V1-Origin Bidirectional Plasticity in Visual Thalamo–Ventral Pathway and Its Contribution to Saliency Detection of Dynamic Visual Inputs

Short-term bidirectional plasticity induced by biased-adaptation protocol in Area 17

We performed electrophysiological single-unit recordings in primary visual cortex (Area 17) of cats to investigate the adaptation effect induced by biased-adaptation protocol (Fig. 1A). First, neurons in Area 17 were stimulated by the Control protocol, that is, sinusoidal drifting gratings of 12 or 24 evenly distributed directions, presented for 0.5 s with 0.5 s blank intervals (Fig. 1B,C). Under the Control protocol, neurons in Area 17 exhibited strong orientation selectivity (OSI = 0.47 ± 0.02, n = 105 from 7 cats; OSI, consistent with previous reports; Li et al., 2017).

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Figure 1.

Biased adaptation induces short-term bidirectional-plasticity in Area 17. A, Brain regions being studied in this study are LGN and Areas 17 and 21a. B, Schematic figure of the experimental design. Top, The temporal order of stimulation sessions; middle, the distribution of orientations during each session; and bottom, the temporal structure (test/blank) of a single stimulus. C, Schematic figure of stimulation and recording protocol. D, Orientation tuning curves of two typical neurons exposed to high-intensity (biased-adaptation probability > 0.6) biased-adaptation stimulus. Black and red lines indicate preadaptation and postadaptation conditions, respectively. Arrows indicate the orientations of adaptors. E, Population statistics of peak response change ratio (%) after high-intensity biased-adaptation stimulus. F, Orientation tuning curves of two typical cases exposed to biased-adaptation stimulation; colors of lines indicate biased-adaptation probabilities; arrows indicate the orientations of adaptors. G, Peak response curves of two typical cases in F. H, Population peak response change ratios of Area 17 neurons exposed to biased-adaptation stimulation. I, Proportion of bidirectional-plastic, adaptation-only, and stable neurons. J, Peak responses of two typical neurons before (−6 min), during (0 min), and after (6, 12 min) potentiation induction. K, Recovery time of potentiation-exhibiting neurons after potentiation induction. The mean recovery time is 10.04 min. L, Two typical cases, averaged neuronal responses to adaptors over time during biased-adaptation stimulation. M, Population response-change ratios of tested neuronal responses to adaptors over time during biased-adaptation stimulation. Error bars indicate SEM; ***p < 0.001, **p < 0.01, *p < 0.05. n.s.: not significant (p ≥ 0.05).

After that, the biased-adaptation protocol was applied. To induce the adaptation effect, the occurrence probability of the adaptor was significantly higher than the one of the nonadaptor (Fig. 1B), which is similar to previous reports (Benucci et al., 2013; Li et al., 2017). In this article, we chose BAP (the occurrence probability of the adaptor) to quantify the extent of bias in biased adaptation (for Control stimulus using 12 or 24 directions, BAP are 0.08 or 0.04, respectively). To quantify the extent of influence induced by biased adaptation, we calculated the peak response change ratio of individual neurons based on preadaptation peak response (see above, Materials and Methods). To thoroughly study how biased adaptation influences neurons in Area 17, we set the BAP gradient (0.12 ∼ 0.79) to investigate the effects on peak response change ratio.

In our study, orientations of adaptors were chosen within 15° near neuronal preferred orientations drifting at optimal directions. With strong biased orientation-adaptation stimulation (BAP >0.6), the orientation tuning curve of tested individual Area 17 neuron was suppressed near the adapted orientation (Fig. 1D). Statistically, tested Area 17 neurons exhibited the classical orientation-adaptation effect (Fig. 1E; significant suppression, −20.16 ± 3.02%, p = 3 × 10⁻⁹, n = 81 from 7 cats) by strong biased-adaptation protocol (BAP > 0.6). These results confirmed the effectiveness of biased-adaptation protocol; it could induce reliable visual adaptation effects, which is consistent with previous reported suppressive effects of top-up orientation adaptation (Kohn and Movshon, 2004; Patterson et al., 2013, 2014a) or biased orientation adaptation (Benucci et al., 2013).

However, at low biased-adaptation probability, we found that the tested individual Area 17 neurons exhibited response potentiation instead of suppression (compared to preadaptation). This potentiation was maximal at biased-adaptation probability 0.12 (Fig. 1F,G; case 1, from 19.11 ± 1.40 Hz at BAP 0.04 to 28.89 ± 2.50 Hz at BAP 0.12; p = 0.003, n = 10 repetitions) or 0.18 (Fig. 1F,G, case 2, from 26.67 ± 2.00 Hz at BAP 0.04 to 34.40 ± 3.40 Hz at BAP 0.18; p = 0.065, n = 10 repetitions). However, at a higher biased-adaptation probability (>0.28), this potentiation was reversed into suppression, and the suppression effect was enhanced as the biased-adaptation probability increased to 0.64 (Fig. 1F,G; case 1, from 19.11 ± 1.40 Hz at BAP 0.04 to 9.78 ± 1.80 Hz at BAP 0.64, p = 0.001; case 2, from 26.67 ± 2.00 Hz at BAP 0.04 to 20.89 ± 2.40 Hz at BAP 0.64, p = 0.083; n = 10 repetitions). Population statistics of neurons from seven cats showed identical results (Fig. 1H); in Area 17, low-intensity of biased-adaptation could induce neuronal response potentiation (BAP 0.12, 20.83 ± 5.77%, p = 0.002, n = 25; BAP 0.18, 30.54 ± 7.59%, p = 0.001, n = 27; BAP 0.21, 15.74 ± 4.91%, p = 0.004, n = 61), whereas high intensity could induce significant suppression (BAP 0.64, −27.56 ± 5.34%, p = 2 × 10⁻⁴, n = 21; BAP 0.79, −17.57 ± 3.59%, p = 7 × 10⁻⁵, n = 60). The difference in sampled neuron numbers was because of slightly different protocols (12 and 24 directions would result in different BAPs) and an incomplete dataset because of strict objective criteria including stable single-unit recording, full recovery from previous biased-adaptation probability, and so on (see above, Materials and Methods). It is notable that a full set of biased-adaptation recordings lasted at least 100 min (including recovery time after each BAP session), and an incomplete dataset of a particular neuron was not rare.

This biphasic form of the adaptation effect (that the same neuron could significantly exhibit potentiation under low BAP and suppression under high BAP) is distinct from the classical suppressive-only visual adaptation effect (Kohn, 2007; Webster, 2015), and we named this novel adaptation effect bidirectional plasticity. For each individual neuron, when it shows statistically significant potentiation at at least one low BAP and suppression at at least one high BAP, we define it as bidirectional plastic. Overall, a majority (70.37%, 57/81) of Area 17 neurons exhibited bidirectional-plasticity, and only a minority (27.16%, 22/81) of Area 17 neurons exhibited pure response suppression after biased adaptation (Fig. 1I).

It is notable that for low BAP (such as 0.21 and 0.07 for nonadaptors), 10 repetitions of 12 directions only took 140 s to finish, which means within 140 s the neuronal response to the adapted orientation could be significantly enhanced, suggesting a rapid inducing time. To quantify the recovery time of this bidirectional-plasticity, after the induction by biased-adaptation protocol, we successively stimulated and recorded bidirectional-plastic neurons with the Control stimulus (each lasting for 2 ∼ 3 min). Tested neurons were considered recovered once their peak responses were not significantly different from the preadaptation response level (Fig. 1J). For tested potentiation-exhibiting neurons (n = 72), the averaged recovery time of potentiation or suppression was 10.04 ± 1.11 min (Fig. 1K) or 14.23 ± 1.50 min, respectively. The results above suggest that this bidirectional-plasticity is a short-term effect.

