We recorded from cells in the anterio-ventral (TEav) and anterio- dorsal (TEad) parts of area TE of the inferotemporal cortex and examined their responses to a set of 100 visual stimuli in awake, fixating monkeys. In both TEav and TEad we found that, depending on the stimulus, the time course of responses varied considerably within individual cells and that there were three main factors in the variation. One factor is variance in the balance between the initial transient part of responses around 130 ms after stimulus onset and the later part after 240 ms from stimulus onset. The later parts of responses were more stimulus selective. The second factor is variance in the latency of response onset and peak and the third is variance in the speed of decay from the peak within the initial part of the responses. Stronger responses had shorter onset and peak latencies and longer decay times. The results suggest that stimulus images can be discriminated very rapidly in TEav and TEad by detecting differences in response onset. TEav cells differed from TEad cells in that they were more difficult to activate than TEad cells: the proportion of responsive TEav cells was smaller, the maximal responses of individual cells were smaller than in TEad and the number of stimuli that evoked significant responses in individual responsive cells was also smaller than in TEad. Moreover, TEav cells, overall, responded more strongly to more colorful object images than less colorful ones, while TEad cells did not show such a tendency. However, the minimum onset latency of individual cells and the sharpness of stimulus selectivity did not differ significantly between TEav and TEad. Responses of TEav cells are as selective as those of TEad cells, although there remains a possibility that the domain of selectivity differs between the two areas. These results support an earlier anatomical finding that TEav and TEad are located at the same hierarchical level of separate serial pathways rather than at successive stages of a single pathway.
The anterior part of the inferotemporal cortex, which corresponds to the cytoarchitectural area TE, is the final purely visual stage along the occipitotemporal or ventral visual pathway. It is thought to be essential for visual object recognition, since a bilateral lesion of TE produces severe and selective deficits in discrimination and recognition of visual images of objects (Blum et al., 1950; Chow, 1951; Mishkin, 1954; Mishkin and Pribram, 1954) [for a review see Gross (Gross, 1994)]. Correspondingly, cells in TE respond selectively to the complex features of object images (Gross et al., 1972; Desimone et al., 1984; Tanaka et al., 1991; Fujita et al., 1992; Kobatake and Tanaka, 1994; Ito et al., 1994, 1995; Sheinberg and Logothetis, 1997).
TE can be divided into a dorsal and ventral part (Table 1). Brodmann divided the inferotemporal region, which corresponds to TE, into areas 20 (ventral) and 21 (dorsal) (Brodmann, 1909). The border between these two regions is located at the anterior middle temporal sulcus. It was later found that the ventral and dorsal parts of TE have differential connections with the amygdala (Iwai and Yukie, 1987; Iwai et al., 1987; Cheng et al., 1997), striatum (Cheng et al., 1997), superior temporal sulcus (Saleem et al., 2000), perirhinal and entorhinal cortices (Saleem and Tanaka, 1996) and hippocampal formation (Iwai and Yukie, 1988; Yukie and Iwai, 1988; Saleem and Hashikawa, 1998). Based on these ventral versus dorsal differences as well as the previously reported posterior versus anterior difference in the afferent connections from the prelunate gyrus (Shiwa, 1987; Morel and Bullier, 1990; Yukie et al., 1990), TE has been divided into four sub-regions; TEpv (posterio-ventral), TEpd (posterio- dorsal), TEav (anterio-ventral) and TEad (anterio-dorsal). Saleem and Tanaka (Saleem and Tanaka, 1996) found that the border between TEav and TEad described by Yukie et al. (Yukie et al., 1990) corresponds to the cytoarchitectural border between TE1 and TE2 described by Seltzer and Pandya (Seltzer and Pandya, 1978) at the anterior–posterior levels where the anterior middle temporal sulcus is found. They proposed using the cyto- architectural TE1/TE2 border as the TEav/TEad border. This border is located around the lip or at some depth in the lateral bank of the anterior middle temporal sulcus, depending on the anterior–posterior level. It has also been shown that TEav and TEad receive visual inputs through separate pathways in parallel (Martin-Elkins and Horel, 1992; Yukie et al., 1992; Saleem et al., 2000). Felleman and Van Essen divided TE into CITv, CITd, AITv and AITd (Felleman and Van Essen, 1991). The anterior– posterior level of CITv and CITd corresponds to that of TEpd and TEpv and that of AITv and AITd to that of TEad and TEav. However, the border between AITd and AITv is located close to the ventral lip of the superior temporal sulcus. Thus, AITv overlaps both TEad and TEav.
