Abstract
The spatial-frequency (SF) tuning of neurons in the early visual cortex is adjusted for stimulus contrast. As the contrast increases, SF tuning is modulated so that the transmission of fine features is facilitated. A variety of mechanisms are involved in shaping SF tunings, but those responsible for the contrast-dependent modulations are unclear. To address this, we measured the time course of SF tunings of area 17 neurons in male cats under different contrasts with a reverse correlation. After response onset, the optimal SF continuously shifted to a higher SF over time, with a larger shift for higher contrast. At high contrast, whereas neurons with a large shift of optimal SF exhibited a large bandwidth decrease, those with a negligible shift increased the bandwidth over time. Between these two extremes, the degree of SF shift and bandwidth change continuously varied. At low contrast, bandwidth generally decreased over time. These dynamic effects enhanced the processing of high-frequency range under a high-contrast condition and allowed time-average SF tuning curves to show contrast-dependent modulation, like that of steady-state SF tuning curves reported previously. Combinations of two mechanisms, one that decreases bandwidth and shifts optimal SF, and another that increases bandwidth without shifting optimal SF, would explain the full range of SF tuning dynamics. Our results indicate that one of the essential roles of tuning dynamics of area 17 neurons, which have been observed for various visual features, is to adjust tunings depending on contrast.
SIGNIFICANCE STATEMENT The spatial scales of features transmitted by cortical neurons are adjusted depending on stimulus contrast. However, the underlying mechanism is not fully understood. We measured the time course of spatial frequency tunings of cat area 17 neurons under different contrast conditions and observed a variety of dynamic effects that contributed to spatial-scale adjustment, allowing neurons to adjust their spatial frequency tuning range depending on contrast. Our results suggest that one of the essential roles of tuning dynamics of area 17 neurons, which have been observed for various visual features, is to adjust tunings depending on contrast.
Introduction
Spatial frequency (SF) is a fundamental visual feature systematically represented in the striate cortex (Bonhoeffer et al., 1995; Issa et al., 2000; Nauhaus et al., 2012, 2016; Ribot et al., 2013), with neurons narrowly tuned to it (Campbell et al., 1969; Movshon et al., 1978; De Valois et al., 1982). Since many receptive field (RF) properties, such as spatial or temporal integration, depend on stimulus contrast to optimize information transmission (Shapley and Victor, 1979; Sceniak et al., 1999; Bair and Movshon, 2004), the study of contrast dependence of SF tuning and its underlying mechanisms is critical to understanding how the cortex achieves optimal SF transmission. By measuring responses to drifting sinusoidal gratings, SF tunings of monkey striate (V1) neurons were shown to depend on contrast (Sceniak et al., 2002). As contrast increased, the bandwidth of SF tuning curves increased, predominantly elevating the amplitude on their high-SF side, so that transmission of fine features is facilitated. Such contrast-dependent SF tunings were also observed in cat striate neuron data (Skottun et al., 1987).
To elucidate mechanisms that shape selectivity for a stimulus feature, it is generally useful to measure the precise time course of tunings in response to brief stimuli (Ringach et al., 1997). Using this method, previous studies have revealed that SF tunings of striate neurons are shaped by a variety of mechanisms. First, bandpass tuning curves are already observed at response onset, indicating that early thalamocortical feedforward processing contributes to the generation of SF tunings of striate neurons. Then, however, the optimal SF is often shifted toward high SF, along with a decrease in tuning width over time (Bredfeldt and Ringach, 2002; Mazer et al., 2002; Frazor et al., 2004; Nishimoto et al., 2005; Ninomiya et al., 2012; Purushothaman et al., 2014). This processing may be because of delayed feedforward excitatory processing (Frazor et al., 2004; Allen and Freeman, 2006) or intracortical inhibition (Bredfeldt and Ringach, 2002). Alternatively, a portion of neurons increases the tuning width over time (Tanaka and Ohzawa, 2020), possibly because of another mechanism for dynamically shaping RF size (Suder et al., 2002; Malone et al., 2007).
It remains to be elucidated which of the aforementioned mechanisms plays an important role in contrast-dependent modulation of SF tunings. To address this, we here measure the time courses of SF tunings of cat striate, or area 17, neurons under different contrasts, with a subspace reverse correlation analysis in which a rapidly flashed sequence of sinusoidal gratings of different SFs is presented. If differences in SF tunings between different contrasts are formed at response onset, the early feedforward processing may be sufficient to explain contrast-dependent SF tunings. Alternatively, contrast-dependent modulation may develop subsequently. In this case, determining which tuning dynamics depends on contrast is critical for specifying the mechanisms.
We found that the optimal SF shifted to a higher SF over time, with a larger shift for higher contrast. At low contrast, neurons generally decreased the bandwidth over time. At high contrast, we found a range of bandwidth dynamics that depended on the degree of the optimal SF shift. These dynamic effects always enhanced the processing of high SF range under high contrast. They could be explained by the combinations of two mechanisms: one that decreases bandwidth and shifts optimal SF, and another that increases bandwidth without shifting optimal SF.
