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Revealing Spatiotemporal Circuit Information of Olfactory Bulb in Large-scale Neural Recordings

Large-scale multi-site biosensors are essential to probe the olfactory bulb (OB) circuitry for understanding the spatiotemporal dynamics of simultaneous discharge patterns. Current ex-vivo electrophysiological techniques are limited to recording a small set of neurons and cannot provide an inadequate resolution, which hinders revealing the fast dynamic underlying the information coding mechanisms in the OB circuit. Here, we demonstrate a novel biohybrid OB-CMOS platform to decipher the cross-scale dynamics of OB electrogenesis and quantify the distinct neuronal coding properties. The approach with 4096-microelectrodes offers a non-invasive, label-free, bioelectrical imaging to decode simultaneous firing patterns from thousands of connected neuronal ensembles in acute OB slices. The platform can measure spontaneous and drug-induced extracellular field potential activity. We employ our OB-CMOS recordings to perform multidimensional analysis to instantiate specific neurophysiological metrics underlying the olfactory spatiotemporal coding that emerged from the OB interconnected layers. Our results delineate the computational implications of large-scale activity patterns in functional olfactory processing. The high-content characterization of the olfactory circuit could benefit better functional interrogations of the olfactory spatiotemporal coding, connectivity mapping, and, further, the designing of reliable and advanced olfactory cell-based biosensors for diagnostic biomarkers and drug discovery.


INTRODUCTION 31
The olfactory bulb (OB) is a vital chemosensory structure of the information coding mechanisms 1 . It allows 32 vertebrates to process and discriminate vast complex odorants and distinguish them with high selectivity 33 and sensitivity 2,3 . The OB's neuronal processes are distributed intricately, composed of an abundance of 34 dendrodendritic interactions and a stratified structure in five connected layers 4,5 . The OB also renders a 35 distinctive form of morpho-functional neuronal plasticity conferred by a constant supply of new neurons 36 (i.e., adult neurogenesis), allowing profound remodeling of the bulbar circuit in response to experience 37 and challenges 6 . 38 This organization represents a high degree of plasticity and is the first relay station of olfactory perception 39 with downstream information processing from the primary sensory epithelium to high-order neurons in 40 subcortical and cortical areas for odor identification and interpretation [7][8][9] . 41 This high dimensionality of olfactory processing and odor coding properties inspired the development of 42 biomimetic olfactory sensors, i.e., electronic noses 10-12 . They promise potential advances in medical 43 diagnosis, food quality, environmental, and military applications 13 . However, a fundamental inherent 44 shortfall in these electronic nose systems is the lack of realistic dynamics at the physiological cellular and 45 network levels, thus hindering this technology's exploitation for further challenging applications 14 . Hence,46 considerable achievements in genetic, biotechnology, and bioengineering have been put forth to enable 47 the implementation of olfactory cell-based biosensors (i.e., bioelectronic noses) 14,15 to circumvent the 48 limitations of conventional electronic noses and to promote specific measurement of target odorants 16 . 49 In this context, many studies have been reported to employ a range of different olfactory cell-based 50 biosensors [16][17][18] . Notably, a roadmap for advancing this technology has focused on deciphering the 51 inherent plasticity mechanisms of olfactory information encoded in the orchestrated spatiotemporal 52 activity patterns to refine and stabilize the OB sensory information 9 . These patterns are rhythmic neuronal 53 synchronizations that encompass time-varying spatial distributions of discrete spike trains superimposed 54 and complemented with a slow oscillation ˂ ~300 Hz of the local field potentials (LFPs) that reflect 55 subthreshold integrative processes [19][20][21] . 