Homeostatic Synaptic Plasticity Rescues Neural Coding Reliability

To survive, animals must recognize reoccurring stimuli. A key requirement for repeated identification of stimuli is reliable representation by the neural code on each encounter. Synaptic transmission underlies neural codes propagation between brain regions. A hallmark of chemical synapses is their plasticity, which enables signal transfer to be modified in an activity-dependent manner. Despite many decades of intense research on synapses, it remains unclear how the plastic features of synaptic transmission can maintain reliable neural coding. By studying the olfactory system of Drosophila melanogaster, we aimed to obtain a deeper mechanistic understanding of how synaptic function shapes neural coding reliability in the live, behaving animal. We show that the properties of the active zone (AZ), the presynaptic site of neurotransmitter release, are critical for generating a reliable neural code. Reducing neurotransmitter release probability specifically at AZs of olfactory sensory neurons disrupted both neural coding and behavioral reliability. Strikingly, these defects were rescued within a day by target-specific synaptic plasticity, whereby a homeostatic increase in the number of AZs compensated the drop in release probability. These findings demonstrate an important role for synaptic plasticity in maintaining neural coding reliability and are of pathophysiological interest by uncovering an elegant mechanism through which the neural circuitry can counterbalance perturbations.

manner. Despite many decades of intense research on synapses, it remains unclear how the 23 plastic features of synaptic transmission can maintain reliable neural coding. By studying the 24 olfactory system of Drosophila melanogaster, we aimed to obtain a deeper mechanistic 25 understanding of how synaptic function shapes neural coding reliability in the live, behaving 26 animal. We show that the properties of the active zone (AZ), the presynaptic site of 27 neurotransmitter release, are critical for generating a reliable neural code. Reducing 28 neurotransmitter release probability specifically at AZs of olfactory sensory neurons disrupted 29 both neural coding and behavioral reliability. Strikingly, these defects were rescued within a day 30 by target-specific synaptic plasticity, whereby a homeostatic increase in the number of AZs 31 compensated the drop in release probability. These findings demonstrate an important role for 32 synaptic plasticity in maintaining neural coding reliability and are of pathophysiological interest by 33 uncovering an elegant mechanism through which the neural circuitry can counterbalance 34 perturbations. 35

Introduction 40
Animals encounter the same stimuli repeatedly and are able to recognize them over and over 41 again. An essential requirement for the repeated identification of a stimulus is that its 42 representation by the neural code will be reliably reproduced on each occasion. Two major 43 approaches are used to describe neural coding 1 . The first is rate coding, where the firing rate of 44 action potentials over a period of time is employed as the neural code. The second is temporal 45 coding, where the precise timing and the precise pattern of action potentials is utilized for coding. 46 Often, a combination of the two strategies is used as the coin of information for neural coding: the 47 firing rate within a relatively small time window along with changes in firing rate over time. 48

49
Coding of an external stimulus by the nervous system begins at the sensory neurons. The code 50 then undergoes transformations as it passes across synaptic contacts and propagates to higher 51 brain regions. Chemical synaptic transmission is the major mode of fast information transfer 52 between neurons. The response of a postsynaptic neuron depends on neurotransmitter release 53 from synaptic vesicles (SVs) fusing with the plasma membrane at the presynaptic active zone 54 (AZ). SV fusion, in turn, is regulated by the complex interplay of various specialized AZ proteins, 55 which determine neurotransmitter release sites and control release probability 2-4 . The molecular 56 mechanisms of SV fusion are highly dynamic and can be modified on timescales ranging from 57 milliseconds to days. Such presynaptic plasticity involves e.g. changes in Ca 2+ signals, SV 58 availability, and activity-dependent modulations of the release machinery 5-9 . Importantly, 59 synapses are also under homeostatic control. Homeostatic synaptic plasticity describes a 60 phenomenon whereby pre-or postsynaptic adjustments maintain synaptic stability 10-13 . This 61 evolutionarily conserved process rebalances destabilizing perturbations and preserves 62 functionality in a changing environment. Correspondingly, dysfunctional homeostatic synaptic 63 plasticity has been implicated in several neurological diseases 14,15 . Considering the many facets  The above results suggest that high-frequency synaptic transmission is most sensitive to 158 manipulations of ORN AZs, consistent with previous reports 49 . High firing rates usually occur at 159 the beginning of the odor response within the first 200 ms ( Figure 1B, C). Thus, we examined the 160 effects of Cac RNAi during the presentation of a strong olfactory stimulus without the initial phase of 161 the odor response. The initial phase of the odor response is characterized by a strong increase 162 in firing rate that is highly correlated. Thus, as expected, removing the first 200 ms of the odor 163 response from the analysis resulted in an overall decrease in correlation ( Figure 1P compare to 164 Figure 1D), which was further reduced by Cac RNAi (Figure 1P, Q). In contrast, the increased 165 standard deviation of PN firing rates that was observed upon Cac RNAi was now completely 166 abolished. No difference in the standard deviation was observed between wt and RNAi flies even 167

