). Two pulses (0.1 ms) with 10 ms interval were used to induce

dendritic plateau potentials. A successful plateau was defined by peak amplitude of EPSP

reaching supra-linear range with the half-duration greater than 50 ms with 35 V or lower

stimulation intensity (Fig. S1E-G).

10

Optogenetic Stimulation

To stimulate local GABA release from ChR2-expressing axon terminals, 450 nm blue laser light

(0.1-0.5 ms pulse, OptoEngine, UT, USA) was collimated, combined and aligned with lasers for

2-photon imaging and 2PLU before entering scanning mirrors, creating a scannable focal spot

with ~19 µm lateral diameter under the objective. The intensities of the laser powers used for

2PLU (730 nm, 20 - 50 mW) and optogenetic stimulation (450 nm, ~0.2 – 1.4 mW under the

objective) were tuned to make sure the amplitude and waveforms of uEPSPs were comparable

to spontaneous EPSPs (Fig. S1B), and ratios between oIPSCs and uEPSCs amplitudes

resembled those obtained with local electrical stimulation (Fig. 4A and S5F). Typically, oIPSC

amplitudes under this condition were between 100 to 200 pA. Interval between each trial was at

least 30 seconds. Each recording was performed in an individual brain slice.

Two-Photon Glutamate Uncaging and Single-Photon GABA Uncaging

4-methoxy-5,7-dinitroindolinyl-L-glutamate trifluoroacetate (DNI-caged glutamate, 0.7 mM or 5

mM) or 4-methoxy-7-nitroindolinyl-caged L-glutamate trifluoroacetate (MNI-caged glutamate, 5

mM) was freshly dissolved in dark environment in bath solution (ACSF or Mg2+-free ACSF) with

mini-circulated perfusion system (heated to 30 - 31°C). The 95% O2 and 5% CO2 was humidified

before bubbling the recording solution. Two-photon uncaging was carried out using a second

mode-locked tunable (690–1040 nm) Ti:sapphire laser Mai Tai eHP (Spectra-Physics, USA)

tuned to 730 nm. The uncaging power was typically ~20–50 mW under objective and adjusted

for each dendrite to obtain uEPSCs with near physiological amplitudes (Fig. S1A).

Ruthenium-bipyridine-triphenylphosphine-caged GABA (Rubi-GABA, 20 µM) was freshly

dissolved from 5 mM stock solution into the bath solution. A 450 nm blue laser light (5-10 ms

pulse, Opto Engine, UT, USA) was collimated and combined with two-photon laser light path with

a 680 nm long-pass dichroic mirror (Chroma, Bellows Falls, USA) before entering scanning

mirrors. The diameter of the focal spot of the blue laser was measured with a thin fluorescent

filament and was 19 µm. The 830, 730, and 450 nm lasers were aligned on a daily basis before

recording using 0.2 µm fluorescent microspheres (FluoSpheres, Invitrogen, USA). 450 nm laser

power (0.7 – 1.2 mW under the objective) was adjusted in each recording to evoke a uIPSC with

peak amplitude ~80% of the uEPSC evoked by two-photon uncaging of glutamate (Fig. S5F and

S9E). No EPSC was observed by blue light illumination.

11

Dual-color uncaging experiments were performed under 25× objective (NA = 0.95; Olympus,

Japan) to obtain a wider imaging field. To induce plateau potential, 20 spines were selected and

assigned with the 730 nm laser glutamate uncaging locations at their spine heads in a

pseudo-random order with a 0.8 ms pulse duration and 1 ms inter-stimulation interval (ISI). In

Mg2+-free condition, only 10 spines were selected for plateau induction. GABA uncaging was

delivered after the last spine activated by glutamate uncaging with 10 - 20 ms delay from the last

720 nm laser pulse). For testing the GABA effect on the plateau potential, dendritic plateau

potentials were induced by two trials of glutamate uncaging without and with GABA uncaging,

respectively, with a 30 s inter-trial interval.

Data Analysis and Statistical Methods

Data analysis was performed in Matlab (MathWorks, USA), Clampfit (Molecular Devices, USA)

and ImageJ. Statistical analyses were performed using Origin 8.1 (OriginLab, USA) and Prism 7

(GraphPad Software, USA). Summary data are reported as mean ± SEM. Non-matched samples

were analyzed with the nonparametric Mann-Whitney U test. Matched samples were analyzed

with Wilcoxon signed ranks test. Multiple comparisons with repeated measurement was

analyzed with Friedman test followed by post-hoc Dunn's multiple comparisons test. For

comparing the probability of plateau induction across conditions, Fisher's exact test was used. P

< 0.05 was considered statistically significant.