56 On the other hand, the mammalian olfactory system generates rhythmic oscillations of LFPs that exhibit 57 a functional role in local odor processing and odor discrimination (i.e., gamma frequency band; 40-100 58 Hz) 22 , odor learning and experience (i.e., beta band; 15-30 Hz) 23 , and respiratory oscillations overlapping 59 with the (theta band; 2-12 Hz) 24 . Thus, investigating the electrophysiological properties is crucial to further 60 understand the local OB functional circuit connections and behavior and how they shape sensory 61 processing. In view of these demands, many previous studies have used cell or slice-based biosensors to 62 report experimental paradigms and classical recording techniques for different olfactory signal biosensing 63 such as microelectrode arrays (MEAs) 25-27 , semiconductor field-effect transistors 28,29 , patch-clamp 64 recordings 30 , light addressable potentiometric sensors 31 , quartz crystal microbalance 32 , electrochemical 65 impedance spectroscopy 33 , and surface Plasmon resonance 34 . 66 Also, these efforts are aided by insights from computational models 35 and neuromorphic computing 36 . 67 Despite MEAs shortcomings (i.e., low spatial resolution due to low numbers of recording electrodes), this 68 method enables non-invasive, multi-site, long-term, and label-free probing of neuronal firing activity 69 without disruption of cellular integrity. Recently, the manufacturing of low-cost semiconductor 70 techniques based on complementary metal-oxide-semiconductor (CMOS) technology has been 71 introduced into the design of biosensors 37  integrates large and dense electrode arrays (i.e., 4096-microelectrodes) and on-chip signal processing 85 implemented in a CMOS-chip. The combination of the OB circuit and the high-density electrode 86 configuration allows the recording of real-time spontaneous and pharmacologically-evoked activation of 87 the OB neural circuit from thousands of connected neuronal ensembles within the interareal layers of the 88 OB operated in ex vivo. We further exploit our OB-CMOS-chip recordings with fundamental basic and 89 advance analyses to instantiate specific neurophysiological metrics underlying the olfactory 90 spatiotemporal information that emerged from the OB interconnected layers. Therefore, we characterize 91 the rhythmic oscillations of the LFPs, oscillatory frequency bands, and coherence magnitude for single 92 electrode activity as well as between OB layers. We also identify the event initiation onsets and quantify 93 the dynamic profiles of signal propagation patterns. The rich information ingrained in our unique datasets 94 also allows us to compute the spatial neural sources that contribute to the LFPs by using the kernel current 95 source density (kCSD) method, functional connectivity (FC) maps, and topological interaction of thousands 96 of active OB neuronal ensembles that delineate the construction of functional OB circuitry. Finally, we 97 validated our platform to unveil a large-scale functional remodeling in the OB circuitry due to ongoing 98 neurogenesis using a well-established and reported mouse model of enhanced neural stem cell 99 expansion 45-47 . 100 To our knowledge, this is the first report of a high-density CMOS-neurochip that allows revealing the 101 spatiotemporal functional processing in the interconnected OB circuit. Deciphering the mechanism of 102 large-scale OB electrogenesis will enhance our understanding of the neuronal ensembles' functional 103 organization. It will also provide an effective tool to investigate information processing and 104 spatiotemporal coding in the OB circuit. Together, our results offer vital evidence for the future 105 development of biomimetic olfaction detection biosensors for various applications. 106

MATERIALS AND METHODS 107
Animals and OB acute slices preparation. All experiments were performed on 8 weeks C57BL/6j (Charles 108 River Laboratories, Germany) and triple transgenic 4D-and 4D+ mice, previously described 45  participation in the circuit activity and followed a skewed lognormal distribution. Thus, we computed the 151 probability density function for the lognormal distribution: 152 Where is the mean, and is the standard deviation. 154 Gini coefficient and CV 2 . To quantify the inequality of participation of individual neurons in the OB circuit, 155 we employed the Gini index as a commonly used measure of inequality and sparsity 48 and computed as 156 the ratio of the areas on the Lorenz curve diagram: 157 Where A is the area above the Lorenz curve, and B is the area below for the cumulative LFP firing rates. 