To further investigate the detailed inducing timeline of potentiation effect during biased-adaptation stimulation, we examined the dynamics of neuronal responses to adaptors. For data of BAP ≤0.21 (which could induce potentiation effect; Fig. 1H), we averaged the neuronal responses to adaptors at fixed intervals (40 s) to check whether there existed a stable tendency of response change. We found that tested neuronal responses to adaptors exhibited a potentiation trend over time (Fig. 1L); during the biased-adaptation stimulation, compared with initial state (0 ∼ 40 s from stimulus onset), the tested neuronal responses to adaptors exhibited a potentiation trend from 40 s to 160 s after stimulus onset, suggesting a rapid potentiation effect. At the population level, we also found that tested neurons could exhibit potentiation during 40 ∼ 160 s from stimulus-onset (Fig. 1M); tested neurons exhibited significant potentiation during 40 ∼ 80 s (14.86 ± 6.60%, p = 0.026, n = 72), 80 ∼ 120 s (12.55 ± 5.83%, p = 0.026, n = 72), and 120 ∼ 160 s (21.9 ± 11.8%, p = 0.034, n = 72). The above results suggested that the potentiation effect could be induced within dozens of seconds, showing a rapid modulation of neuronal response.

We also examined the potential influence of the probability of nonadaptors on bidirectional-plasticity and found that tested neurons could exhibit bidirectional-plasticity using 12 and 24 directions, and there was little difference between the BAPs corresponding to maximal neuronal potentiation. These results suggested that the influence of the probability of nonadaptors on bidirectional-plasticity was limited.

Potentiation induced by biased-adaptation in Area 17 is feature selective but interocular transferable

Feature-preference modulation during adaptation may reflect the interactions between feature-coding channels in visual system (Maffei et al., 1973; Movshon and Lennie, 1979; Carandini et al., 1998; Müller et al., 1999; Dragoi et al., 2002; Felsen et al., 2002; Solomon and Kohn, 2014), suggesting cortical circuit mechanisms. To further investigate the potential mechanisms of potentiation of Area 17 neurons induced by the biased-adaptation protocol, we studied two aspects of neuronal response change in Area 17, inducing and transferring stages. We used 12 directions for stimulation in these experiments (BAP of Control stimulus was 0.08).

During the inducing stage (when biased-adaptation protocol was applied), the above findings have suggested that majority of Area 17 neurons could exhibit potentiation under optimal pattern parameters (Fig. 1H). However, when nonoptimal (nonpreferred) pattern parameters (50 or 150% preferred spatial-frequency, or 50% contrast) were chosen in biased-adaptation protocol, it could not induce a potentiation effect (Fig. 2A). Population statistics of neurons from two cats suggest an identical result (Fig. 2B); when biased-adaptation probability is <0.32, under 50% preferred spatial-frequency (group SF−), 150% preferred spatial-frequency (group SF+), and 50% contrast (group C−) conditions, tested potentiation-exhibiting neurons (significantly potentiated when induced by optimal condition; SF−, 20.51 ± 5.11%, p = 0.001, n = 17; SF+, 25.71 ± 5.90%, p = 8 × 10⁻⁴, n = 14; C−, 22.43 ± 5.01%, p = 5 × 10⁻⁴, n = 15) were significantly suppressed under each nonoptimal condition (SF−, −5.63 ± 1.62%, p = 0.002, n = 17; SF+, −24.65 ± 6.50%, p = 0.001, n = 14; C−, −8.31 ± 2.33%, p = 0.002, n = 15). Moreover, when biased-adaptation probability equaled 0.45, tested neurons did not exhibit a significant response change under optimal condition (Fig. 2C; SF−, 4.12 ± 4.35%, p = 0.357, n = 17; SF+, −1.25 ± 5.42%, p = 0.822, n = 14; C−, 4.20 ± 3.39%, p = 0.236, n = 15) and were significantly suppressed under nonoptimal conditions (SF−, −12.05 ± 4.90%, p = 0.013, n = 17; SF+, −30.86 ± 6.02%, p = 2 × 10⁻⁴, n = 14; C−, −16.36 ± 2.73%, p = 3 × 10⁻⁵, n = 15). This result suggests that potentiation could only be induced at around neuronal preferred pattern-parameter condition. In other words, the potentiation induced by biased-adaptation protocol is pattern-feature selective.

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Figure 2.

Potentiation induced by biased adaptation in Area 17 is selectively induced by optimal stimuli but interocularly transferable. A, The induction of bidirectional plasticity in three typical neurons exposed to preferred and nonpreferred stimuli. Case 1, 50% preferred spatial frequency; case 2, 150% preferred spatial frequency; case 3, 50% contrast. Red lines (open circle) are induced by preferred stimuli, whereas blue lines (solid circle) are induced by nonpreferred stimuli. Black circles indicate Control condition. B, Population and individual peak response change ratios of Area 17 neurons exposed to preferred and nonpreferred stimuli (biased-adaptation probability < 0.32). Black points indicate population statistical results, whereas gray points indicate individual data. C, Same as B, when biased-adaptation probability equals 0.45. D, Population statistics of peak response change ratios during transferring period. Neurons were initially exposed to Control stimulus using preferred or nonpreferred (50 or 200% SF_pref) parameters (pre-Ad); then neurons were potentiated by biased-adaptation stimulus using preferred parameters (Trained under Preferred condition). After potentiation induction, neurons were immediately exposed to Control stimulation using nonpreferred parameters to examine transferability (Tested under nonpreferred condition, peak response change ratios were calculated based on pre-Ad using corresponding nonpreferred parameters.). Black Points indicate population peak response change ratios, and gray points indicate individual neuronal firing rates. E, Histogram of Ocular Dominance Index (n = 26) of Area 17 neurons; 1 means purely contralateral driven, −1 means purely ipsilateral driven. F, Peak response curves of two typical neurons in interocular transfer experiment. Red lines (open circle) are evoked by Trained Eye (which is monocularly induced), and blue lines (solid circle) are evoked by Passive Eye (which is covered during induction and tested after monocular induction). G, Population peak response change ratios of potentiation-exhibiting Area 17 neurons. Red bars indicate the Trained Eye, whereas blue bars indicate the Passive Eye. Error bars indicate SEM; ***p < 0.001, **p < 0.01, *p < 0.05. It is notable that we adopted the 12-directions protocol in these experiments, and thus the probability of the Control stimulus was 0.08 instead of 0.04. pre-Ad: preadaptation. n.s.: not significant (p ≥ 0.05).

During the transferring stage (after the potentiation effect was already induced by optimal stimuli), we investigated whether the potentiation induced by optimal stimuli remained under nonoptimal pattern condition. In other words, we examined whether the potentiation of Area 17 neurons could be transferred between different pattern-feature conditions. We induced potentiation in Area 17 neurons with proper biased-adaptation probability under optimal pattern parameters and then immediately stimulated these neurons under Control conditions using nonoptimal parameters (50 or 200% preferred spatial frequency). Peak response change ratios were calculated based on corresponding preadaptation Control (optimal or nonoptimal stimuli) results.

Consistent with previous results, tested neurons (from two cats) exhibited significant potentiation after biased-adaptation stimulation under optimal parameters (Fig. 2D; Trained, 50% SF_pref Group, 31.75 ± 9.90%, p = 0.005, n = 11; 200% SF_pref Group, 24.14 ± 4.89%, p = 3 × 10⁻⁴, n = 14). However, when exposed to nonoptimal parameters immediately afterward, tested neurons still exhibited significant potentiation relative to corresponding preadaptation Control (nonoptimal) results (Fig. 2D; Tested, 50% SF_pref Group, 27.20 ± 13.60%, p = 0.036, n = 11; 200% SF_pref Group, 184.00 ± 30.80%, p = 5 × 10⁻⁵, n = 14, large peak response change ratios were because of poor activation of tested neurons under 200% SF_pref conditions before biased adaptation). Similarly, when potentiation-exhibiting Area 17 neurons were exposed to optimal Control stimulus (with identical duration to nonoptimal Control stimulus) immediately after potentiation induction, their response potentiation could also remain (10.65 ± 3.42%, p = 0.001, n = 72 from 7 cats). These results suggest that the potentiation effect could be transferred from optimal pattern condition to nonoptimal pattern condition. It is notable that according to the previous literature (Saul and Cynader, 1989), the suppressive orientation-adaptation effect is transferable over a broad range of spatial frequency (at least ±2 octaves from preferred spatial frequency), and the transferable potentiation effect in our result (±1 octave from preferred spatial frequency) is similar to that in the previous literature.