Tuning dynamics have been shown for SF, orientation, size, and disparity tunings. They have been implicated in coarse-to-fine processing (Marr and Poggio, 1979; Menz and Freeman, 2003; Malone et al., 2007) or increase in feature selectivity (Ringach et al., 1997). However, the crucial functions of tuning dynamics remain controversial (Frazor et al., 2004). The steady-state tuning curves for these features, to some extent, depend on contrast (Sceniak et al., 1999; Alitto and Usrey, 2004). Our results indicate that one of the essential roles of tuning dynamics of striate neurons is to adjust tunings depending on contrast.
Materials and Methods
Surgery and unit recordings
All animal care and experimental protocols conformed to the guidelines established by the National Institute of Health and were approved by the Osaka University Animal Care and Use Committee. The experiments were conducted with anesthetized and paralyzed male cats. Details of the surgery and unit recordings were provided in our previous paper (Tanaka and Ohzawa, 2020).
Two adult male cats (2.5–4.0 kg) were used. Initial surgery for tracheotomy and vascular catheterization was performed under anesthesia with 2%-3.5% isoflurane. Anesthesia was then switched to thiopental sodium (Ravonal, 1.0–1.5 mg·kg−1·h−1, dissolved in a Ringer's solution, 1 ml·kg−1·h−1) to maintain anesthesia for the rest of the surgery and subsequent recording sessions. Paralysis was induced and maintained with gallamine triethiodide (Flaxedil, 10 mg·kg−1·h−1, initial dose 10–20 mg, continuous infusion). Artificial respiration was maintained with a mixture of nitrous oxide (70%) and oxygen at 20-30 strokes/min. The respiration rate and stroke volume were adjusted to maintain an end-tidal CO2 between 3.5% and 4.3%. A rectangular hole was made in the skull over the representation of area 17, and the dura was dissected to allow for penetration by the microelectrode array. Pupils were dilated with atropine sulfate (1%), and the nictitating membranes were retracted with phenylephrine hydrochloride (Neosynesin, 5%). Contact lenses of appropriate power with 4 mm artificial pupils were placed over the corneas.
We used four-shaft silicon microelectrode arrays with a total of 32 recording probes (A4 × 8_200_400_177, NeuroNexus) to record neuronal activity in area 17. Electrode signals were amplified (PBX2, gain 1000, Plexon), bandpass filtered (0.1–3 kHz), and fed into a custom-made data acquisition system, where they were A-D converted at a sampling rate of 20 kHz and saved as data files. Accurate spike sorting using a commercial software package (Offline Sorter, Plexon) and data analyses were then conducted offline with these data. In parallel, the filtered signals were processed online with reduced accuracy with a custom-built spike sorter to monitor neuronal responses to visual stimuli in real time. Details of offline spike sorting were provided in our previous study (Tanaka and Ohzawa, 2020). Herein, we briefly describe this method. Spike segments that exceeded a threshold were extracted and plotted as data points in the PC1−PC2 space of the principal component analysis. The threshold was set at 2.5-5 SD of the raw signal amplitude. If a subset of the data points appeared to form an isolated cluster, we manually drew a contour enclosing these points. Templates of the spike waves for the clusters were then calculated by averaging all the spike segments inside the contours. Next, we performed a template-matching procedure to resort the spike segments into the clusters. Clusters were taken as the activity of single units if their averaged spike amplitude was >3 times the background noise level.
Visual stimulation
Visual stimuli were displayed on a color CRT monitor (76 Hz, 1600 × 1024 pixels, 46.6 cm wide × 29.9 cm high, 47 cd·m−2, GDM-FW900, Sony). In each recording session, the luminance nonlinearity of the monitor was measured with a photometer (Minolta CS-100, Konica Minolta Photograph Imaging) and linearized using γ-correction look-up tables. Cats viewed the monitor screen through a custom-built haploscope, which allowed visual stimuli to be presented to the left and right eyes separately. A black separator was placed between the left and right visual fields to prevent stimulation of the unintended eye. The distance between the monitor screen and the eyes was set at 57 cm, subtending a visual field of 23.3° × 29.9° for each eye.
In total, we conducted three penetrations of the microelectrode arrays in two cats (two penetrations in one cat and one penetration in the other cat). After penetration, we adjusted the electrode depth to enable the recording from as many probes as possible. We waited ∼30 min before starting the recording sessions because the amplitude of the neural signals sometimes changed considerably shortly after electrode penetration. We first determined the approximate position, size, and basic tuning parameters of the classical RFs of one or two cells on each of the four electrode shafts, using small bars or small circular patches of sinusoidal grating under manual mouse control.