159 To assess the local-trial variability of the firing patterns, we used the coefficient of variation (CV 2 ) 49 , which 160 is defined as the function of pairs of adjacent inter-LFP-event intervals and +1 divided by the 161 average of the event intervals. 162 Time-frequency and PSD analyses. We first constructed the frequency-time dynamics in pseudo-color 164 spectrograms for a selected 10 sec time window using a filtered LFPs (1-100 Hz). Next, we computed the 165 Periodograms to identify dominant frequencies in the oscillatory activity in a given time-series. The 166 spectra were calculated using Welch's method 50 by calculating the Fast Fourier Transform of the recorded 167 LFPs to estimate the power spectral density (PSD). 168 Waveform classification. To characterize and allocate the nonsinusoidal shapes of the recorded LFP 169 waveforms to their OB layers, we developed a procedure consisting of four steps. First, (waveform 170 extraction) -we detected active firing electrodes clustered based on their structural relation to the OB 171 layers. We extracted information from hundreds of waveform segments that occurred at least in three 172 consecutive LFP events. Second, (Pre-processing) -all detected and extracted waveforms were denoised 173 by hard threshold algorithm and filtered by Low pass filter (1-100 Hz). Third, (Parameter Identification) -174 we defined in our extracted waveforms four features, including amplitude (waveform height feature), 175 period, rise-decay symmetry (slope features), and area (area feature). Fourth, (classification and 176 clustering), we reduced the dimensionality of the computed waveform features using the Principal 177 Component Analysis (PCA) and K-means clustering algorithms 51 to identify best-classified waveform 178 shapes into five clusters associated with the GL, GCL, PL, ONL, and OCx layers. 179 Spatial coherence. To measure the predictable relationship between firing LFP rates at different spatial 180 locations of the OB circuit, we calculated the 2D spatial coherence maps. We calculated the correlation 181 between pairs of firing electrodes of the CMOS-chip (64 x 64 electrodes) arranged in rows (x) and columns 182 (y). The function is a dimensionless quantity and ranges between (0,1) and defined as 52 : 183 Where ( ) and ( ) are the autospectral density of the firing x and y, respectively, and ( ) is the 185 cross-spectrum density of the firing x and y estimated using Welch's method. A Spatial coherence near 186 one indicates that the active electrodes encode all information of the firing rate of that spectral frequency 187 component. In contrast, a value close to zero indicates no information of the firing is present. 188 LFP propagation dynamics. To quantify the propagation magnitude of the spatiotemporal LFP Events in 189 all interconnected OB layers, we employed the center-of-activity trajectories (CATs) analysis 53,54 . We used 190 the voltage values embedded in the LFP frame activities within 5 ms moving time bins to collect the CAT 191 magnitudes in each firing event. The value of the CA at time t is a two-dimensional vector defined as: 192 Where ( ) represents the LFP firing rate corresponding to the active electrodes E within a time 194 window (tm). Also, ( ) and ( ) are the column and row numbers of the associated E. Then,195 and are the coordinates of the physical center of 64 x 64 electrodes. n is the total number of active 196 electrodes. Then, the CA trajectory from t 0 to t 1 with a time step ∆ is defined as: 197 Velocity of conduction. To track the putative displacement of �����⃗ of LFP events in the entire 199 interconnected OB circuit, the velocity of conduction was calculated as the average of instantaneous 200 vector quantity displacements calculated for the time of a propagating LFP event and expressed as: 201 Where ∆ is the change in displacement and ∆ is the change in time. 203 Functional connectivity and causal links. To infer the large-scale statistical dependent connectivity in a 204 multilayered OB network, we first calculated the cross-covariance between pairs of active electrodes in 205 the 64 x 64 array using the Pearson correlation coefficient (PCC) 55 . The correlation coefficient between 206 electrodes was then sorted based on the OB layers and presented in a symmetric matrix. The PCC is 207 defined as: 208 Where N is the number of all nodes in the network, , is the average path length between all nodes in 225 the network. 