The stimulus selectivity of TEav cells is largely unknown, while the selectivity of TEad cells has been extensively studied (Desimone et al., 1984; Tanaka et al., 1991; Fujita et al., 1992; Kobatake and Tanaka, 1994; Ito et al. 1994, 1995). Most previous studies of responses of TEav cells mainly focused on response aspects other than stimulus selectivity (Miyashita, 1988; Miller et al, 1991). Moreover, only one study has compared TEav and TEad. Baylis and co-workers (Baylis et al., 1987) recorded a large number of cells from a wide extent of the inferotemporal cortex and the superior temporal sulcus in monkeys performing a fixation task and compared response properties among the different cytoarchitectural areas defined according to Seltzer and Pandya (Seltzer and Pandya, 1978). However, Baylis et al. were only able to activate 36% of cells recorded from TE1 and TE2 (Baylis et al., 1987). Thus, the first aim of the present study was to compare the response properties of cells in TEad and TEav.
A time course analysis of visually evoked responses may help identify the carrier of information and provide an insight into the mechanisms by which the stimulus selectivity of responses arises. There are two contradictory views regarding the carrier of information in the responses of inferotemporal cells. One view emphasizes the importance of temporal modulation over the period of stimulus presentation (several hundred milli- seconds). It has been shown that temporal patterns of responses recorded from TEad cells were stimulus dependent and that the temporal patterns carried a significantly larger amount of information about the stimulus than the information derived from the averaged firing rate (Optican and Richmond, 1987; Richmond et al., 1987). The other view emphasizes the richness of information contained in the early phase of responses. It has been shown that the average firing rate within 150 ms after stimulus onset represents most of the information carried by the whole response, including the temporal modulation (Tovee et al., 1993). Consistent with this finding, electroenchepalogram recordings from the prefrontal cortex showed that 150 ms is enough time to judge whether there is an animal in a complex scene (Thorpe et al., 1996; Fabre-Thorpe et al., 1998). The second aim of the present study was to examine the temporal patterns of responses in TEav and TEad cells.
We routinely used a large number (100) of visual stimuli in the present study to quantitatively analyze the response properties of TEav and TEad cells. We presented all 100 stimuli to all the recorded cells. We believe that the use of a large number of stimuli is essential in studying stimulus selectivity, because it has been reported that most TE cells respond to specific stimuli (Tanaka et al., 1991; Fujita et al., 1992; Kobatake and Tanaka, 1994; Ito et al., 1994, 1995; Sheinberg and Logothetis, 1997). An incorrect conclusion might be drawn if the stimulus set did not include the optimal or sub-optimal stimuli for individual cells. We used awake, behaving animals in this study because we found it difficult to activate cells in TEav in anesthetized animals (H. Tamura and K. Tanaka, unpublished observation). Some of the results have been reported previously in abstract form (Tamura and Tanaka, 1996, 1997).
Materials and Methods
Cell responses were recorded from two macaque monkeys (Macaca fuscata) while they performed a visual fixation task. The monkeys weighed 5.4 and 7.4 kg. All procedures were performed in accordance with the Guiding Principles for the Care and Use of Animals in the Field of Physiological Science (Japanese Physiological Society).
A lever press by the monkey initiated a trial. A small white spot (0.15° in diameter) was presented on a CRT display (PCKM153; NEC, Tokyo, Japan), which was placed at 57 cm from the eye. The monkey was required to fixate the spot within 500 ms of spot onset and continue fixating for a period varying randomly between 2.5 and 7 s. The size of the fixation window was set to 4 × 4°. At the end of each trial the color of the fixation spot changed slightly and the monkey had to release the lever within 500 ms. Correct performance on a trial was rewarded with a drop of juice. A 1.5 s inter-trial interval was imposed before the next trial could begin.