Monosynaptic effects and circuit activity underlie distinct features of coding reliability 245
Next, we examined whether the diminished coding reliability caused by Cac RNAi is due to 246 monosynaptic mechanisms or instead arises from circuit effects. GABAergic iLNs also receive 247 their major input from ORNs and inhibit these presynaptically. Whereas PNs are uniglomerular 248 and receive homogenous ORN input, iLNs are multiglomerular and receive heterogeneous ORN 249 input. PNs respond to ORN activity in a non-linear manner 30,43,52 . In contrast, GABAergic iLNs 250 respond linearly to ORN activity 52 , are more sensitive to reduced input from ORNs 53 , and show a 251 stronger transiency in their odor response 54 . Thus, we speculated that at least some of the 252 observed reduction in PN reliability following presynaptic manipulations might arise from reduced 253 recruitment of iLNs. In particular, this may explain the increased standard deviation in Cac RNAi that 254 no longer occurred in the absence of the initial transient phase of PN odor responses ( Figure 1R These results demonstrate that knocking down Cac in ORNs severely impairs the activation of 264 iLNs. We therefore examined the effects of blocking GABAergic transmission on PN reliability. To 265 this end, we exposed the brain to 100 µM CGP54626, a selective GABAB receptor antagonist, 266 and 250 µM picrotoxin, a chloride channel blocker 56 and antagonist of GABAA receptors ( Figure  267 3E). As expected, GABA blockers affected wt firing rates ( Figure 3F) but had no effect on PN 268 spiking in Cac RNAi flies, where iLNs are hardly recruited ( Figure 3B). When blocking GABA 269 receptors in wt flies, we observed a significant increase in the variability of PN activity but the 270 temporal integration window of PN responses was unaffected ( Figure 3G). This suggests that 271 iLNs affect only certain aspects of the code reliability. Since the odor response of iLNs was 272 diminished when Cac was knocked down in ORNs we expected that blocking GABA receptors 273 would not further affect coding reliability in Cac RNAi flies. Indeed, blocking GABAergic transmission 274 had no effect whatsoever on variability and only a minor effect on the temporal integration window 275 in presynaptic Cac RNAi ( Figure 3G). 276

277
The AL also contains another population of inhibitory local neurons, which release glutamate. driving GABA-B-R2 RNAi57 in ORNs ( Figure 3H) has similar effects on PN coding reliability as 283 pharmacologically blocking the inhibitory iLNs ( Figure 3G). Indeed, GABA-B-R2 RNAi increased the 284 variability at high firing rates but did not affect the temporal integration window ( Figure 3I). The 285 glutamatergic iLNs inhibit PNs by activating the glutamate receptor GluClα 33 . Therefore, we 286 expressed an RNAi construct against GluClα in PNs ( Figure 3J) and tested their odor response 287 and coding reliability. Knockdown of the GluClα receptor in PNs had a strong effect on coding 288 variability, as observed for the pharmacological experiments and by blocking the inhibitory 289 presynaptic circuit ( Figure 3K). In addition, we also observed an overall decrease in correlation 290 ( Figure 3K). However, in contrast to Cac RNAi , which increased PN integration time ( Figure 1D), we 291 did not observe any such increase in GluClα RNAi , if at all, the integration time of PNs was slightly 292 decreased ( Figure 3K and Figure S6). In summary, both pharmacological and genetic approaches 293 suggest that the inhibitory AL circuit affects the variability of PN activity but not the temporal 294 reliability. 295 296