In addition to spatial frequency, eye preference was also examined. Previous literature has shown that the orientation-adaptation effect could transfer interocularly in cat Areas 17 and 18 (Maffei et al., 1986). Considering that neurons in Areas 17 and 18 are mainly binocularly driven (Hubel and Wiesel, 1962; Maffei et al., 1986; Kasamatsu and Imamura, 2020), whereas LGN neurons are monocularly driven (Sanderson, 1971; LeVay and Ferster, 1977; Weyand, 2016; Ghodrati et al., 2017), interocular transferability supports the cortical origin hypothesis of orientation adaptation.

To investigate the origin (cortex or subcortex) of potentiation, we then examined its interocular transferability. The ODI of tested Area 17 neurons is 0.15 ± 0.06 (Fig. 2E; n = 26), consistent with previous studies (Hubel and Wiesel, 1962; Wiesel and Hubel, 1963; Kara and Boyd, 2009; Kasamatsu and Imamura, 2020) showing a binocular-driven feature. The interocular transferability test consists of several steps. First, we separately recorded preadaptation Area 17 neuronal responses of each eye (with the other eye covered) using Control stimulus. Second, we recorded neuronal response of the Trained Eye to biased-adaptation stimulus (Passive Eye covered). Third, we immediately recorded neuronal response of the Passive Eye to Control stimulus (Trained Eye covered). In this way, we examined the possibility that the biased-adaptation aftereffect on Area 17 neurons could be induced by one eye and be exhibited by the other eye.

As expected, we successfully reproduced potentiation of individual Area 17 neurons by solely stimulating the Trained Eye with biased adaptation (biased-adaptation probability = 0.21). More interestingly, the responses of these tested neurons driven by the Passive Eye (which only received Control stimuli) also exhibited potentiation (Fig. 2F). Population statistics of potentiation-exhibiting neurons (18/26 neurons) showed that (Fig. 2G) when biased-adaptation probability was <0.32, neuronal responses to the Trained Eye and the Passive Eye both exhibited potentiation (Trained Eye, 26.48 ± 4.14%, p = 2 × 10⁻⁵, n = 18; Passive Eye, 10.33 ± 5.73%, p = 0.0675, n = 18) significantly or nonsignificantly; when biased-adaptation probability equaled 0.79, neuronal responses to the Trained Eye and the Passive Eye were both significantly suppressed (Trained Eye, −19.68 ± 6.53%, p = 0.006, n = 18; Passive Eye, −12.67 ± 4.25%, p = 0.015, n = 16; tests of two neurons were skipped because of unsuccessful recovery; see above, Materials and Methods). Proportion analysis showed that among tested potentiation-exhibiting neurons, 72.22% (13/18 neurons) could exhibit significant potentiation when exposed to the Passive Eye. We noticed that when BAP was <0.32, tested neuronal responses to the Passive Eye exhibited nonsignificant potentiation. This was because of the large variation caused by a few (11.11%, 2/18) strongly suppressed neurons (whose peak response change ratios were less than −20%). The above results suggested the overall interocular transferability of potentiation effect.

Moreover, there is no significant difference in ODI between interocular transferable and the rest of the neurons (ODI_transferable = 0.225 ± 0.082, ODI_rest = 0.084 ± 0.092; p = 0.264, two-sample Student's t test; n_transferable = 13, n_rest = 13).

The above results suggest that the potentiation of Area 17 neurons induced by biased-adaptation protocol might originate within primary visual cortex rather than LGN. To examine this idea, we next performed latency analysis.

Bidirectional plasticity arises from the antagonistic competition of adaptation of the receptive-field

Response-latency analysis reflects the primary origin of neural inputs and has been extensively applied (Nowak et al., 1995; Angelucci and Bullier, 2003; Patterson et al., 2013; Yao et al., 2015). Neurons in Area 17 primarily receive inputs from three origins, the excitatory feedforward projections from subcortical (mainly LGN) regions, the inhibitory or excitatory horizontal connections from local cortical circuits, and the excitatory feedback projections from higher visual cortices such as Area 21a (Huang et al., 2004; Tong et al., 2011). Their effects are mainly reflected within corresponding response-latency time window; neural responses corresponding to feedforward, horizontal, and feedback inputs are primarily reflected in the early (50 ∼ 100 ms), middle (100 ∼ 200 ms), and late (>200 ms) epoch components from stimulus onset, respectively (Nowak et al., 1995; Angelucci and Bullier, 2003; Bair et al., 2003; Xing et al., 2011; Henry et al., 2013; Patterson et al., 2013; Shapley and Xing, 2013; Yao et al., 2015). Therefore, in this study, we examined how neural peak responses change with biased-adaptation probabilities during early (50 ∼ 100 ms), middle (100 ∼ 200 ms), and late (200 ∼ 500 ms) epochs. Considering the potential response-latency variation and considerable proportion of slow responsive (latency >100 ms) neurons (Raiguel et al., 1989; Nowak et al., 1995), during each epoch, nonresponsive neurons were identified as outliers and were excluded (see above, Materials and Method).

We found that after biased-adaptation, neurons in Area 17 behaved differently during early, middle, and late epochs. During the early epoch (Fig. 3A), population statistics of analyzed neurons did not exhibit significant response change at BAP 0.12 ∼ 0.45 (BAP 0.12, 20.60 ± 34.60%, p = 0.759, n = 9; BAP 0.18, −30.80 ± 20.50%, p = 0.352, n = 8; BAP 0.21, 3.30 ± 14.50%, p = 0.938, n = 21; BAP 0.28, −38.50 ± 14.10%, p = 0.061, n = 10; BAP 0.31, 0.30 ± 19.00%, p = 0.989, n = 21; BAP 0.45, −10.70 ± 17.60%, p = 0.759, n = 23) but only showed significant suppression at 0.48 and >0.6 (BAP 0.48, −63.80 ± 10.10%, p = 8 × 10⁻⁴, n = 10; BAP > 0.6, −38.87 ± 9.85%, p = 0.004, n = 22).

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Figure 3.

Latency analysis and RF structure dependence of bidirectional-plasticity in Area 17. A, Population peak response change ratios of tested Area 17 neurons during the early epoch of biased-adaptation stimulation. B, Population peak response change ratios of tested Area 17 neurons during the middle epoch of biased-adaptation stimulation. C, Population peak response change ratios of tested Area 17 neurons during the late epoch of biased-adaptation stimulation. D, Peak response (change) ratios of Area 17 neurons exposed to RF-center-only (1.5–2.5° in diameter) stimulation. Top left, Schematic diagram of stimulation field. Bottom left, Peak response curves of two typical neurons. Right, Population peak response change ratios of Area 17 neurons. Blue bars indicate RF-center-only condition, whereas the gray bars are from both RF center and surround (adopted from Fig. 1H). E, same with D, under RF-surround-only condition (annular, 4° internal and 10° external in diameter). Blue bars in right figure indicate RF-surround-only condition. Error bars indicate SEM. F, RS/FS classification analysis. Left, Spike waveform examples of RS/FS neurons. Right, Proportions of potentiation-exhibiting or adaptation-only neurons among RS (n = 54)/FS (n = 51) neurons. G, Simple/Complex classification analysis. Left, PSTH examples of Simple/Complex cells. Right, Proportions of potentiation-exhibiting or adaptation-only neurons among Simple (n = 44)/Complex (n = 61) cells; ***p < 0.001, **p < 0.01, *p < 0.05. n.s.: not significant (p ≥ 0.05).