Reagents and Chemicals

All reagents were purchased from Sigma-Aldrich (St. Louis, USA), except Picrotoxin, (+)-MK 801

maleate (MK-801), CGP 55845, Rubi-GABA, and QX-314 Cl (Tocris, Bristol, UK). Alexa Fluor

488 and Alexa Fluor 594 were purchased from Thermo Fisher (USA). MNI-caged glutamate TFA

and DNI-caged glutamate TFA were provided by Femtonics (Budapest, Hungary).

12

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33. Pologruto TA, Sabatini BL, & Svoboda K (2003) ScanImage: flexible software for operating laser scanning microscopes. Biomed Eng Online 2:13.

34. Carter AG & Sabatini BL (2004) State-dependent calcium signaling in dendritic spines of striatal medium spiny neurons. Neuron 44(3):483-493.

Fig. S1. Validation of the SPN model and 2-photon glutamate uncaging, and demonstrationng that local GABAergic inhibition dampensed the generation of dendritic plateaus. (A) Representative single and averaged 2-photon glutamate uncaging-evoked EPSCs (uEPSCs) and spontaneous EPSC (sEPSC). Shaded areas indicate mean value ± SEM The properties of uEPSCs and model EPSCs are comparable with sEPSCs (uEPSC: amplitude = 24.7 ± 2.1 pA, 10-90 rise time = 2.7 ± 0.3 ms, decay tau = 7.3 ± 1.0 ms, n = 19 events/3 cells; sEPSC: amplitude = 23.4 ± 1.8 pA, 10-90 rise time = 2.9 ± 0.3 ms, decay tau = 9.8 ± 1.3 ms, n = 31 events/3 cells). (B) Representative single and averaged traces of 2-photon glutamate uncaging induced EPSP (uEPSP) and spontaneous EPSP (sEPSP). Shaded areas indicate mean value ± SEM The properties of uEPSPs and model EPSPs are comparable with sEPSPs (uEPSP: amplitude = 0.93 ± 0.10 mV, 10-90 rise time = 6.4 ± 0.9 ms, decay tau = 29.0 ± 5.7 ms, n = 11 events/3 cells; sEPSP: ampli-tude = 0.60 ± 0.07 pA, 10-90 rise time = 6.3 ± 0.7 ms, decay tau = 26.2 ± 4.2 ms, n = 31 events/3 cells). (C) Representative traces of current-clamp recordings from SPNs (black: experiment; red: model) in response to current injections (700 ms, 50-pA steps). (D) Summary of F-I curves generated by experiments (n = 11, gray dashed lines) and by a detailed SPN model (red line). (E and F) Examples illustrating success (E) and failure (F) in generation of dendritic plateau potentials when GABAARs were unblocked in brain slices. Upper, representative traces of the depolarization induced by increasing the intensity of local electri-cal stimulation at distal dendrites of SPNs. Lower, the peak amplitude (black) and duration (blue) of EPSPs were plotted against stimulation intensity. The amplitude of the depolarization became supra-linear at certain stimulation intensities (red dashed squares). The duration of EPSPs was increased when a plateau potential was successfully evoked (E), whereas the duration was shortened when failure occurred (F). (G) The success rate for plateau generation is significantly higher when GABAAR was blocked by PTX (50 µM) (upper, control (Ctrl) in ACSF: successful rate = 6.3 %, 1 out of 16 cells; in PTX: successful rate = 58.3 %, 7 out of 12 cells, Fisher Exact test, p = 0.0042). Summary statistics of the mean duration of plateau (lower, ACSF 36.8 ± 1.9 ms, n = 16 cells; in PTX: 55.0 ± 4.0 ms, n = 12 cells, Mann-Whitney, p = 0.0028).