Reducing neural coding reliability disrupts behavioral reliability 297
The above results demonstrate that knocking down AZ Ca 2+ channels specifically in ORNs 298 decreases the coding reliability by PNs. To examine whether these changes are strong enough 299 to affect behavioral output we tested the flies' ability to correctly classify an ecologically relevant 300 stimulus. To reduce response variability, which is often observed for naïve behavior 58,59 , we paired 301 isopentyl acetate with an electric shock, using a well-established custom built apparatus 53,58-61 302 and tested the accuracy of the animals' behavioral responses, i.e. avoidance of isopentyl acetate. 303 Following prolonged exposure (2 minutes) to the olfactory stimulus, presynaptic Cac RNAi animals 304 were equally successful as their parental controls in classifying the odor ( Figure 4A, B), indicating 305 no impairment in the flies learning capabilities. However, when flies navigate in their natural 306 habitat, olfactory stimuli are often brief and repetitive. Since such short stimuli could not be 307 delivered in the above paradigm, we used an alternative assay in which the flies walked on a 308 Styrofoam ball. This setting allowed us to present the same pattern of olfactory stimuli as used 309 for the electrophysiological recordings ( Figure 1). Ten one-second trials of the shock-paired odor 310 isopentyl acetate were presented and the behavioral response was classified as correct if flies 311 turned away from the odor source ( Figure 4C). Indeed, Cac RNAi flies made significantly fewer 312 correct choices as a result of identifying isopentyl acetate less reliably ( Figure 4D). This reduction 313 in correct stimulus classification was not due to a learning defect since the learning index was 314 similar between wt and Cac RNAi flies ( Figure 4B). Furthermore, Cac knockdown was only 315 performed in the first order ORNs and not in third order neurons, which are required for memory 316 formation 62 . Since our electrophysiological results showed that homeostatic synaptic plasticity 317 rescues the neural code reliability only at lower odor concentrations, we tested whether behavioral 318 reliability was also rescued at low stimulation intensities. The overall learning scores of wt and 319 Cac RNAi flies were similar at low ( Figure 4E) and high odor concentrations ( Figure 4B). As 320 predicted, Cac RNAi flies were as successful as wt animals in correctly classifying the low 321 concentration odor ( Figure 4F). In contrast, while Cac RNAi animals displayed normal memory 322 formation at day 0 ( Figure 4G), low concentration odor classification was significantly impaired in 323 young flies ( Figure 4H). This nicely matches our finding that homeostatic compensation takes a 324 day to develop. In summary, these results demonstrate how synaptic plasticity can maintain 325 neural coding reliability and enable the animal to consistently classify an important physiological 326 stimulus. 327 328

Discussion 329
In this study, we addressed two distinct yet related questions. First, we provide in vivo evidence 330 that the molecular integrity of the AZ is important for neural coding reliability. Second, we 331 demonstrate that homeostatic synaptic plasticity operates at the ORN-PN synapse and that this 332 process compensates a reduction in release probability by increasing the number of ORN AZs. 333 The homeostatic adjustment restored eEPSC amplitudes in PNs and neural coding reliability. We 334 further show that the restored neural reliability maintains behavioral performance. Finally, we 335 demonstrate that the homeostatic compensation is limited in its efficiency and fails to maintain 336 reliable neural coding and behaviour at elevated firing frequencies in response to high odor 337

concentrations. 338 339
Using in vivo recordings, we show that high release probability of ORN AZs is required for a 340 reliable neural code in the postsynaptic PNs in response to physiological odor stimuli. Knockdown 341 of Cacophony from ORN AZs that reduced ORN release probability affected neural code reliability 342 in several manners. First, the high firing rates of the initial transient phase of PN odor responses 343 became more variable due to reduced recruitment of iLNs. Second, the onset of odor responses 344 was delayed and more variable. Third, the temporal dynamics of the odor response became less 345 reliable. This third effect results most probably from monosynaptic changes to ORN-PN 346 transmission. At a behavioral level, we find that reduced coding reliability of PNs impairs the 347 animals' ability to correctly classify a physiologically relevant stimulus. 348 We found that knocking down Cacophony affected neuronal coding reliability mainly at high firing 349 rates. This was true for both the monosynaptic as well as the circuit mechanisms. In terms of the 350 circuit effects on rate code reliability, this is in line with the recruitment of iLNs. iLNs sample many 351 types of ORNs with different response profiles 48 and as a result, they respond linearly to ORN 352 activity 52 . Thus, iLNs respond most strongly to high ORN firing rates, which in turn are triggered 353 by high odor intensities. Correspondingly, almost entirely eliminating the recruitment of iLNs by 354 manipulating ORN AZs (Figure 3), mostly affects processing of high firing rates. We find that a 355 decrease in SV release probability increases the latency and jitter of eEPSC (Figure 2 were randomly patched from both the lateral and medial cluster and iLNs were randomly patched 411 from the lateral cluster. Voltage or current recordings were performed using Axon Instruments 412 MultiClamp 700B amplifier in current-or voltage-clamp mode respectively. Data was low-pass 413 filtered at 1 kHz and sampled at 50 kHz. Upon break-in to the cell, a small constant current was 414 applied to maintain a membrane potential of -60mV. Action potential times were extracted using 415 a custom MATLAB code followed by manual inspection to verify correct identification. Flies used for functional imaging were reared as described above. Imaging was done as 444 previously described 59,61,76 . Briefly, using two-photon laser-scanning microscopy (DF-Scope 445 installed on an Olympus BX51WI microscope). Flies were anesthetized on ice then a single fly 446 was moved to a custom built chamber and fixed to aluminum foil using wax. Cuticle and trachea 447 in the required area were removed, and the exposed brain was superfused with carbonated 448 solution as described above. Odors at final dilution of 5X10 -2 were delivered as described above. The 20 chambers were stacked in two columns each containing 10 chambers and were backlit 498 by 940 nm LEDs (Vishay TSAL6400). Images were obtained by a MAKO CMOS camera (Allied 499 Vision Technologies) equipped with a Computar M0814-MP2 lens. The apparatus was operated 500 in a temperature controlled incubator (Panasonic MIR 154) at 25℃. 501 Fly position was extracted from video images using a virtual instrument written in LabVIEW 7.1 502 (National Instruments). The same virtual instrument was also used to control odor delivery. Data 503 were analyzed in MATLAB 2018a (The MathWorks). Conditioning protocol included 12 equally 504 spaced 1.25 s electric shocks at 50 V, and was repeated twice. The learning index was calculated 505 as (preference for isopentyl acetate before training) -(preference for isopentyl acetate after 506 training). Flies that were used for the ball assay in figure 4C were given a training protocol that 507 consisted of 12 equally spaced 1.25 s electric shocks at 50 V, and repeated 6 times with 15 508 minutes interval. Flies were then tested on the ball assay 12 hours later. 509 510 511 22