During the middle-epoch (Fig. 3B), population statistics of analyzed neurons exhibited slight potentiation (not significant) at BAP 0.12 (BAP 0.12, 70.10 ± 29.90%, p = 0.077, n = 22) and significant potentiation at BAP 0.18 ∼ 0.21 (BAP 0.18, 92.70 ± 33.90%, p = 0.048, n = 23; BAP 0.21, 42.90 ± 15.20%, p = 0.048, n = 29) but did not exhibit significant response change when BAP ≥ 0.28 (BAP 0.28, 10.90 ± 21.50%, p = 0.661, n = 22; BAP 0.31, 8.54 ± 7.68%, p = 0.521, n = 33; BAP 0.45, 8.67 ± 9.98%, p = 0.521, n = 45; BAP 0.48, −12.40 ± 13.90%, p = 0.521, n = 21; BAP > 0.6, −5.60 ± 12.60%, p = 0.661, n = 50).

During the late-epoch (Fig. 3C), population statistics of analyzed neurons exhibited slight potentiation (not significant) at BAP 0.12 (26.30 ± 14.30%, p = 0.090, n = 24) and significant potentiation at BAP 0.18 (38.50 ± 11.50%, p = 0.008, n = 24); these neurons showed no significant response change at BAP 0.21 ∼ 0.31 (BAP 0.21, 3.19 ± 6.49%, p = 0.626, n = 31; BAP 0.28, 14.20 ± 7.60%, p = 0.090, n = 24; BAP 0.31, −10.32 ± 4.75%, p = 0.059, n = 35) and were significantly suppressed when BAP ≥ 0.45 (BAP 0.45, −16.83 ± 5.42%, p = 0.008, n = 34; BAP 0.48, −18.36 ± 4.98%, p = 0.004, n = 23; BAP > 0.6, −26.14 ± 4.74%, p = 2 × 10⁻⁶, n = 53). The proportions of potentiation-exhibiting neurons (among responsive neurons) when BAP < 0.32 during early, middle, and late epochs are 47.22, 64.62, and 58.82%, respectively.

The results above showed that the potentiation effect seemed to appear in the middle epoch, in which the intracortical horizontal connections started to play major role. As a previous study suggested, horizontal connections drive major neural responses of RF extended surround (Li and Li, 1994; Li et al., 2001; Angelucci et al., 2017; Wang et al., 2020). Thus, we next examined the detailed receptive field property of bidirectional plasticity.

Neurons in Area 17 were stimulated by the same biased-adaptation protocol under two conditions (Fig. 3D,E, top left), annular (4° internal and 10° external diameter) and small (1.5–2.5° diameter) stimulation fields. The annular or small stimulation field was designed to only cover the surround part (RF extended surround, the same below) or center part (classical RF, the same below) of neural RF, respectively (Durand et al., 2007). The biased-adaptation probability gradient was set to 0.21, 0.31, 0.45, and 0.79 to investigate the difference in bidirectional plasticity patterns under different stimulation field conditions.

We found that under small stimulation field (1.5 ∼ 2.5° diameter) condition, tested individual Area 17 neurons only exhibited suppression effects (Fig. 3D, bottom left), similar to classical adaptation. Population statistics showed identical results (Fig. 3D, right); tested neurons were significantly suppressed at all tested biased-adaptation probabilities (BAP 0.21, −11.01 ± 4.84%, p = 0.017; BAP 0.31, −13.77 ± 4.06%, p = 0.004; BAP 0.45, −16.99 ± 3.81%, p = 6 × 10⁻⁴; BAP 0.79, −26.37 ± 4.37%, p = 2 × 10⁻⁵; n = 20). This result is distinct from our previous findings (using large stimulation-field, 7° diameter) that Area 17 neurons exhibited bidirectional plasticity (Fig. 1H, and Fig. 3D, right, gray bars), suggesting that the inhibitory RF surround is necessary for the generation of potentiation effect induced by biased adaptation.

Next, we used annulus stimulation field to only adapt the RF surround with biased adaptation and found that tested individual neurons only exhibited a suppression effect (Fig. 3E, bottom left). Population statistics showed identical results (Fig. 3E, right); tested neurons did not exhibit significant response change at BAP 0.21 (−1.51 ± 3.12%, p = 0.317, n = 22) but were significantly suppressed when BAP ≥ 0.31 (BAP 0.31, −9.50 ± 3.28%, p = 0.007; BAP 0.45, −15.08 ± 4.30%, p = 0.004; BAP 0.79, −14.18 ± 4.98%, p = 0.007; n = 22).

These results demonstrate that the bidirectional modulation of neural response requires the interaction of RF center and surround subareas. The bidirectional-plasticity might come from the competition of suppressive adaptation effects between RF center and antagonistic surround, reflecting the balance between inhibition and disinhibition (excitation) effects, which has been reported in traditional topping-up adaptation studies (Dragoi and Sur, 2000; Patterson et al., 2013; Solomon and Kohn, 2014).

Furthermore, we also did neuron-type (regular/fast-spiking and simple/complex; see above, Materials and Methods) classifications but found no remarkable classification specificity (Fig. 3F,G); proportions of potentiation-exhibiting neurons among RS and FS neurons are 70.37% (38/54) and 66.67% (34/51), respectively, and proportions of potentiation-exhibiting neurons among simple and complex neurons are 72.73% (32/44) and 65.57% (40/61), respectively.

Hierarchical propagation of bidirectional-plasticity in visual thalamo–ventral pathway

Previous works have shown that the adaptation of orientation could propagate through visual ventral pathway (Li et al., 2017). In this study, we asked in addition to Area 17, whether this bidirectional orientation adaptation effect coexists in other regions of visual system and could propagate along the visual thalamo–ventral pathway like classical orientation adaptation does. To answer this question, we conducted biased-adaptation research in LGN and Area 21a of cats.

We first stimulated LGN neurons with Control protocol, sinusoidal drifting gratings of 12 or 24 evenly distributed directions. Gratings were presented for 0.5 s with 0.5 s blank intervals. Similar to the previous literature (Daniels et al., 1977; Shou and Leventhal, 1989; Zhou et al., 1995; Ghodrati et al., 2017), LGN neurons exhibited weak orientation sensitivity (OB = 0.14 ± 0.01, n = 71 from two cats; see above, Materials and Methods) under cutoff spatial frequencies using mean response or FFT H1 component as criteria (see above, Materials and Methods). Then we stimulated LGN neurons with biased-adaptation protocols (12 or 24 directions, leading to two sets of BAP, see above, Materials and Methods). Adaptors were chosen at neuronal preferred orientations, and the biased-adaptation probability gradient was set from 0.28 to 0.92.

We found that tested individual LGN neurons exhibited response potentiation at BAP 0.28 ∼ 0.78. This potentiation effect faded away from BAP 0.73–0.84, suggesting a further suppression trend (Fig. 4A,B). Population statistics of tested neurons showed identical results (Fig. 4C); tested LGN neurons were nonsignificantly potentiated at BAP 0.28 ∼ 0.64 (BAP 0.28, 15.00 ± 9.41%, p = 0.108, n = 36; BAP 0.48, 21.30 ± 11.10%, p = 0.093, n = 36; BAP 0.64, 13.85 ± 8.09%, p = 0.108, n = 36), and exhibited significant potentiation at BAP 0.73 ∼ 0.78 (BAP 0.73, 27.09 ± 8.40%, p = 0.009, n = 36; BAP 0.78, 38.20 ± 12.00%, p = 0.009, n = 33). These neurons did not exhibit significant response change at BAP 0.84 ∼ 0.92 (BAP 0.84, 7.3 ± 15.0%, p = 0.632, n = 21; BAP 0.85, 5.86 ± 8.86%, p = 0.577, n = 35; BAP 0.88, −7.80 ± 11.80%, p = 0.577, n = 35; BAP 0.92, −13.80 ± 12.10%, p = 0.393, n = 31) but suggested the tendency of further suppression. The difference in sampled neuron numbers was because of different direction numbers used during stimulation (resulting in different BAPs), as well as objective and strict criteria (see above, Materials and Methods).