226 Further, based on local clusters, the local efficiency is the average efficiency of clusters (subgraph) for a 227 node with the neighbors, and calculated as: 228 Where is the number of nodes in the local subgraphs (clusters) G i of the network, , is the path 230 length between pair nodes in the cluster. 231 Wiring cost. The network cost was estimated based on the minimum spanning tree (MST) method using 232 the Kruskal algorithm. This approach is calculating the shortest path length connecting all network nodes 233 at the cheapest cost. 234 Statistical analysis. All statistical analyses were performed with Originlab 2020. All data in this work were 235 expressed as the mean ± standard error of the mean (SEM). All box charts are determined by the 25 th -75 th 236 percentiles, and the whiskers by the 5 th -95 th percentiles, and lengths within the Interquartile range (1.5 237 IQR). Also, the lines depict the median and the squares for the mean values. Differences between groups 238 were examined for statistical significance, where appropriate, using one-way analysis of variance (ANOVA) 239 or two-way ANOVA followed by Tukey's posthoc testing. P < 0.05 was considered significant. 240

RESULTS AND DISCUSSION 241
In what follows, we first developed and implemented a hybrid biosensor platform by integrating the OB 242 circuit into a CMOS-MEA chip, which allowed us to record LFPs and spikes from the OB multilayers. Next, 243 we exploited the multidimensional data focusing on the LFPs to infer and provide new insights about the 244 subthreshold integrative processes in high spatiotemporal resolution expressed by extracting unique 245 dynamic features of the oscillatory activity and functional mapping across the OB circuitry. (i.e., frequency 246 spectrum, coherence, cross-correlation, causal connectivity, and assessing neurogenic remodeling effect). 247

Implementation of the large-scale BIONICS platform 248
To quantitatively probe the large-scale firing patterns and characterize the olfactory spatiotemporal 249 coding of the whole-multilayered, we engineered a biohybrid OB-CMOS biosensor platform (BIONICS) 250 (Figure 1). It combines isolated whole-OB acute slices and a CMOS-chip. The OB-CMOS-chip continuously 251 perfused with an optimized aCSF to maintain high viability and stable, functional responses for several 252 hours (n=24 slices from 8 adult mice at 8 weeks old). Also, we employed optical imaging into the 253 bioelectrical OB-CMOS-based setup allowing the light-imaging acquisition of OB slices concurrently with 254 the whole-circuit firing pattern recordings (Figure 1a). The CMOS-chip integrates a 64 x 64 255 microelectrodes pixel array to allow the simultaneous recordings of sub-millisecond extracellular firing 256 information from the entire OB circuitry (Figure 1b). The active sensing area of the CMOS-chip is 2.69 x 257 2.69 mm (i.e., 4096 microelectrodes with 42 µm pitch) 39 , which is compatible with the area of a whole 258 mouse OB slice (i.e., 2 x 2 mm). Our OB-CMOS biosensor readout captured the real-time circuitry 259 acquisition rendered by a single electrode (i.e., pixel-like) sensor from sequential bioimaging frames 260 encoding olfactory functional information at high spatiotemporal resolution. The voltage values of each 261 sensing pixel were mapped on large-scale OB layers with a pseudo-color scaling. The final construction 262 illustrated real-time bioimaging video frames of the entire OB functional circuit ( Figure. 1b, and  263 Supplementary Movie 1). The CMOS-chip surface was functionalized using a specific adhesion-promoting 264 molecule -poly-DL-ornithine (PDLO) 60 that enhanced the cell-electrode interface coupling and improved 265 SNR by increasing the seal resistance (R seal ) between the tissue and the electrode ( Figure 1c). 266 antagonist. We computed several first-order statistical parameters (LFP energy, oscillation frequency, 281 amplitude, and duration). Spontaneous oscillation and spiking activity were detected in all OB layers 282 except the ONL. We found substantial responses of the OB circuit assessed by the statistical parameters 283

Features of spatiotemporal LFP firing patterns and activity-dependent distributions in multilayered OB