Next, we conducted the main recording session to accurately determine the time courses of orientation and SF tuning properties of all recorded neurons under a variety of contrasts, using a subspace reverse correlation technique (Fig. 1A) (Ringach et al., 1997). We repetitively presented rapid random sequences of sinusoidal gratings with circular patches that typically included 13 spatial frequencies, 18 orientations (0°–180° in 10° steps), four phases (0°, 90°, 180°, and 270°), and one blank stimulus. The contrast of the sequence was kept constant for each presentation of the sequence, which typically lasted for 37 s. The same stimulus sequences at three or four contrast levels between 3% and 50% were presented 10-15 times in an interleaved manner within a single recording session. We conducted three sessions in this study (one session per one electrode penetration) with different contrasts used for different sessions (first session: 6.25%, 12.5%, 25%, 50%; second session: 6.25%, 12.5%, 50%; third session: 3.125%, 6.25%, 12.5%, 50%). The flash duration of each grating in the sequence was set at 26.3 ms (two video frames); but when sufficient responses were not observed from some online-monitored cells, we reset the duration to 39.5 ms (three video frames) and restarted the recording session from the beginning. Therefore, the flash duration was always the same for the sequences of all contrast levels within a session. Two video frames were used in one session and three video frames were used in two sessions.
The size of the grating patch was adjusted to cover the RFs of all recorded cells. The range of stimulus SFs was adjusted to cover the entire frequency range that evoked responses from all recorded cells, and the distribution of the stimulus SFs was regular on a logarithmic scale (ranges: 0.1-2.0 or 2.5 cycles per degree [cpd]). We presented the stimuli monocularly. Therefore, the dominant eye was not always chosen for all cells. However, the majority of area 17 cells produce at least some response to each eye, and previous evidence indicates that SF tuning dynamics of area 17 cells are generally consistent between the two eyes (Ninomiya et al., 2012).
Data analysis
The evoked responses and the stimulus sequence of each contrast level were cross-correlated to obtain two-dimensional SF and orientation selectivity maps (SFOR maps, Fig. 1B) at correlation delays from 0 to 150 ms in 5 ms steps (phase was not discriminated). One-dimensional SF tuning curves were then calculated by integrating each time slice of the SFOR maps over a ±10° range of peak orientations. The SF tuning curves were further smoothed with a one-dimensional uniform filter (width 3) for noise reduction. We thus obtained the time course of SF tuning curves for each contrast condition (Fig. 1C). For a subset of neurons, we computed SF tuning curves at a suboptimal orientation, by integrating SFOR maps between 10° and 30° off the peak orientation.
The SF tuning curve was then fitted with the following empirical function (mixture of bandpass Gaussian and low-pass Gaussian) on a logarithmic scale as follows:
For each contrast condition, neurons were judged whether they had a reliable time course of SF tuning curves for the contrast, as follows: For each contrast, we first estimated the noise level by computing the response variances of the SFOR maps for noncausal delays from −100 to 0 ms in 5 ms steps, and then determined consecutive time points between 0 and 150 ms at which the response variance exceeded the mean of the noise level by 5 SDs, and the function fitting of
The SF corresponding to the peak position of the fitted curve
To quantify the S/N ratio of the SF tuning curves, we computed a ratio of the height (defined above) of the fitted function to the square root of the residual variance as follows:
To quantify the degree of dynamic change in the optimal SF, we calculated the SF shift as follows:
To place cells along a simple/complex cell continuum using responses obtained with a reverse correlation method, we used modulation index (MI) (Nishimoto et al., 2005; Ringach and Malone, 2007) defined as follows:
Experimental design and statistical analysis
The key values of the derived measures are often represented as mean ± SD. To compare the means of derived measures between different response times and contrasts, we used paired t test or two-sample t test (functions ttest or ttest2), and to compare the variance, we used two-sample F test (functions vartest2) using MATLAB. We provided n, p value, and mean ± SD for these tests in relevant portions of the text. Pearson correlation coefficient was represented as r.
Histology and reconstruction of the recording site
For each penetration, we passed currents of 7-10 µA for 10 s at several selected sites for histologic examination after the recordings for that penetration. We then retracted the electrode and, for one cat, reinserted the electrode at another cortical location for another recording session. After all recordings were completed, the animals were then administered an overdose of pentobarbital sodium (Nembutal or Somnopentyl, 100 mg/kg) and perfused transcardially with formalin (4% in buffered saline). The visual cortex was then sliced parasagittally at a thickness of 60 µm. Every other section was Nissl-stained with thionine and the remainder with cytochrome oxidase. From these sections, we assessed the laminar locations of the recording sites. Additional details were provided in our previous study (Tanaka et al., 2014).