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Figure 4.

Bidirectional plasticity coexists in LGN and Area 21a with hierarchical progressive patterns. A, B, Two typical LGN neurons exposed to biased-adaptation stimulation. For each case, orientation tuning curves of two typical cases, colors of lines indicate biased-adaptation probabilities, arrows indicate the orientations of adaptors (left); trained response curves of two typical cases (right). C, Population trained response change ratios of LGN neurons exposed to biased-adaptation stimulation. D, Proportion of potentiation-exhibiting, adaptation-only, and unclassified neurons in LGN. E, Population trained response change ratios of tested LGN neurons during early, middle, and late epoch of biased-adaptation stimulation. F, Population trained response change ratios of LGN neurons with the lesion of Area 17. G, H, Two typical Area 21a neurons exposed to biased-adaptation stimulation. For each case, orientation tuning curves of two typical cases; colors of lines indicate biased-adaptation probabilities, and arrows indicate the orientations of adaptors (left); peak response curves of two typical cases (right). I, Population peak response change ratios of Area 21a neurons exposed to biased-adaptation stimulation. J, Proportion of potentiation-exhibiting, adaptation-only, and stable neurons in Area 21a. K, Population peak response change ratios of tested Area 21a neurons during early, middle, and late epoch of biased-adaptation stimulation. L, Population peak/trained response change ratio curves of LGN, Area 17, and Area 21a shown in one figure for comparison. Data are adopted from Figures 1H and 4, C and I. Colored lines indicate brain regions. Error bars indicate SEM; ***p < 0.001, **p < 0.01, *p < 0.05. n.s.: not significant (p ≥ 0.05).

Together, we found that LGN neurons could also be bidirectional-plastic under biased-adaptation protocol but with a distinct manner comparing with cortical neurons. The switch threshold of potentiation and suppression (BAP > 0.84) is much higher than that of cortical neurons (BAP > 0.31), which is unexpected but reasonable (see below, Discussion). The proportion of potentiation-exhibiting neurons in LGN is 87.32%, higher than in Area 17 neurons (Fig. 4D).

Previous researches have revealed that massive (31 ∼ 58%) inputs to LGN neurons are feedback projections from Area 17 (Guillery, 1969; Montero, 1991; Erişir et al., 1998; Van Horn et al., 2000). Using the latency analysis method, we attempted to examine the origin of LGN bidirectional-plasticity. Time windows were chosen based on the previous literature (Troy and Lennie, 1987; Lu et al., 1995; Guido and Sherman, 1998); the early epoch (30 ∼ 80 ms) components reflect the inputs from retina and LGN interneurons, whereas the middle epoch (80 ∼ 200 ms) and late epoch (200 ∼ 500 ms) components mainly reflect feedback projections from Area 17 (directly or indirectly). Considering the potential response-latency variation and considerable proportion (approximately half) of slow-responsive (latency > 80 ms) LGN neurons (Troy and Lennie, 1987; Lu et al., 1995; Guido and Sherman, 1998), during each epoch, nonresponsive neurons were identified as outliers and were excluded (see above, Materials and Methods).

During the early epoch (Fig. 4E, left), responses of analyzed LGN neurons did not significantly changed at all tested BAPs (BAP 0.28, 9.80 ± 16.30%, p = 0.627, n = 20; BAP 0.48, 25.70 ± 21.90%, p = 0.627, n = 20; BAP 0.64, 16.30 ± 17.10%, p = 0.627, n = 20; BAP 0.73, 30.00 ± 26.30%, p = 0.627, n = 20; BAP 0.78, 17.60 ± 19.30%, p = 0.627, n = 19; BAP 0.84, 11.60 ± 18.00%, p = 0.627, n = 13; BAP 0.85, −4.40 ± 26.90%, p = 0.873, n = 15; BAP 0.88, −37.70 ± 15.10%, p = 0.234, n = 15; BAP 0.92, −16.50 ± 22.80%, p = 0.627, n = 13). Repeated p values were because of the false discovery rate method used for multiple comparison corrections.

During the middle epoch (Fig. 4E, middle), responses of analyzed LGN neurons exhibited significant potentiation at BAP 0.28 ∼ 0.78 (BAP 0.28, 29.40 ± 10.80%, p = 0.014, n = 36; BAP 0.48, 26.23 ± 9.96%, p = 0.014, n = 34; BAP 0.64, 42.90 ± 11.70%, p = 0.007, n = 36; BAP 0.73, 38.50 ± 12.80%, p = 0.009, n = 36; BAP 0.78, 30.90 ± 15.00%, p = 0.043, n = 32), and exhibited slight nonsignificant potentiation at BAP 0.84 ∼ 0.85 (BAP 0.84, 33.20 ± 18.80%, p = 0.071, n = 20; BAP 0.85, 16.80 ± 12.30%, p = 0.113, n = 30). Tested neurons exhibited nonsignificant suppression at BAP 0.88 ∼ 0.92 (BAP 0.88, −12.20 ± 12.00%, p = 0.318, n = 30; BAP 0.92, −23.40 ± 13.70%, p = 0.113, n = 27).

During the late epoch (Fig. 4E, right), responses of analyzed LGN neurons exhibited significant potentiation at BAP 0.28 ∼ 0.78 (BAP 0.28, 25.40 ± 8.01%, p = 0.004, n = 36; BAP 0.48, 32.14 ± 9.74%, p = 0.003, n = 36; BAP 0.64, 37.50 ± 10.20%, p = 0.003, n = 36; BAP 0.73, 47.70 ± 12.70%, p = 0.003, n = 36; BAP 0.78, 42.80 ± 13.80%, p = 0.004, n = 33). These analyzed neurons exhibited nonsignificant potentiation at BAP 0.84 ∼ 0.85 (BAP 0.84, 20.10 ± 13.90%, p = 0.091, n = 21; BAP 0.85, 12.62 ± 9.77%, p = 0.102, n = 35) and significant suppression at BAP 0.88 ∼ 0.92 (BAP 0.88, −14.43 ± 6.01%, p = 0.014, n = 34; BAP 0.92, −26.66 ± 9.82%, p = 0.009, n = 28).

Proportional analysis also showed a latency-related pattern; according to the same criteria in Area 17 (a neuron was considered to exhibit potentiation when its response significantly increased after the biased-adaptation stimulation), we found in LGN, 42.86% (15/35) of responsive neurons exhibited potentiation at BAP 0.28 ∼ 0.78 during the early epoch, however 53.03% (35/66) and 66.20% (47/71) of responsive neurons exhibited potentiation at BAP 0.28 ∼ 0.78 during the middle and late epoch, respectively. We noticed that during the early epoch, although as a population tested neurons did not exhibit statistically significant potentiation in their population averaged response, individual neurons in LGN showed great divergence. A strong potentiation effect (trained response change ratio > 100%) could be found in a few neurons (17.14%, 6/35), whereas the rest of neurons exhibited either suppression (−57.14%, 20/35) or moderate potentiation (25.71%, 9/35), leading to high variance of statistical results. This divergence might reflect more complicated circuit origins in addition to cortical feedback. Considering that the superior colliculus (SC) could encode visual saliency before V1 (White et al., 2017) and project to LGN (Liu et al., 2022), it is possible that the potentiation of some LGN neurons during the early epoch might be influenced by the SC (see below, Discussion).