The behavioral task was first trained without head fixation or eye position measurement. After the monkeys learned the task they were prepared for cell recordings by aseptic surgery. The surgery commenced with injection of atropine sulfate (0.1 mg/kg i.m.). Anesthesia was induced by i.m. injection of ketamine hydrochloride (12 mg/kg), maintained by i.p. injection of sodium pentobarbital (35 mg/kg) and supplemented by additional injections of sodium pentobarbital (9 mg/kg each). Three blocks for head restraint and a recording chamber were implanted in the dorsal surface of the skull. After a week of recovery, training was resumed, but this time the eye position was measured. Cell recording was started when the monkey had succeeded in completing the task with continuous eye fixation.
Extracellular single cell recordings were made from anterior infero- temporal cortex with tungsten electrodes (FHC, ME) while the monkeys were performing the fixation task. The electrode was advanced with an Evart type manipulator (MO95; Narishige, Tokyo, Japan) from the dorsal surface of the brain through a guide cannula, which was inserted to ~15 mm above the recording sites. We determined the position of the dura at the ventral surface of the brain by detecting the characteristic noise in the recorded voltage when the electrode tip contacted the dura and limited the studies to the gray matter at the ventral surface of the brain (Fig. 1). Except for a few cells at the ventral lip, no cells in the superior temporal sulcus were studied. We did not select cells according to their responsiveness, but always studied the first well-isolated cells (with spikes >4 times as large as the baseline noise) at the intended depth. Successively studied cells were spaced at 100 μm intervals or greater. In some penetrations the initial part of the gray matter was ignored so that different cortical layers would be evenly sampled.
The timing of action potentials and lever press and release by the monkey were recorded with 1 ms resolution and stored on a personal computer (PC9821; NEC). Peristimulus time histograms were displayed on-line. The eye position was monitored every 16 ms by an infrared method. An infrared laser beam was projected onto the cornea of the right eye and the reflected image was captured by a charge coupled device camera. The eye position was then determined by an image processor (Percept Scope; Hamamatsu Photonics, Hamamatsu, Japan). At the end of the day's recording we took X-ray images of the electrode within the brain to determine the electrode position relative to the bony landmarks.
One hundred visual stimuli (Fig. 2) from a fixed set containing 74 stationary photographs of natural objects and 26 stationary two- dimensional geometric shapes were presented individually on the CRT display against a common homogeneous gray background. The 100 stimuli were selected so as to maximize the variety of shape, color and texture combinations. The monkeys had experienced the stimuli for several weeks during the training period of the fixation task before cell recording was begun. The images occupied a 160 × 160 pixel region subtending 6° × 6° and were encoded at a color depth of 24 bits. The pictures were presented at 56.4 Hz without interlacing and the luminance of the gray background was 35.6 cd/m2. Among all the images the luminance of the brightest white and the darkest black were 50.5 and 2.66 cd/m2, respectively. All stimuli were presented with their centers at the fixation point, which was placed at the center of the display. During one fixation trial two to six stimuli were sequentially presented for 400 ms each with an inter-stimulus interval of 500 ms. A homogeneous gray field with the same luminance as the stimulus background was presented during the inter-stimulus and inter-trial intervals. All 100 stimuli were routinely presented to each cell. The stimuli were presented in a quasi-random order until every stimulus had been presented between five and 10 times (with a mean ± SD of 7.9 ± 2.0). The fixation spot was turned off during stimulus presentation. The vertical synchronization signals of the CRT display were recorded by the computer in order to register the exact timings of stimulus presentation with respect to cell responses. Stimulus onset was defined as the point at which the CRT beam swept the center of the stimulus images, 9 ms after the vertical synchronization signal.
The statistical significance of responses was determined in two steps. First, cells were examined for stimulus selectivity. Response magnitude in individual trials was calculated as the mean firing rate during the 400 ms response period (with the window position shifted by 60 ms from the stimulus presentation period to compensate for the response latency) minus the spontaneous firing rate during the 200 ms period immediately prior to stimulus onset. Its dependency on the stimulus was assessed by one-way ANOVA (P < 0.01). Second, the significance of individual responses was examined for the cells selected in the first step by comparing the distribution of firing rates during the 400 ms response window in individual trials with that of the spontaneous firing rates during the 200 ms period immediately prior to stimulus onset in individual trials [P < 0.01 by the Kolmogorov–Smirnov (K-S) test]. A cell was further analyzed if its responses were stimulus selective and at least one of the 100 visual stimuli evoked significant responses in it. The first step was necessary to avoid false detection of a responsive cell due to multiple statistical examination of 100 responses in individual cells.