Results
Cell population
Using four-shaft silicon microelectrode arrays with a total of 32 recording probes (A4 × 8_200_400_177, NeuroNexus), we conducted three recording sessions with different penetrations from cat area 17, in which we recorded the activities of neurons in response to a rapidly flashed, random sequence of sinusoidal gratings consisting of various SFs, orientations, and phases under several contrast conditions (Fig. 1A). The same stimulus sequences at three or four different contrast levels between 3% and 50% were presented 10-15 times in an interleaved manner within a single experimental session. The stimulus contrast was kept constant for each presentation of the stimulus sequence. We used different contrasts for different sessions (see Materials and Methods). Using the cross-correlation of the evoked response with the stimulus sequence, we calculated SFOR maps for the correlation delays from 0 to 150 ms in 5 ms steps for each contrast (Fig. 1B). Then, one-dimensional SF tuning curves were derived by integrating each time slice of the SFOR maps over a ±10° range of peak orientations and then smoothing it using a one-dimensional uniform filter (width 3). These curves were fitted with the following empirical function:
An example of time courses of SF tuning curves at different contrasts, using subspace reverse correlation method. Data are obtained from an area 17 cell of a cat. A, The configuration of a single recording session of the present experiment. We recorded the activities of neurons in response to a sequence of sinusoidal gratings of different SFs, orientations, and phases under different contrasts. The same stimulus sequences at three or four different contrast levels between 3% and 50% were presented 10-15 times in an interleaved manner within a single experimental session. The contrast was kept constant for each presentation of a stimulus sequence that typically lasts 37 s. The flash duration of each grating in the sequence (26.3 or 39.5 ms) was the same for the sequences of all contrasts within the same recording session. B, SF and orientation maps reconstructed by reverse correlation for low (top row) and high (bottom row) contrast stimuli. Data at delays of 40, 60, and 80 ms are shown. Abscissa and ordinate indicate SF and orientation (OR), respectively. Brighter colors represent larger responses. C, Panels represent SF tuning curves (dotted lines) and fitted functions (solid lines) for delays between 30 and 100 ms in 5 ms steps. Fitted functions are only shown for delays between
The contrasts commonly used in the three sessions were 6.25%, 12.5%, and 50%. We limited our analysis to the tuning curves for these contrasts. We recorded a total of 103 neurons producing ≥250 spikes for contrast 50% condition. Among these neurons, 59 neurons showed a reliable time course of SF tuning curves for both 50% contrast and lower contrasts (12.5% and/or 6.25%). For these neurons, we compared SF tunings at low- and high-contrast levels. The low-contrast level refers to the lowest contrast under which neurons showed a reliable time course of tuning curves (6.25% and 12.5% for 40 and 19 neurons, respectively). The high-contrast level was always 50%. These 59 neurons produced ≥250 spikes and exhibited tuning curves nicely fitted with
SF tuning dynamics at low and high contrast for another three cells. A–C, Data for each neuron. First and second columns, Normalized SF tuning curves and fitted functions at
The amplitude of the fitted tuning curves for lower contrast was generally smaller than that for high contrast (Fig. 1C). However, the tuning curves for both contrasts showed similarly high S/N ratios, ruling out the possibility that the differences in the tuning dynamics between contrasts were artificially caused by an insufficient S/N ratio of the tuning curve at low-contrast condition. For the neuron shown in Figure 1C, the amplitude of the fitted functions for the low contrast at
Temporal shift of optimal SF depends on contrast
To elucidate the mechanisms underlying the contrast dependence of SF tunings, we assessed how and when differences in tuning arise during a response. Several previous studies reported that SF tuning curves were dynamically shifted toward a high SF end. Even if differences in SF tunings between the contrasts are not present at response onset, differences may arise as a result of this shift, depending on the contrast, which is what we examined, first. Figure 1 illustrates an example from one neuron in area 17. The two columns in Figure 1C show temporal sequences of SF tuning curves (dotted lines) for the delays between 30 and 100 ms in 5 ms steps for low (left column) and high contrast (right column). Fitted functions are also depicted with solid lines for delays between development and decay times (
We found that neurons typically exhibited a positive SF shift over a wide range of contrasts, but the degree of shift was highly dependent on contrast, as is the neuron illustrated in Figure 1. Another such example is shown in Figure 2A. Normalized tuning curves at
Figure 3 presents a population summary of the temporal shift of the optimal SF at different contrasts. For the 59 neurons analyzed in this study, the distribution of optimal SF at peak delay (
Population summary of the dynamic shift of the SF tuning curves (n = 59). A, Distribution of optimal SF at peak delay (
We examined whether the smaller SF shifts for low contrast arise from smaller responses at this contrast, rather than the low-contrast stimuli themselves. We calculated an SF shift for the SF tuning curves for high-contrast gratings whose orientation was 20° apart from the optimal orientation and compared it with that for the optimal orientation (Fig. 3C, top). Here, we analyzed 35 neurons in which the amplitude of SF tuning curves (at optimal delay) for the suboptimal orientation was between 20% and 60% of that for the optimal orientation (mean 43%). There was no tendency for a larger SF shift for tuning curves with a larger response (optimal orientation) (p = 0.82, paired t test, n = 35). Next, in Figure 3C, bottom, we compared the SF shifts of these neurons between the two contrast conditions as before (using SF tuning curves for the optimal orientation). The amplitude of the low-contrast SF tuning curves was, on average, 38% of that of the high-contrast tuning curves. There was a tendency for a larger SF shift for the high-contrast condition (p = 0.003). These results indicate that the differences in SF shift between the two contrast conditions cannot be explained by the differences in the magnitude of response.