These latency-related findings altogether suggested that the bidirectional-plasticity property of LGN neurons might derive from the feedback projection of Area 17, consistent with our findings that bidirectional-plasticity started to appear during the middle epoch in Area 17. However, subcortical (like superior colliculus) mechanisms may also contribute to the early epoch potentiation of some LGN neurons.

To further clarify the cortical origin hypothesis, we inactivated Area 17 with liquid nitrogen (see above, Materials and Methods) to remove cortical feedback and then examined the effect of biased adaptation in LGN neurons. We set the BAP gradient (0.31 ∼ 0.92) and found that after cortical lesion, distinct from previous experimental results (Fig. 4C), tested LGN neurons did not exhibit potentiation effect but only showed adaptive suppression (Fig. 4F). Population statistics showed that tested neurons did not exhibit a significant response change at BAP 0.31, 0.45 or 0.88 (BAP 0.31, 2.44 ± 6.04%, p = 0.692; BAP 0.45, −5.61 ± 5.29%, p = 0.461; BAP 0.88, 4.78 ± 6.48%, p = 0.568; n = 15) but showed slight suppression (not significant) at BAP 0.79 ∼ 0.85 (BAP 0.79, −10.02 ± 4.86%, p = 0.058; BAP 0.85, −9.35 ± 4.12%, p = 0.058; n = 15) and significant suppression at BAP 0.92 (−30.55 ± 5.89%, p = 6 × 10⁻⁴, n = 15). This lesion experiment demonstrates that the bidirectional-plasticity in LGN might be derived from the feedback projection of Area 17.

In Area 21a we first recorded neurons with Control protocol, sinusoidal drifting gratings of 12 evenly distributed directions. Gratings were presented for 0.5 s with 0.5 s blank intervals. Comparing them with Area 17 neurons, Area 21a neurons also exhibited strong orientation selectivity (OSI = 0.40 ± 0.03, n = 77), and the average preferred spatial frequency is lower [0.37 cycle per degree (cpd), n = 77] than Area 17 (1.08 cpd, n = 105). These properties of recorded neurons in Area 21a are consistent with the previous literature (Dreher et al., 1993; Morley and Vickery, 1997).

Next, we stimulated Area 21a neurons with biased-adaptation protocol (biased-adaptation probability gradient 0.15 ∼ 0.79; a finer BAP gradient was used during the second half of the experiment, leading to different neuron numbers; see above, Materials and Methods) and found the bidirectional-plasticity feature in tested individual neurons (Fig. 4G,H). Population statistics showed identical results (Fig. 4I); tested Area 21a neurons exhibited significant potentiation at BAP 0.15 (10.16 ± 3.88%, p = 0.045, n = 37) and were significantly suppressed at BAP 0.79 (−19.71 ± 5.20%, p = 0.007, n = 30); tested Area 21a neurons did not exhibit significant response change at BAP 0.21 ∼ 0.66 (BAP 0.21, 3.13 ± 3.35%, p = 0.618, n = 77; BAP 0.27, −0.67 ± 5.32%, p = 0.901, n = 37; BAP 0.31, 1.27 ± 6.04%, p = 0.901, n = 73; BAP 0.45, −2.60 ± 4.67%, p = 0.811, n = 65; BAP 0.66, −8.23 ± 7.15%, p = 0.600, n = 39) but suggested the tendency that higher adaptation intensity would induce stronger suppression. Proportion analysis showed that majority of Area 21a neurons (64.94%, 50/77) exhibited potentiation after exposed to biased-adaptation stimulation (Fig. 4J).

Latency analysis of Area 21a neurons was performed to examine the potential origin of Area 21a neuronal bidirectional-plasticity. Responses of analyzed Area 21a neurons during biased adaptation were separated into three epoch components suggested by previous reports (Lee et al., 2007; Sundberg et al., 2012), the early epoch (50 ∼ 100 ms), the middle epoch (100 ∼ 200 ms), and the late epoch (200 ∼ 500 ms). Considering the potential response-latency variation and considerable proportion of slow-responsive (latency > 100 ms) Area 21a neurons (Lee et al., 2007; Sundberg et al., 2012), during each epoch nonresponsive neurons were identified as outliers and excluded.

During the early epoch (Fig. 4K, left), population statistics of analyzed neurons showed nonsignificant potentiation at BAP 0.15∼0.27 (BAP 0.15, 49.40 ± 26.00%, p = 0.128, n = 20; BAP 0.21, 25.70 ± 21.00%, p = 0.319, n = 40; BAP 0.27, 19.20 ± 26.00%, p = 0.493, n = 20). These neurons did not exhibit a significant response change at BAP 0.31 (−8.90 ± 12.90%, p = 0.493, n = 35). Moreover, these neurons exhibited significant suppression at BAP 0.45 ∼ 0.79 (BAP 0.45, −29.10 ± 10.20%, p = 0.019, n = 36; BAP 0.66, −49.00 ± 15.70%, p = 0.019, n = 20; BAP 0.79, −50.80 ± 14.40%, p = 0.014, n = 19).

During the middle epoch (Fig. 4K, middle), population statistics of analyzed neurons showed nonsignificant potentiation at BAP 0.15 (24.20 ± 14.20%, p = 0.117, n = 27). These analyzed neurons did not exhibit a significant response change at BAP 0.21 ∼ 0.27 (BAP 0.21, −6.88 ± 5.64%, p = 0.228, n = 54; BAP 0.27, −18.06 ± 8.25%, p = 0.053, n = 27) and exhibited significant suppression at BAP 0.31 ∼ 0.79 (BAP 0.31, −15.39 ± 6.58%, p = 0.042, n = 48; BAP 0.45, −33.32 ± 5.85%, p = 2 × 10⁻⁶, n = 47; BAP 0.66, −38.65 ± 5.88%, p = 2 × 10⁻⁶, n = 26; BAP 0.79, −40.02 ± 6.01%, p = 2 × 10⁻⁶, n = 25).

During the late epoch (Fig. 4K, right), population statistics of analyzed neurons showed no significant response change at BAP 0.15 (−1.44 ± 6.09%, p = 0.815, n = 22) as well as nonsignificant suppression at BAP 0.27 (−13.21 ± 7.10%, p = 0.091, n = 21). However, these analyzed neurons exhibited significant suppression at BAP 0.21 and 0.31 ∼ 0.79 (BAP 0.21, −14.92 ± 4.38%, p = 0.001, n = 46; BAP 0.31, −18.59 ± 4.52%, p = 4 × 10⁻⁴, n = 44; BAP 0.45, −34.87 ± 4.05%, p = 2 × 10⁻⁹, n = 37; BAP 0.66, −41.06 ± 5.06%, p = 5 × 10⁻⁷, n = 26; BAP 0.79, −45.23 ± 4.68%, p = 4 × 10⁻⁹, n = 24).

Together, the above results showed that (1) the suppression effect at high adaptation intensity could be found during all three epochs, (2) a nonsignificant potentiation effect could be found during the early and middle epochs, and (3) the extent of suppression was stronger during the middle and late epochs. Moreover, proportions of potentiation-exhibiting neurons (among responsive Area 21a neurons) at BAP 0.15 ∼ 0.21 during early, middle, and late epochs are 40.00, 46.30, and 39.58%, respectively. We noticed that during early and middle epochs, the potentiation effects of analyzed neurons are nonsignificant at BAP 0.15. This might result from the inconsistency of neuronal adaptation effects; at BAP 0.15, 35.00 and 29.63% of sampled neurons exhibited suppression during early and middle epoch, respectively. This inconsistency leads to high variance of statistical results, and these results suggest the inconsistency and complexity of Area 21a neuronal effects of biased adaptation.