shown in (Figure 2a, right). We also illustrated the spatial organization of those large-scale metrics by 284 overlaying the computed mean of the functional, statistical metrics obtained from the LFP recordings on 285 the OB circuits' optical images ( Figure. 2a left). 286 The collective functional representations given in firing patterns of synchronized neuronal populations 287 have been shown to exhibit a skewed distribution of firing rates in other experimental setups 41,63 . Our 288 large-scale OB recordings found similar highly skewed lognormal-like distributions of firing LFPs in GCL, Pl, 289 and OCx layers (Figure 2b). Those skewed distributions showed a range of low and high firing patterns 290 indicating a network-wide firing fluctuation and a diverse repertoire of participating neurons to the 291 circuitry firing information. In other words, when the OB circuit activity was enhanced by unbalancing the 292 inhibition or excitation drive (i.e., BiC and MK-801 treatments), the firing distribution showed lognormal-293 like distributions in the GCL, PL, and OCx, but a symmetric distribution in the GL, indicating a mixture of 294 circuit firing regimes of fluctuation-driven and mean-driven, respectively 64 . Remarkably, this mixture of 295 activity regimes within interconnected OB layers indicates the significance of featured spatiotemporal 296 patterns in maintaining the sensitivity and stability balance of the OB circuitry. 297 To parametrically evaluate the spatial sparsity and diversity of participating neurons (i.e., the efficiency of 298 neuronal representations) rendered by their firing potentials, we used Lorenz statistics to generate the 299 Gini coefficient 48 . The Gini index is between zero and one and reflects the inequality of participation of 300 neurons giving by their firing LFP values; it is higher in the unequal participation of neurons to the firing 301 activity (Figure 2c). The Gini index was higher in the spontaneous recorded LFPs in all OB layers (i.e., 302 particularity in GCL, PL, and OCx), which decreased along with the treatment of the compound of 4-AP, 303 BiC, and Mk-801, respectively (Figure 2d). This indicated that neuronal selectivity and the sparseness of 304 representation in a given spontaneous activity reflected the strong activation of a relatively small group 305 of cells. This is the first evidence of the sloppiness in spontaneously active OB layers. A small but stable 306 subnetwork of neurons is critical for global stability, allowing considerable plasticity to occur in the 307 remaining majority of cell population 65 . 308 On the other hand, the evoked-activity groups (i.e., BiC and MK-801) showed sloppiness but not 309 sparseness, which inferred stiff dimensions involving more complicated combinations of other parameters 310 following the circuit modulation. Despite the overall stability we observed in the OB functional 311 representations, changes in the LFP timings were found upon manipulating the balance of inhibition and 312 excitation by pharmacological treatment. To evaluate these changes in all OB layers, we quantified the 313 LFP timings irregularity using the coefficient of variation index (CV 2 ) 49 (see methods). We found a 314 significant decrease in the CV 2 (i.e., GCL=1.3, 1.04, and 0.87) in spontaneous, 4-AP, and BiC conditions, 315 respectively (Figure 2e). This decrease in CV 2 values indicates decreased variability, hence, increased LFP 316 regularity upon compound treatment. Altogether, our results delineate detailed spatiotemporal 317 functional implications of discharge patterns in distinct subnetworks of the OB layers opt for stability and 318 spatial information coding. This could emerge from the intrinsic biophysical properties of the individual 319 ensembles in these circuits and their distinct wiring, as reported in other brain states 63 . 320

Characterization of oscillatory-dependent features of LFPs and waveform classification 321
Next, we sought to identify the functional significance in the frequency and time domains of the LFP 322 oscillatory synchronized waves rendered by the interconnected OB layers. Thus, we employed band-pass 323 filters on the recorded data to set for high-frequency oscillation (HFO), indicating spiking activity and low-324 frequency-oscillation (LFO), indicating three prominent oscillatory bands in different layers (i.e., theta, 325 beta, and gamma) (Figure 3a). We observed slow and fast oscillations in nearly all layers under 326 spontaneous and pharmacologically-modulated activity (Figure 3a, GL and GCL layers examples). Then, 327 we computed the synchronous frequency-time domain dynamics of a selected electrode within the 4096-328 microelectrode array in pseudo-color spectrograms. This spectrogram exhibited superposed sustained 329 oscillatory events in the GL layer lasting for several seconds with coordinated frequency peaks in the 330 theta-gamma band (Figure 3b, left). 