To further characterize the temporal shift of SF tuning curves, for each contrast, we computed mean optimal SFs at three delay times (
Figure 3D also demonstrates that the mean value of the optimal SF is invariant to the contrast at
Finally, nearly 90% of neurons (52 of 59) had
Bandwidth dynamics and two different underlying mechanisms
Previous research using drifting gratings has demonstrated that the tuning width of steady-state SF tuning curves increased as the contrast increased, predominantly elevating the amplitude on the high-SF side (Sceniak et al., 2002). We here examine how bandwidth and high-cutoff SFs at each time slice changed depending on contrast.
We first illustrate simple models of tuning-curve dynamics that may contribute to the contrast-dependent modulation of conventional SF tuning curves (Fig. 4), taking the following observations into account. In the previous section, we showed that the degree of shift of optimal SF was diverse across neurons. Moreover, we previously observed that, at high contrast, neurons with a larger shift of optimal SF tend to show a larger bandwidth decrease (Tanaka and Ohzawa, 2020). Therefore, we illustrate a descriptive model of neurons with a large shift and small shift of optimal SF separately, depicting tuning curves of these models in Figure 4A and Figure 4B, respectively. For both model neurons, we assume that bandwidth is invariant to contrast at response onset (top row). In the neuron with a large optimal-SF shift (Fig. 4A), SF tuning curve at high contrast (magenta) becomes more narrowly tuned to a high-frequency region with its bandwidth decreasing over time. This allows the neuron to selectively enhance processing at a high-frequency range at high contrast. In the neuron with a small optimal-SF shift (Fig. 4B), the bandwidth of tuning curve at high contrast may increase over time to sufficiently elevate high-cutoff frequency (magenta). At low contrast, where the shift in optimal SF is, on average, half of that at high contrast (Fig. 3B), the bandwidth may decrease over time to become more narrowly tuned to a low-frequency region (green, Fig. 4A,B). Based on these dynamics, for both the model neurons, the average SF tuning curves during a response (Fig. 4A,B, bottom), which approximate steady-state tuning curves (Nishimoto et al., 2005), would have a larger bandwidth and a higher high-cutoff SF for high contrast than for low contrast.
A, B, Tuning curves of hypothetical model neurons. The top three rows represent SF tuning curves at three different delays. Bottom panels, Average tuning curves during a response. Magenta and green represent high and low contrast. A, Panels represent tuning curves of a neuron exhibiting a large shift of optimal SF and a bandwidth decrease at high contrast. B, Panels represent tuning curves of a neuron with a small shift of optimal SF and a bandwidth increase at high contrast. The tuning curves for low contrast (green) are the same in A and B. For both models shown in A and B, the bandwidth of the temporally averaged SF tuning curves is larger at high contrast than at low contrast (bottom row). High cutoff SF is also higher at high contrast.
Hereafter, we will examine these possibilities. Currently, it is unknown how bandwidth dynamics depend on contrast. Further, the relationship between bandwidth change and optimal-SF shift has not been characterized in detail. Consequently, it is unclear whether the correlation of optimal-SF shift with bandwidth dynamics could enhance high-frequency processing at high contrast and contribute to contrast modulation of SF tunings.
Figure 1E and the rightmost panels of Figure 2A–C show the time courses of the full bandwidth of the example neurons, where full bandwidth is defined as
Although the above results suggest that bandwidth dynamics depend on the amount of shift of optimal SF, we first examined the mean time course of bandwidth for each contrast on average across the whole dataset. For this analysis, we used half bandwidth (log-ratio of high-cutoff SF to optimal SF) as a measure of tuning width because, in 7 of 59 cells, at least 1 of 6 SF tuning curves (
Population summary for bandwidth dynamics at low and high contrast (n = 59). A, Population average
We next analyzed the bandwidth dynamics cell by cell for each contrast condition, considering its dependency on the optimal SF shift. The bandwidth difference (
Figure 5C shows the bandwidth difference (
These results are generally consistent with the hypothesis described in Figure 4, in which there are two types of neurons: some showing bandwidth decreases and a large optimal-SF shift, and others showing bandwidth increases and a negligible optimal-SF shift. However, neurons in Figure 5C were not discontinuously segregated into these two types but formed a continuum in which the two neurons illustrated in Figure 4 represent two extremes. In the middle portion, this continuum contains neurons with an intermediate SF shift (0.3-0.5 octaves) and a relatively small bandwidth change. Like the extreme neurons, these neurons are also expected to have a larger high-cutoff SF for higher contrast because they should exhibit a larger shift of tuning curves for higher contrast while retaining their bandwidth.