The bidirectional-plasticity pattern in Area 17 and Area 21a are slightly different. Area 21a neurons have a disposition to exhibit potentiation at lower BAPs than Area 17 neurons; according to population statistics, at BAP 0.21, Area 21a neurons could not exhibit significant response change (Fig. 4I), whereas Area 17 neurons could exhibit significant potentiation (Fig. 1H). This result suggested that compared with Area 17 neurons, potentiation of Area 21a neurons requires lower biased-adaptation intensity.

The latency-analysis results of LGN and Areas 17 and 21a neurons also suggested an interesting tendency (Figs. 3A,B,C, 4E,K). Although during the early epoch, the response change patterns of neurons in LGN and Areas 17 and 21a were noisy, there was still a noticeable hierarchical trend during the middle and late epochs; from LGN to Area 21a, the potentiation effect gradually diminished, and the suppression effect was enhanced. During the middle epoch, the maximal significant potentiation inducing BAP was 0.78 for LGN and 0.21 for Area 17, whereas there was only nonsignificant potentiation at BAP 0.15 for Area 21a; during the late epoch, the minimal significant suppression inducing BAP was 0.88 for LGN, 0.45 for Area 17, and 0.21 for Area 21a.

In summary, our findings demonstrate universally existing bidirectional-plasticity along the visual thalamo–ventral pathway, with progressive hierarchical propagation pattern. The bidirectional-plasticity properties of LGN and Areas 17 and 21a neurons show a progressively changing pattern (Fig. 4L). The BAPs required for inducing potentiation and suppression are lower for neurons of higher visual areas. Moreover, the proportion of potentiation-exhibiting neurons in tested LGN, Area 17, and Area 21a neurons are 87, 70, and 65%, respectively. Together, we found that bidirectional-plasticity induced by biased adaptation exists in LGN, Area 17, and Area 21a with progressive pattern changes.

Modeling of the origin and functional significance of bidirectional-plasticity

As shown in our above experimental RF-structure data, biased-adaptations suppress (adapt) both the neural response to RF center (classical RF, the same below) and the one to surround (RF extended surround, the same below) subareas. We hypothesized that suppression of the RF excitatory center leads to inhibition of neural responses, whereas suppression of the RF inhibitory surround leads to disinhibition (excitation). The balance between excitation and inhibition can be reversed as the biased-adaptation strength increases, thus causing bidirectional-plasticity of overall neuronal response. Modeling of this RF center-surround adaptation-competition was thus performed (see above, Materials and Methods) to quantitatively simulate this effect.

Receptive-field adaptation competition model

The computing simulation model is modified from the classical DoG model (Marr and Hildreth, 1980; Birch et al., 2010) with biased-adaptation-related features. In detail, we retrofitted this static model by adding adaptation-strength-related decaying factors to both Gaussian components, making it dynamically transformable with the strength of adaptation.

In the classical DOG model, positive and negative Gaussian components represent the spatial response tuning curves of excitatory center and inhibitory surround of RF, respectively. When the spatial control factor (σ₁) of the positive component was smaller than that of then negative component (σ₂), the summation of two Gaussian-components could exhibit classical center-surround RF structure (Fig. 5A; example parameters, a₁ = 1.0, a₂ = −2.0, σ₁ = 1, σ₂ = 5; see above, Materials and Methods).

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Figure 5.

Simulation of the receptive field adaptation competition model. A, Schematic diagram of RF center-surround components (top) and overall RF structures (bottom) before (dotted line) and after (solid line) adaptation. B, Schematic diagram of decay functions of RF center and surround components during biased adaptation. Blue line indicates the response change ratio of RF center, and red line indicates that of RF surround. C, Two typical fitting results of bidirectional plastic Area 17 neurons using the RF adaptation-competition model. Black circles indicate experimental data, whereas red curves indicate fitting result. D, Histogram of fitting quality (R²) for bidirectional plastic Area 17 neurons. E, Same as C of an adaptation-only Area 17 neuron. F, Same as D of adaptation-only Area 17 neurons. G, Fitting results of population peak response change ratio curve of Area 17 neurons using the RF adaptation competition model. Black circles indicate experimental data, whereas red curve indicates fitting result. H, The parameter spaces showing the neural peak response change ratios under different magnitude factors (decay factors fixed). Left, Parameter space at BAP 0.18. Right, Parameter space at BAP 0.79. Black solid frames indicate proper parameter ranges of each subgraph, whereas black dashed frames indicate proper parameter ranges of the other subgraph, so that there is a 0% ∼ 40% increase at BAP 0.18 and a −40% ∼ 0% change at BAP 0.79. White frames in two subgraphs indicate the intersection of proper parameter ranges. I, Same as H of decay factors (magnitude factors fixed).

After that, both positive and negative Gaussian components were set to decay with the increasing biased-adaptation probabilities. According to previous reports (Cavanaugh et al., 2002; Patterson et al., 2013), decay functions were exponentially shaped, with different decay factors (b₁ for positive Gaussian component, b₂ for negative Gaussian component) and magnitude factors (c₁ for positive Gaussian component, c₂ for negative Gaussian component) to simulate the adaptation sensitivity of RF center and surround subareas (Fig. 5B; see above, Materials and Methods). In the model, the decay speed of the negative Gaussian component is faster than that of the positive Gaussian component (b₂ > b₁); thus the suppression of RF surround is greater at low BAP. Meanwhile, the decay magnitude of the positive Gaussian component is greater than that of the negative Gaussian component (c₁ > c₂); thus the RF center component plays a dominant role at high BAP. At low BAP, the negative Gaussian component is suppressed more than the positive Gaussian component, leading to potentiation of the overall response, whereas at high BAP, the positive Gaussian component is suppressed more than the negative Gaussian component, leading to suppression of the overall response. Therefore, this model could simulate the bidirectional modulation of neural response during biased adaptation.

To verify the goodness of this model, we fitted the experimental data (peak response change ratios of Area 17 neurons) and obtained satisfying results. The bidirectional-plasticity feature of two individual Area 17 neurons could be well reproduced by this model (Fig. 5C). We fitted all the bidirectional-plastic neurons with sufficient data points (≥4), and a population distribution of R² (0.86 ± 0.02, n = 59) suggests overall goodness of fit (Fig. 5D). Moreover, experimental results showed a minority (27%) of Area 17 neurons exhibited the adaptation-only feature after biased adaptation, and we found this model could also reproduce the peak response change ratios of the adaptation-only neuron (Fig. 5E) with acceptable goodness (Fig. 5F; R² = 0.89 ± 0.03, n = 19) by setting the magnitude factor of the negative Gaussian component (c₂) to minimum, which is equivalent to removing the RF surround modulation effect. These findings suggest the effectiveness of the model in simulating the experimental bidirectional-plasticity feature as well as its compatibility with classical suppressive-only adaptation effects.

To further investigate effectiveness of this model in reproducing experimental population results and to determine corresponding optimal simulation parameters, we fitted the experimental population peak response change ratios of Area 17 neurons (Fig. 1H) with this model and obtained satisfying results (Fig. 5G). The corresponding optimal simulation parameters are (1) gain control factors of RF Gaussian components, a₁ = 1.0, a₂ = −2.0; (2) spatial control factors of RF Gaussian-components, σ₁ = 1, σ₂ = 5; (3) decay factors of decay functions, b₁ = 6.56, b₂ = 16.19; and (4) magnitude factors of decay functions, c₁ = 22.56, c₂ = 20.13 (see above, Materials and Methods).

To explore the relationship between parameter settings and the bidirectional-plastic modulation effect, we analyzed the parameter spaces focusing on the impact of magnitude factors (c) and decay factors (b) on neural peak response change ratios (%), especially at low (BAP = 0.18) and high (BAP = 0.79) adaptation intensities. Proper parameter settings should reproduce the experimental result that bidirectional-plastic neurons were moderately potentiated at BAP 0.18 and moderately suppressed at BAP 0.79. Therefore, we examined the parameter spaces to ascertain if the parameters were meeting the above requirements by finding the intersection of parameters that could both induce moderate potentiation (0 ∼ 40%) and moderate suppression (−40 ∼ 0%) at BAP 0.18 and 0.79, respectively.