Ball assay 512
Flies walked on a treadmill ball as previously described 80 . Briefly, the fly thorax was glued to a 513 fine metal rod using wax in a manner that allowed for free limb movement. Flies were then placed 514 on a Styrofoam ball with a diameter of 9 mm which floated on an air-steam in a custom made ball 515 holder. Videos were captured using a Blackfly S (FLIR® Systems) camera fitted with a Computar 516 Macro zoom 0.3-1×, 1:4.5 lens at a frame rate of 100 FPS. The ball was illuminated by infrared 517 LEDs and the fly position was tracked offline using FicTrac 81 . Odor delivery was the same as for 518 the electrophysiological recording experiments described above. 519 520

Quantification and statistical analysis 521
Integration window analysis 522 For this analysis, only the first two seconds after the beginning of the odor stimulus were used. 523 Spike trains were then binned with bin sizes ranging from 1ms to 200ms with 1ms intervals. For 524 each bin size, the pairwise Pearson correlation was calculated for all combinations of the 10 trials 525 for each odor. Trials that did not show any odor response were excluded from this analysis. 526

Statistics and data analysis 538
All statistical testing and parameter extraction were done using custom MATLAB code (The 539 MathWorks, Inc.). All statistical tests details can be found in table S1. Significance was defined 540 as a p-value smaller than 0.05 and all statistical tests were two-sided. Normality assumption 541 was tested using the Shapiro-Wilk test 542 (https://www.mathworks.com/matlabcentral/fileexchange/13964-shapiro-wilk-and-shapiro-543 francia-normality-tests). In cases where the normality assumption was violated a permutation 544 test was used using the 'permutationTest' function in MATLAB 545 (https://github.com/lrkrol/permutationTest).           All correlation values for all PNs and odors were pooled to yield the correlation curve on the right.   A. Temporal reliability analysis as performed in Figure 1A but for single odors as indicated. Data obtained from Figure 1 and Figure S1. For all odors, a final odor dilution of 5X10 -2 was used.

B.
The optimal integration window analysis as performed in Figure 1D but for single odors as indicated. Data obtained from Figure 1 and Figure S1. For all odors, a final odor dilution of 5X10 -2 was used.
C. Firing rate reliability analysis as performed in Figure 1E but for single odors as indicated. Data obtained from Figure 1 and Figure S1. For all odors, a final odor dilution of 5X10 -2 was used. Analysis of the latency of the first spike (left) and jitter of the first spike (right) in PNs as performed in Figure 1I,J but in response to mock odor application (i.e. no odor was actually applied) for wt flies or Cac RNAi in ORNs (n=47-49). No change in any parameter was observed.  Left, the curve saturation point was calculated for each odor-neuron combination and pooled across the two odors for the data in Figure 3K. Right, Left curves are presented at a larger scale.

Figure S5
GluClα knockdown in PNs did not increase their optimal temporal integration window. If at all, it slightly improved as reflected by a smaller integration window spread and a slight peak shift to the left.             Two sample t-test Paired pulse ratio-day 0 (10ms) Figure 3D *         Figure S6