These dynamic effects may be parsimoniously explained by two underlying mechanisms as follows. One mechanism causes a bandwidth decrease and a positive shift of tuning curves. Another mechanism increases bandwidth at high contrast without a shift of optimal SF. The continuous distribution of dynamic effects across neurons indicates that these two mechanisms do not affect distinct groups of neurons separately, but rather affect a considerable portion of neurons in an overlapping manner. As a result, the neurons equally affected by the two mechanisms show an intermediate shift of optimal SF and little bandwidth change because the effects of the two mechanisms on bandwidth cancel each other out. Various relative effects of the two mechanisms essentially explain the full range of the relationship between optimal-SF shift and bandwidth changes in the data population. We will consider the cellular basis of these mechanisms in the Discussion.
We next analyzed how bandwidth difference between contrast arises dynamically cell by cell, taking the types of SF shifts into account. In Figure 5D, differences in
We then examined whether the diverse dynamic effects always enhance high SF processing at high contrast. As shown in Figure 5F, the high-cutoff SF at
Together, the results presented in the current and previous sections indicate that there is a range of dynamic effects whose extreme behaviors are depicted in Figure 4, and which always dynamically enhance high-frequency processing at high contrast. They may mediate the contrast-dependent modulation of the steady-state SF tunings. We further examine this issue in the next section.
How dynamic effects contribute to contrast-dependent modulation of steady-state SF tuning
So far, we demonstrated the dynamic effects that modulate the SF tuning curves depending on contrast, enhancing high-frequency processing at high contrast. The dynamic effects likely contribute to the contrast-dependent modulation of steady-state SF tuning curves observed for drifting gratings. However, to what degree do these effects contribute? To examine this, we calculated predicted steady-state SF tuning curves by summing the sequence of tuning curves in all the time slices during the response time (
Tuning curves obtained by summing SF tuning curves in all the time slices between
For the population summary, we compared the bandwidth, optimal SF, high cutoff frequency, and low cutoff frequency of the summed tuning curves between low and high contrast. As shown in Figure 6B, half bandwidth was, on average, 5% larger for high contrast (mean ± SD: 0.78 ± 0.15 and 0.81 ± 0.17 octaves for low and high contrast, p = 0.033, paired t test, n = 59). We also compared the full bandwidth between contrasts. For the summed tuning curves, full bandwidth at both low and high contrasts was assessed for all tested neurons (Fig. 6C). Full bandwidth was on average 7% larger for high contrast (1.58 ± 0.31 and 1.67 ± 0.37 octaves for low and high contrast, p = 0.009, paired t test, n = 59). Because there were no systematic differences in the distribution of
The optimal SF of the summed tuning curves was, on average, 7% larger for high contrast (0.62 ± 0.22 and 0.67 ± 0.25 cpd for low and high contrast, p < 10−4, paired t test, Fig. 6D). Since there were no systematic differences in
In the last section, we showed that the high cutoff SF of the tuning curve in the time slice at
In Figure 6F, the low cutoff SF of the summed tuning curves at high contrast is plotted against that at low contrast. The difference in low cutoff SF between contrasts is on average 3% (p = 0.037, paired t test, 0.37 ± 0.15 and 0.38 ± 0.18 for low and high contrast), a value much smaller than the corresponding value for high cutoff SF. This result is consistent with the observation of Sceniak et al. (2002) that the difference in low cutoff SF between contrasts is much smaller than that of high cutoff SF.
Together, the predicted steady-state SF tuning curves obtained by summing the temporal sequences of tuning curves show contrast dependence, which is consistent with that found for drifting gratings. This indicates that the dynamic shifts described in the last sections essentially explain the contrast-dependent modulation of the conventional SF tuning curves.
The range of SF dynamics in relation to simple/complex classification and laminar structure
We found a continuous range of SF dynamics likely mediated by two different mechanisms. Herein, we address how these mechanisms work in relation to the known cortical cell types and laminar structure. Simple and complex cells are the two major cell classes in the striate cortex. We investigated whether there are any differences in the range of SF dynamics between these cell types. To place cells along the simple/complex continuum based on data obtained by reverse correlation analysis, we computed a modulation index (MI, see Materials and Methods), which nicely corresponds to the more standard index, F1/F0 ratio computed from responses to drifting grating stimuli (Nishimoto et al., 2005; Ringach and Malone, 2007). Using the commonly used criteria to dichotomize cells (0-1: complex cell; 1-2: simple cell), 10 and 49 cells were classified into simple and complex cells, respectively (17% and 83%).