331 Similarly, (Figure 3b, right) shows the GCL layer's spectrogram, where single transient pulses of oscillatory 332 events occurred with a frequency range between 4-60 Hz. Further, we employed the power spectral 333 density (PSD) analysis to simultaneously quantify a specific oscillatory band's power magnitude in the 334 interconnected OB layers recorded by a full-CMOS array from spontaneous and drug-induced activity 335 phases (Figure 3c, and Supplementary figure 1). Despite the noticeable fluctuations in the power 336 magnitude upon pharmacological manipulation, we found a prevailing network frequency oscillation in 337 the theta band in all OB layers, predominantly in GCL and GL. These results confirm evidence on the 338 rhythmic theta oscillations coordinated by the glomerular network 66 . Further, the theta-gamma coupled 339 oscillations were also reported to emerge from the individual mitral cells 67 and also found in hippocampal 340 networks 68 . Our results suggest rhythmogenic subregions of the OB circuitry to drive distinct functional 341 microcircuits that are vital for OB coding. 342 In addition, investigating the features of oscillatory waveforms can better elucidate the contribution of 343 individual neuronal populations in the spatially connected layers and the olfactory information coding. 344 Therefore, we employed unsupervised clustering of LFP waveforms, which resulted in five distinct 345 waveform classes across all OB layers (Figure 3d). We analyzed hundreds of waveforms in each OB layer 346 made possible by the multidimensional high spatiotemporal resolution CMOS-recordings. The 347 classification process was performed by PCA and clustering with the k-means algorithm (see methods). 348

Mapping the spatial firing patterns for global circuit dynamics 349
Coherent oscillatory activity in the olfactory circuit is rendered by the spatiotemporal LFP patterning 350 across the OB layers. We sought to exploit BIONICS to quantify the spatial mapping of large-scale firing 351 rate encoded in the whole OB interconnected layers. To do so, we computed the spatial coherence as the 352 correlation of the encoded firing rates of interelectrode distance. The spatial extent of LFP rates is derived 353 from each pixel in the 4096-microelectrode array to capture the dynamics of rhythmic components in 354 space and estimate the breadth of activity origin. Thus, higher coherent electrodes have more regulated 355 LFP firing in a specific cluster of the OB region in space, while lower coherent electrodes show more 356 nonstationary and lower firing activity. In all recorded OB slices, coherence increased across evoked-357 activity conditions (spontaneous vs. 4AP, BiC, and MK-801, respectively) due to increased spatial rate 358 coding under the activation of more cells in the OB circuits (Figure 4a). We also computed and quantified 359 the population average of the spatial coherence across the pharmacological conditions divided over bins 360 by groups (Figure 4b and inset). Despite analogous peaks in all groups, the spontaneous and 4-AP groups 361 do not extend the right tail, as in the BiC and MK-801 groups, indicating recorded electrodes with low 362 coherence values. On the other hand, the distributions of BiC and MK-801 groups are shifted to the right, 363 indicating an increase of the spatial coherence for the average oscillatory activity in the whole OB 364 neuronal circuit. These significant changes in the spatial coherence may set new standard values, 365 especially in the GL layer (i.e., greater than 0.3 ), which meet the criteria for considering the GL as a 366 morpho-functional basis of the spatial coding in the early processing of the sensory input 69 . 367 In turn, this mapping may provide insights into a better understanding of large-scale circuit coding, 368 decoding, and functional features of synchronized firing patterns in OB circuits. 369

Localized initiation and large-scale propagation of synchronized oscillatory activity 370