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Fig. S2. Dendritic plateau potentials broadens theed spatiotemporal integration windows for excitatory inputs (A) Left, clustered inputs were activated at different locations within the same dendritic branch (i, ii, and iii). Temporal window for integrating excitatory inputs was broadened only when a dendritic plateau potential was generated at a distal dendrite (iii). Right, summary of simulated firing probability (upper) and averaged spike number (lower) (n = 1,000 trials). (B) Representative histograms of spike number and spike timing with ∆tEXT= 0 ms (upper) and 60 ms (lower). The spiking timing was defined as delay to the activation of the last spine onset timing of spikes relative to the clustered inputs. (C) Left, dynamic current clamp (I-clamp) was placed at the soma and coupled to high frequency inputs. The I-clamp was designed to reproduce an identical somatic depolarization mimicking a plateau potential induced by clustered synaptic inputs. Right, representative traces of somatic depolarization induced by dynamic I-clamp (blue) and clustered inputs (red) without (upper) and with (lower) coupling to high frequency inputs. (D) Summary of firing probability induced by dynamic I-clamp and clustered synaptic inputs coupled with high frequency inputs (n=1,000 trials ). The data suggests somatic depolarization is not sufficient to induce cell-wide integration of excitation. (E) Upper, representative traces of single trials and averaged EPSPs during activation of high frequency excitatory inputs (5 Hz independent Poisson trains for 200 ms). Lower, simulation trial numbers for each mean distance-to-soma bin. Each bin consists of 100 trials, and there are 10 total bins from 30 to 100 µm. (F) Linear regression of the input distance-to-soma vs firing probabilities for different ΔtExt (0 to 100 ms). Upper, represen-tative examples of fitting. Lower, the slopes of the linear regression fit (the spatial profile of integration for excitation) were close to zero for all ΔtExt, suggesting a spatially unbiased integration for excitation.

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Fig. S4. Branch-specific inhibition of spiking(A) The simulation scheme for timing of clustered, high frequency, and inhibitory inputs. In addition to background noise and high frequency inputs, the model SPN was loaded with additional unitary inhibitory inputs. Inhibitory synapses (2 GABAergic synapses, gmaxGABA = 1,500 pS, ECl- = - 60mV) were placed at selected locations: plateau site (on-site), on the same dendrite proximal to plateau site (on-path), off the dendrite that contains the plateau site (off-path), and the soma. For each location, inhibitory synapses were activated with a time delay, ∆tInh. Lower inserts: example voltage traces (n = 20 trials) recorded at the “plateau site” (blue curves) or soma (red curves) when 2 GABAergic synapses were activated. Grey traces represent the plateau potentials that failed to trigger spike. (B) Upper: example simulated somatic voltage traces (20 trials, the arrow indicates GABAAR activation). Lower: Firing probabili-ties (n = 1,000 trials per condition) were plotted as functions of ∆tInh. (C) Firing probabilities for ‘on-site’ inhibition were plotted as functions of ∆tInh at ∆tExt = 0, 30, and 60 ms. (D) Firing probabilities for ‘on-site’ inhibition were plotted as functions of ∆tInh under different ECl-. (E) The simulation procedure was the same as in (A), but GABAergic synapses were replaced with IPSC-like current injections (inset) at selected locations (on-path, on-site, off-path, and soma). (F) Firing probabilities were plotted as a function of current injection timing (∆tI-inj).

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Fig. S5. Combining optogenetics and 2-photon glutamate uncaging(A) Illustration of the virus injection sites. AAV5 DIO-ChR2-mCherry was injected to dorsolateral striatum (DLS) of A2a-Cre and PV-Cre mice. (B-C) Left, epi-fluorescent images of mCherry signal in AAV5 DIO-ChR2-mCherry injected A2a-Cre (B) and PV-Cre (C) mice. Right, confocal images of DLS in A2a-Cre (B) and PV-Cre (C) mice of boxed areas. (D) Spatial profile of optogenetic activation of GABAergic axon terminals (450 nm blue laser, focal spot diameter = ~ 19 μm). Left, 2-photon image of a SPN filled with green fluorescence, Alexa 488 (green) from a brain slice with ChR2-mCherry (red) expression. 450 nm laser locations for optogenetic stimulation are marked with blue circles. Right, IPSCs evoked by blue laser (oIPSCs) at corresponding locations shown in left (blue shaded area i - iii). (E) DNI-caged glutamate (DNI-Glu) at a low working concentration (0.7 mM) preserved 36.4 ± 9 % of oIPSC (n = 5 cells). (F) The ratio of eIPSCs and eEPSCs (I/E ratio) evoked by local electrical stimulation is ~0.8 ± 0.2 (n = 4 cells). (G) Dendritic inhibition of plateau potentials by PV interneurons. Experimental scheme of dendritic inhibition on plateau potential in SPNs by dendritic targeted axon terminals of PV interneurons. 2PLU of DNI-Glu was achieved by 730 nm laser, and the locations of uncaging are indicated with the red circles. Optogenetic activation of GABA release from dendritic targeted axon terminals of PV interneurons was achieved by focal illumination of 450 nm blue laser, and location is indicated with the blue circle. Inset, the representative traces of oIPSCs (blue) of inhibition by PV interneurons and uEPSC (red) for plateau poten-tial induction. (H) 2-photon image of a SPN filled with Alexa Fluor 488 (green), in a brain slice with ChR2-mCherry (red) expression from PV-Cre mice. 720 nm and 450 nm laser locations for 2PLU (arrow head) and optogenetic stimulation (arrow) are marked as red spots and blue circle, respectively. (I) Inhibition induced by optogenetic stimulation was timed to the last 2PLU spot (∆tInh). Representative traces of plateau potential without (black) and with (red) dendritic inhibition by PV interneurons at four different ∆tInh (-10, 10, 30, and 50 ms). (J) Representative traces for ∆Vm aligned with the trace of plateau potential. (K) Summary result of dendritic inhibition of plateau potential by PV interneurons. ∆Vm was significant larger at ∆tInh = 30 ms (∆Vm (∆tInh) = -0.7 ± 0.2 mV (-10 ms), -0.7 ± 0.1 mV (10 ms), -2.2 ± 0.4 mV (30 ms), -0.7 ± 0.2 mV (50 ms); n = 6, P = 0.004, Friedman test followed by Dunn's multiple comparisons). Values are shown as mean ± SEM. *P < 0.05.