We examined how SF dynamics depends on MI to assess the difference in tuning dynamics between simple and complex cells. We plotted the SF shift and half-bandwidth difference (
Relationship between SF dynamics and simple/complex cell class (n = 59). MI is used to place cells on simple and complex continuum. Complex cells are placed toward left while simple cells are placed toward right. The SF shift at high contrast (A), (
We next examined the effects of the two mechanisms of SF dynamics for each cortical layer. We previously demonstrated a wide range of SF dynamics for all six layers (Tanaka and Ohzawa, 2020). Here we focus on whether their generation can be explained by the interactions of the two mechanisms. Each panel of Figure 8 graphs the half-bandwidth change (
Each panel plots (
In addition, an F test revealed that the range (variance) of the SF shift in layer 5 was significantly larger than that in layer 4 (p < 0.01). We also previously showed with a larger data sample (n = 42 and 64 for layer 4 and layer 5) that the variance of SF shifts was significantly different between layers 4 and 5 (p < 0.03), but the range of SF shifts covered by neurons in layer 4 corresponded to ∼80% of that in layer 5 (Tanaka and Ohzawa, 2020). Therefore, a wide range of SF dynamics is already present as early as in layer 4, although additional enhancement of SF dynamics likely occurs outside layer 4.
Discussion
We measured the time course of SF tunings of cat area 17 neurons under different contrast conditions and found a range of dynamic effects that enhance the processing of high-frequency ranges under a high-contrast condition. These dynamic effects are likely to be mediated by combinations of two different mechanisms. Below, we compare our results with those of previous studies and discuss the circuit basis of the mechanisms and functional implications of the present results.
SF tuning dynamics explain the contrast dependence of SF tunings
Whereas neurons with bandwidth increases and decreases were observed in a similar frequency in this study, Bredfeldt and Ringach (2002) reported that, in monkeys, the majority of neurons decrease bandwidth over time. The average shift of optimal SF reported in their study was 0.62, ∼1.5 times larger than the current results. It may be possible that, in monkeys, the effect of the bandwidth-decrease mechanism is stronger than that of the bandwidth-increase mechanism.
We showed that the dynamic SF tuning effects generally explain the contrast-dependent tuning modulation for drifting gratings found in monkey V1 neurons (Sceniak et al., 2002). However, the differences in SF tuning curves between contrast for drifting grating were more prominent than those of the integrated tuning curves shown in the present study. For example, the average SF bandwidth of monkey V1 neurons increased by 124% with contrast, while the corresponding percentage is 107% in this study. Several factors may be responsible for this difference. First, while Sceniak et al. (2002) determined contrast levels of low- and high-contrast stimuli for each neuron so that they lie at sufficiently low- and high-sloping portions of the contrast response function, we set high contrast at 50% for all neurons. It is possible that, for some neurons, 50% contrast is not sufficient to evoke strong dynamics of SF tuning. Next, Sceniak et al. (2002) showed that a threshold effect partially explains bandwidth differences between the contrasts, which is not simply measurable in the present reverse-correlation analysis.
Nevertheless, we found that the contrast-dependent modulation of SF tunings for temporally integrated responses in reverse correlation qualitatively agrees well with that measured using drifting gratings. Furthermore, the contrast-dependent shift of tuning curves toward a high SF end cannot be explained by a threshold effect. Therefore, the mechanisms of SF dynamics we reported in this study essentially explain the contrast-dependent modulation of SF tuning curves as previously observed for drifting gratings.
Neural circuits of contrast-dependent SF tuning modulation
We indicated that the dynamic effects are mediated by two mechanisms: one that decreases bandwidth and shifts optimal SF, and the other that increases bandwidth without shifting optimal SF. The exact neuronal circuitry underlying these mechanisms remains unclear. We here describe a hypothesis regarding this, first for the bandwidth increase. Previous studies have shown that the size of the RF is smaller at higher contrast (Kapadia et al., 1999; Sceniak et al., 1999; Nienborg et al., 2013) and shrinks over time (Suder et al., 2002; Malone et al., 2007). An SF bandwidth increase during a response is likely related to this RF shrinkage because, for linear RF, the bandwidth of SF tuning is inversely related to the size of RF. While RF size is shaped by feedforward, lateral (horizontal), and feedback circuits (Nurminen et al., 2018), a marked reduction of lateral connectivity at high contrast has been demonstrated (Nauhaus et al., 2009). Earlier, Sceniak et al. (1999) suggested that the RF shrinkage at high contrast is because of this reduction, and further discussed that a decline of EPSP of lateral connections at high contrast is possibly caused by depression of excitatory synapses (Thomson and Deuchars, 1997) or increased shunting effects of postsynaptic neurons elicited by increased cortical activity at high contrast (Bernander et al., 1991). In both synaptic mechanisms, RF shrinkage could occur rapidly, likely accompanied by a dynamic bandwidth increase.
Previous evidence indicates that the shift of optimal SF and associated bandwidth decrease are mostly accompanied by tuned suppressive components of SF tuning curves, indicating that intracortical tuned inhibition plays an essential role in the bandwidth-decrease dynamics (Bredfeldt and Ringach, 2002; Ninomiya et al., 2012). Because the magnitude of intracortical inhibition depends on stimulus contrast, becoming dominant over excitation as contrast increases (Rubin et al., 2015; Adesnik, 2017), this tuned suppression may explain the larger optimal-SF shift for higher contrast. On the other hand, our results demonstrated that optimal-SF shift is more prominent in complex cells than in simple cells. This would be explained by a larger SF-tuned suppression for complex cells. Alternatively, this may indicate a different mechanism for the optimal-SF shift mediated by excitatory intracortical feedforward processing, where complex cells receive inputs from other cortical neurons tuned to a variety of SFs with different latencies. For this mechanism to mediate a contrast-dependent shift, high-frequency input should be less sensitive to contrast than low-frequency input. Further studies using optogenetic circuit manipulation (Atallah et al., 2012; Wilson et al., 2012) may help to determine which circuit mechanisms play more essential roles.