In a multilayered OB circuit, the site of generation and spatial propagation of rhythmic electrical patterns 371 are critical for the integrative activity of olfactory information processing. We used the simultaneous 372 recordings from all OB layers to compute the initiation of the field potential signals and trace the 373 spatiotemporal propagation of individual synchronized events. Figure 4c and Supplementary Movie 2 374 show multi-frame pseudo-color images of the LFP oscillatory activity overlaid on the OB circuit's 375 corresponding anatomical regions. In the case of MK-801 treatment, the input signal is processed at the 376 ONL. The oscillatory activity is initiated in the GL layer, propagates across GCL and PL layers to finally send 377 output at the OCx layers. These characterized patterns sculpt complex propagation dynamics of the 378 cellular mechanisms underlying the spatiotemporal coordination of information processing displayed by 379 glomerular, mitral, and granule cells in the OB circuit. 380 Furthermore, Figure 4d shows the horizontal propagation of the LFPs across all OB layers. The dynamical 381 propagation is illustrated as the time shift of the peak of the oscillatory events of exemplified signal traces 382 selected from five electrodes located in different OB layers. 383 Next, to quantitatively investigate the propagation dynamics in detail and identify the ignition site of the 384 location-weighted average of spatiotemporal LFP patterns, we employed a population parameter using 385 the center-of-activity trajectories (CAT) 54 . We calculated the CAT mean for each electrode by integrating 386 the total number of LFP events in 200 ms voltage-coded movies (see methods). We performed the CAT 387 analysis on the spontaneous, 4-AP, BiC, and MK-801 groups that showed distinct ignition sites in the GL 388 layer in all groups, with a dynamic population propagation towards the OCx in BiC and MK-801 ( Figure  389 4e). 390 Furthermore, measuring a multi-site relation of spatiotemporal differences of occurring events allowed 391 us to compute the instantaneous velocity of displacements of the CATs of LFP events in spontaneous and 392 pharmacologically-induced groups to describe the dynamic of spreading activity within the 393 interconnected layers of the OB circuitry. We found a range of propagation mean velocities of CATs ranged 394 from (117 ± 17 to 323.67 ± 21.4 mm/s) in spontaneous and MK-801 groups, respectively (Figure 4f). These 395 values are in the range of other previously reported for olfactory bulb velocity of conduction in rats (250-396 460 mm/s) 70,71 . 397 In sum, these results display the global dynamics of the OB population activity by incorporating the 398 physical location of the electrode and the neuronal firing patterns of the multilayered OB circuitry. Also, 399 illustrate evidence of spatial changes in interconnected neuronal populations of the bulbar 400 rhythmogenesis upon blocking the dendrodendritic synaptic connections with MK-801. Similar results 401 were also confirmed by previous in vivo reports on the role of NMDA receptor blockade for 402 dendrodendritic inhibition concluded in smaller-scale electrophysiological and molecular readouts 72,73 . 403

Multilayered OB functional decomposition for network-wide connectivity 404
The intricate spatiotemporal propagation patterns illustrated in the previous section involve diverse local 405 neuronal connectivity necessary to synchronize the OB population. To unravel OB circuitry's complexity 406 to understand the underlying information processing, we have examined the emergence of inter-bulbar 407 functional connectivity patterns as a network of pairwise firing electrode interactions. In this context, we 408 computed the Pearson correlation between all LFP firing electrode pairs and performed the OB regional 409 clustering to identify the level of synchronization (Figure 5a). Quantitatively, the cross-correlation 410 increased significantly after pharmacological treatment with 4-AP, BiC, and MK-801 compared to the 411 baseline spontaneous activity (Supplementary figure 2). Further, by tracing the emergence of functional 412 links from spontaneous to MK-801-evoked activity, we found in the GL, predominant distinct populations 413 of activated ensembles forming hub microcircuits and connecting OB subregions circuitry (Supplementary  414  figure 3). This mesoscale rewiring of those new connections is signaled by the activity-dependent changes 415 processed by the contemporary firing ensembles in the GL and determined the interareal coordination 416 and reorganization of the population activity. These results illustrate individual neuronal ensembles' 417 contribution to the OB network synchrony in ever-available detail. Further, help to identify the critical OB 418 subregions for optimizing network patterns in a large-scale spatial organization. 