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Fig. S8. Local GABAergic inhibition and NMDAR control generation of dendritic plateau potentials (A) Experimental configuration: local stimulation was applied at distal dendrites (> 90 μm from soma, average distance-to-so-ma is 119 ± 5 µm, n = 19). (B) Representative traces of somatic EPSPs evoked by local eStim in Ctrl, PTX (50 µM), and PTX + MK-801 (10 µM). (C) Amplitude and duration of EPSPs was increased in the presence of PTX (amplitude: 134.4 ± 7.8% of Ctrl, n = 9 cells, Wilcoxon Signed Rank, p = 0.0005; half-duration: 157.4 ± 15.8% of Ctrl, n = 9 cells, Wilcoxon Signed Rank, P = 0.0039). (D) Amplitude and duration of dendritic plateaus recorded in PTX were significantly attenuated by NMDAR blocker: MK-801 (amplitude: 71.5 ± 6.8% of PTX, n = 6 cells, Wilcoxon Signed Rank, P = 0.0313; half-duration: 65.3 ± 5.3% of PTX, n = 6 cells, Wilcoxon Signed Rank, p = 0.0313). Values are shown as mean ± SEM. *P < 0.05.

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Fig. S9. Dual-color 2-photon glutamate uncaging and 1-photon GABA uncaging(A) Spatial profile of 1-photon Rubi-GABA uncaging (focal spot diameter = ~19 µm; 450 nm; Rubi-GABA 20 µM). Left, image of a dendrite illustrating 1-photon uncaging locations. Middle, uIPSCs evoked by GABA uncaging at corresponding locations shown in left (blue shaded area). Right, peak amplitude of IPSCs plotted against distance of the uncaging spot to the dendrite. The length constant (λ) is ~ 16 µm. (B) Peak amplitude of uIPSC was not affected by GABAB receptor antagonist, CGP 55845 (CGP, 5 µM) and was abolished by picrotoxin (PTX, 50 µM) (Ctrl vs CGP: P > 0.9999; Ctrl vs PTX: P = 0.0099; N = 5, Friedman test followed by Dunn’s multiple comparisons test). (C) MNI- (left) and DNI- (right) caged glutamate at high concentrations (5 mM) strongly attenuated uIPSC evoked by Rubi-GABA uncaging.(D) DNI-Glu at a low working concentration (0.7 mM) caused ~ 34% inhibition in peak uIPSC (66.0 ± 5.4 %, n = 7 cells). (E) The laser powers used for GABA uncaging were set to mimic the local I/E ratio recorded in (Fig. S5E). The locations for 2-pho-ton glutamate uncaging were indicated as red dots (730 nm; DNI-caged glutamate 0.7 mM) and the location for GABA uncag-ing was indicted as the blue area. (F) Simulation of the effect of GABAAR activation on dendritic plateau potential: plateau potential was induced at a distal dendrite and recorded at the soma. Input location of slow GABA was placed either on the same dendritic branch where the plateau potential was induced (on branch) or on the neighboring dendritic branch (off branch). Right, the amplitude and time courses of uIPSCs were comparable with simulated IPSCs. (G and H) Averaged simulated traces of dendritic plateau potentials induced by clustered inputs with (red) or without (black) dendritic slow GABA inhibition at “on branch” and “off branch” locations in control (G) and in Mg2+-free conditions (H). Values are shown as mean ± SEM. *P < 0.05.