Different from the cortical origin of the SF dynamics described above, Allen and Freeman (2006) showed that neurons of cat LGN display a substantial temporal shift of optimal SF. Additionally, using drifting gratings, the high cutoff SF of LGN neurons generally increases with contrast (Nolt et al., 2004). Assuming that SF tuning dynamics of LGN neurons depend on contrast, the contrast-dependent modulation of high cutoff SF may be caused by SF tuning dynamics of LGN neurons. This may in turn contribute to the contrast-dependent SF dynamics of striate cells. However, this contribution is probably limited, given that the shift in optimal SF of cortical neurons was often shaped by tuned suppressive components. A larger optimal-SF shift of complex cells also suggests an essential contribution of intracortical process, indicating that subcortical contribution is limited. Together with the results of laminar analysis, this evidence indicates that intracortical processing, particularly within layer 4, likely plays an essential role.
A new view of the function of tuning dynamics
The contrast-dependent modulation of SF tuning may be generally understood in terms of optimal information transmission, together with the effect of RF size change (Sceniak et al., 1999). Under high contrast, neurons increase the spatial resolution by narrowing the RF, sacrificing sensitivity. For a higher spatial resolution, it is also reasonable to tune to higher SFs to better transmit information of fine details. Under low contrast, neurons need to increase sensitivity by using a larger RF size, sacrificing resolution. In such a case, it may be reasonable to be more tuned to lower SFs abundantly contained in features of a larger size.
It is generally considered that contrast-dependent response modulation occurs in two phases: a slow phase with a time scale of seconds (Ohzawa et al., 1982; Carandini and Ferster, 1997) and a fast phase with a time scale of <0.2 s (Victor, 1987; Baccus and Meister, 2002; Mante et al., 2005). Fast modulation has a functional sense because in daily life stimulus contrast within neurons' RF may change at every fixation, which occurs every 300 ms. Here, we did not directly measure the time required for the contrast-dependent modulation of SF tunings to work after changing the contrast level. However, our demonstration that this modulation is based on dynamic effects suggests that the contrast-dependent modulation of SF tuning is at least partly mediated by a fast adaptive process. For example, the degree of SF shift mediated by intracortical suppression could immediately reflect the amplitude of transient inhibitory inputs in response to recent stimuli, which increases with stimulus contrast. However, we cannot rule out a slow adaptive effect because these inhibitory inputs may become even larger with a slow time scale. To isolate a fast adaptive effect, a reverse correlation experiment simultaneously changing stimulus contrast and SF is required.
The dynamics of SF tunings, as well as disparity and size tunings, have been implicated in coarse-to-fine algorithms, the processing where coarse and fine information is analyzed sequentially (Menz and Freeman, 2003; Malone et al., 2007; Neri, 2011), but this remains controversial (Frazor et al., 2004; Mareschal et al., 2006). The present results demonstrate that the tuning dynamics contribute to the contrast-dependent modulation of SF tunings. Like SF, contrast modulation of size tuning was shown under a steady-state condition (Sceniak et al., 1999). We suggest that tuning dynamics play critical roles in contrast-dependent tuning modulation for these features.
In addition, tuning dynamics for orientation processing were shown for monkeys (Ringach et al., 1997). These dynamics have been viewed as a process for narrowing orientation tuning. Because orientation selectivity (measured in terms of circular variance) also depends on contrast to some degree under a steady-state condition (Alitto and Usrey, 2004), our results suggest that, rather than simply sharpening orientation tuning, the orientation dynamics adjust the tuning in a contrast-dependent manner. Overall, our findings implicate that the dynamic tuning process plays an essential role in contrast-dependent tuning refinement for various visual features in the early visual processing.
Footnotes
This work was supported by Ministry of Education, Culture, Sports, Science and Technology/Japan Society for the Promotion of Science KAKENHI Grants JP 16K01965, JP 15H05921, and 22K06437; and Kyoto Sangyo University Research Grant M2001. We thank I. Ohzawa for a great deal of support and valuable discussions; and K. Sasaki, M. Fukui, Y. Asada, T. Arai, T. Nakazono, R. Mizoguchi, D. Kato, K. Kurihara, M. Yoshida, M. Baba, and K. Kohara for assistance with the experiments and discussions.
The authors declare no competing financial interests.
- Correspondence should be addressed to Hiroki Tanaka at rtanaka{at}cc.kyoto-su.ac.jp