419 Identifying OB neuronal transmembrane current source and sink generators 420 Next, we further exploited our platform to pinpoint the high spatiotemporal resolution of local field 421 generators (i.e., sources and sinks) in the inter-bulbar circuitry as emerged from the oscillatory activity of 422 OB neuronal ensembles. Thus, we constructed bidimensional maps employing the kCSD method 58 to 423 estimate the average transmembrane currents extracted from the field activity recordings (Figure 5b, and 424 Supplementary Movie 3). At the beginning of the LFP activation (t=187.5 ms), the KCSD maps (Figure 5b,  425 bottom) showed defined topographic activation features indicated by stronger and focused source 426 generators (red color-coded in the GL and GCL layers with defined cellular identity (i.e., the 427 microelectrodes underlying the transmembrane dipole). In contrast, the potential maps (Figure 5b,  properties that were unfeasible in smaller-scale electrophysiological methods. 460

CONCLUSIONS AND OUTLOOK 461
The unmet need for large-scale multi-site bioelectrical imaging techniques limits the understanding of 462 information processing in the olfactory system. It also hampers implementing olfactory biomimetic cell-463 based biosensors to exploit olfaction in fundamental applications. computational models capable of achieving faster and optimized olfactory information processing and 500 enabling optimal selectivity and sensitivity for challenging applications. 501 In sum, our work has attempted to paint with a large-scale neurotechnological approach the breath and 502 wonders of the chemosensory olfaction system. BIONICS has meticulously broken down the intricate firing 503 patterns associated with olfactory code, promising progress, not only toward understanding neural 504 processing of sensory information in the olfactory system but the biomimetic design of a bioelectronic 505 nose and artificial chemosensory system development that may provide specific biomarkers for health 506 and disease. 507

ACKNOWLEDGMENT 508
We would like to thank Prof. Dr. Gerd Kempermann, Dr. Caghan Kizil (DZNE, Dresden), and Dr. Alessandro 509 Maccione (3Brain AG, Switzerland) for their insightful comments on the manuscript and the fruitful 510 discussion. We also thank Dr. Daniel Wójcik and Mr. Władysław Średniawa (NENCKI Institute, Poland) for 511 their support on the kCSD analysis. We are thankful to Dr. Sara Bragado Alonso for supporting experiments 512 using the 4D line. We would also like to acknowledge the Animal platform of DZNE-Dresden (Dr. Alexander 513 Garthe, Anne Karasinsky, and Sandra Günther) for their support. We also like to acknowledge Ms. 514 Katarzyna Wiśniewska (Thames British School, Poland) and Ms. Brett Emery (DZNE-BIONICS lab, Germany) 515 for proofreading and discussing the manuscript's clarity and readability. 516 517 AUTHOR CONTRIBUTIONS 518 HA: Project conceptualization, planning, performing & management, and writing the manuscript 519 FC: Provided the mouse line and data concerning the 4D overexpression system 520 representations and statistical characteristics of the large-scale firing patterns recorded simultaneously 703 from the entire OB slices (i.e., 4AP induced activity example). Pseudo-color values of the energy, 704 frequency, amplitude, and LFP activity duration superimposed to the OB structural images. LFP energy is 705 significantly higher in GL than other layers, and frequency denotes a similar frequency oscillation range in 706 all layers. The amplitude is also significantly higher in GL and increased with drug treatment; in contrast, 707 the LFP events' duration is decreased with consecutive drug treatments. b) LFP firing events display a 708 lognormal-like distribution in interconnected OB layers allowing to elucidate the network dynamics. c) 709 Lorenze curve and Gini coefficient analysis indicate the inequal neuronal participation and sparseness. d) 710 Gini coefficient quantification for the firing activity in all OB layers (*p < 0.05, **p < 0.01, ANOVA). e) CV 2 711 values (0 → 2) showing the quantification of the spread of IEIs for the entire firing events. The CV 2 is 712 reduced (towards regular pattern) after blocking the GABAergic population with BiC as indicated by 713 moving red arrows to the left side (**p < 0.01, ANOVA) (n=24 slices from 8 mice). 714