By fixing the decay factors at the optimal condition for population simulation (b₁ = 6.56, b₂ = 16.19), we calculated the neural peak response change ratios under different magnitude factors (c₁ and c₂, range = 15∼25, step = 0.2) and found proper magnitude factors for inducing moderate potentiation (Fig. 5H, left, black solid frame) and moderate suppression (Fig. 5H, right, black solid frame). The intersection of proper magnitude factors (Fig. 5H, white frames) is suitable for modeling the experimental results.

By fixing the magnitude factors at the optimal condition for population simulation (c₁ = 22.56, c₂ = 20.13), we calculated the neural peak response change ratios under different decay factors (b₁ and b₂, range = 0 ∼ 20, step = 0.4) and found proper decay factors for inducing moderate potentiation (Fig. 5I, left, black solid frame) and moderate suppression (Fig. 5I, right, black solid frame). The intersection of proper decay factors (Fig. 5I, white frames) is suitable for modeling the experimental results.

Together, the above simulation results provide evidence supporting the goodness of our RF center-surround adaptation-competition hypothesis.

Saliency detection

Saliency detection is essential for information processing. During this process, the novelty of a specific element within a complex background could be quantified by occurrence probability; higher probability indicates higher ordinariness (redundancy, which needs to be suppressed), whereas lower probability indicates higher novelty (which needs to be potentiated). Within a time window, the occurrence-probability-dependent bidirectional modulation of neural responsiveness we found in this work would lead to response potentiation of novel stimulus and response suppression of redundant stimulus. As a result, it will help the temporal novelty detection. We thus hypothesize that the short-term bidirectional-plasticity in visual thalamo–ventral pathway may provide the neural mechanism of saliency detection (Li, 2002, 2019) of temporal-statistically distributed visual stream inputs.

To verify this hypothesis, we performed simulation of saliency detection, based on occurrence-probability-dependent short-term bidirectional-plasticity properties of Area 17 neurons. According to saliency detection theory, the saliency value of each visual location was calculated as its corresponding neural response intensity relative to the highest response intensities of other surrounding locations (Li, 2019), and the saliency values of the entire visual field could form a saliency map. Therefore, by temporally modulating relative neural responses during a period of time (based on the occurrence-probability-dependent neural bidirectional-plasticity), saliency detection of dynamic visual inputs could be performed.

In simulation, neuron clusters (whose population RFs covered the entire visual field) were exposed to a visual stream frame by frame (each frame lasted for 0.5 s with 0.5 s blank intervals, identical to experimental protocol) composed of three successive phases (each lasting for 100 frames), initial noise, detection, and ending noise (Fig. 6). During the initial and ending noise phases, neuron clusters received randomized unbiased orientation stimuli of bars as a simulation of noisy context visual stream inputs. During the detection phase, a stable elliptical contour repetitively (frame by frame) appeared in the visual field, thus modulating the responses of corresponding neuron clusters. Over time, the cumulative occurrence probabilities of stimulating bars gradually changed and induced modulation of neural responses because of V1 bidirectional-plasticity properties revealed by experiments (bidirectional-plasticity function obtained by the RF center-surround adaptation-competition model described above; see above, Materials and Methods). The sliding time window used for calculating occurrence probability and its related neural response modulation is 10 min, suggested by the experimental bidirectional-plasticity time course (Fig. 1K).

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Figure 6.

Saliency maps (Area 17) during dynamic visual inputs. A, Initial noise phase (0 ∼ 100 s). B, Detection phase (100 ∼ 200 s). C, Ending noise phase (200 ∼ 300 s). Each sample consists of two parts, the left part is visual input, and the right part is a corresponding saliency map. Duration of each stimulus is 0.5 s with a 0.5 s blank interval (300 stimuli last for 300 s, and each phase lasts 100 s). Full video can be found in Movie 1.

During the initial and ending noise phases, no particular change occurred in the saliency map (Fig. 6A,C). However, during the detection phase (Fig. 6B, Movie 1), the saliency values continuously changed at locations where the elliptical contour appeared (because the occurrence probabilities of corresponding locations kept refreshing and accumulating frame by frame, and thus the one of contour orientation increased with time). During 0 ∼ 20 s (0 ∼ 20 frames) after stimulus onset, the occurrence probability gradually increased from 0 to 0.18, and meanwhile the relative saliency values of stimulus also increased because of the potentiating modulation of the bidirectional-plasticity function (Fig. 6B; from 100 s to 120 s). During 20 ∼ 30 s (20 ∼ 30 frames) after stimulus onset, the occurrence probability continuously increased from 0.18 to 0.26, and meanwhile the relative saliency values of the stimulus decreased to the context level (Fig. 6B; 130 s). During 30 ∼ 100 s (30 ∼ 100 frames) after stimulus onset, the occurrence probability increased from 0.26 to 0.52, and meanwhile the relative saliency values of stimulus further decreased because of the suppressing modulation of bidirectional-plasticity function (Fig. 6B; after 140 s). Clearly, the time-dependent bidirectional modulation enhances the novelty information of stimulus and reduces the redundant information. By detecting the changes of the saliency map, novel targets could pop up from the context, which is helpful for further selection of visual inputs. We noticed that during the ending noise phase, saliency values of some locations (that displayed elliptical contour in the detection phase) exhibited suppression when identical orientations (displayed during the detection phase) randomly reoccurred. This suppression resulted from the aftereffect of the detection phase, which was maintained during the ending noise phase.

To further explore the difference in the saliency detection property between Area 17 and LGN, we repeated this simulation with the LGN version of bidirectional-plasticity function (obtained by the smoothing spline method with satisfying fitting quality of individual LGN neurons; Fig. 7A; see above, Materials and Methods). After applying this function in the saliency detection algorithm, we found that the distinct bidirectional-plastic properties of Area 17 and LGN lead to distinct time scales of saliency detecion dynamics (Movie 2, notice the time label). With a similar stimulation design (however, for LGN, detection phase was prolonged to 800 frames), we found that the dynamics of LGN neuronal saliency detection is much slower than that of Area 17. LGN neurons need more time to finalize the potentiating modulation of visual inputs, 470 s (Fig. 7C, 570 s) for LGN and 30 s (Fig. 6B, 130 s) for Area 17 (time after stimulus onset), suggesting different strategies of information filtering and coding.

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Figure 7.

Saliency detection model of LGN and Area 21a. A, Fitting result of population trained response change ratio curve of LGN neurons, using the smoothing spline method. Black circles indicate experimental data, and red curve indicates fitting result. B, Same as A of Area 21a neurons. C, Saliency maps of LGN during the detection phase of dynamic visual inputs; each sample consists of two parts, the left part is visual input, and the right part is the corresponding saliency map. Duration of each stimulus is 0.5 s with a 0.5 s blank interval. D, Same as C of Area 21a. Full video can be found in Movies 2 and 3. It is notable that in C and D, the detection phase followed a 100 s initial phase, so the detection phase started from 100 s.

We also simulated the saliency detection of Area 21a neurons to further check the potential differences across LGN and Areas 17 and 21a. The population bidirectional-plasticity function of Area 21a was fitted by the smoothing spline method instead of the RF center-surround adaptation-competition model (Fig. 7B), for it was currently unclear whether the bidirectional-plasticity of Area 21a neurons was RF structure-dependent. We found during the detection phase (Movie 3, Fig. 7D), Area 21a neurons only took ∼15 s to finalize the potentiating modulation of visual inputs (470 s for LGN, 30 s for Area 17).

Together, the short-term bidirectional-plasticity in visual thalamo–ventral pathway may contribute to the saliency detection of temporal statistically distributed visual stream inputs.