To determine the latency of response onset and peak response, the spike trains of individual trials were first converted into 1 ms resolution spike density functions by convoluting the spike trains with a Gaussian kernel (σ = 5 ms). Next, we determined the level required to significantly exceed activity within the 200 ms spontaneous period (P = 0.01 by K-S test). The onset of the response was designated the first bin that exceeded this level following stimulus onset and was followed by five consecutive bins also exceeding this level. The peak response was designated the peak with the maximum magnitude in the spike density function and the time from stimulus onset to response peak was measured as peak latency. When multiple peaks of the same amplitude were found, the first peak was taken as the response peak. Decay time was defined as the latency between stimulus onset and the first bin in the spike density function at which the firing rate returned to the spontaneous firing range after the response peak.
Unless otherwise noted, the magnitude of responses is computed as the mean firing rate during the whole 400 ms response window (with the window position shifted by 60 ms from the stimulus presentation period) minus the mean spontaneous firing rate during the 200 ms period immediately preceding stimulus onset. The height of the response peak beyond the spontaneous firing level was also used as another measure of response magnitude when we examined the correlation between response time course parameters and response magnitude.
We defined the tuning width of individual cell excitatory responses using two measures. The first was the number of stimuli that evoked responses >25% of the maximal cell response (TW25). TW25 was measured to the first digit by interpolating responses just above and below 25% of the maximal response. We also counted the number of effective stimuli that evoked significant (P < 0.01) excitatory responses in individual cells (TWP<0.01). TWP<0.01 was measured as an integer.
It may be worth mentioning that the information and selectivity indices are different measures. When a cell responds to many stimuli with slightly different amplitudes the selectivity is low while the amount of information carried by the responses is large. Conversely, when a cell responds to only a limited number of stimuli the selectivity is high while the amount of information is small.
The correlation between two different measures taken for a given cell or cell response (for example the onset latency of the maximal response versus the stimulus selectivity) was examined by calculating the Pearson's correlation coefficient. However, where both of the two measures correlated with a common third measure (for example the magnitude of the maximum response), the net correlation between the first two measures was examined by calculating the partial correlation coefficient given by the formula:
After completion of the recordings the monkeys were deeply anesthetized and perfused in the heart. The brain was removed and cut into 50 μm thick sections for reconstruction of electrode penetration. The penetrations were reconstructed using the damage trace of the electrode penetrations and the X-ray images taken at the end of each individual penetration. During recording we had determined the location of transition from gray matter to white matter and the position of the dura at the ventral surface of the brain (see above). We determined the vertical positions of recorded cells along the penetrations using these positions together with the readings of the electrode manipulator.
The sampling of cells covered a posterior–anterior extent from 13 to 21 mm anterior to the ear bar, where the anterior middle temporal sulcus is located (Fig. 1). Therefore, the recording sites corresponded to area TEa. The lateral–medial extent covered from the ventral lip of the superior temporal sulcus to around the midpoint between the anterior middle temporal sulcus and the rhinal sulcus (Fig. 1). We followed Saleem and Tanaka for determination of the borders between TEad, TEav and the perirhinal cortex (Saleem and Tanaka, 1996). Several cells were excluded from the analysis because the recordings were taken at the border between TEav and TEad.
Cells in the Perirhinal Cortex
In addition to cells recorded from TEav and TEad, we recorded 32 cells from the perirhinal cortex. Only 12 cells (38%) were stimulus selective (P < 0.01 by ANOVA) and showed significant responses (P < 0.01 by K-S test) to at least one of the 100 stimuli. The proportion of such cells in perirhinal cortex was significantly smaller than in TEav (P < 0.001 by χ2 test) and TEad (P < 0.001 by χ2 test). The magnitude of the maximal excitatory response of individual perirhinal cortex cells (14.4 ± 16.9 spikes/s) tended to be smaller than that of TEav (P = 0.032 by U-test) and TEad cells (P < 0.001 by U-test), while the spontaneous firing rate of perirhinal cells (7.5 ± 11.7 spikes/s) did not differ significantly from that of TEav (P = 0.239 by U-test) or TEad cells (P = 0.093 by U-test). Since we found that perirhinal cells were not very active under the present experimental conditions (fixation task), we made no further recordings from these cells and excluded the 32 cells from the analyses in this paper.
We completed the test with the set of 100 visual stimuli (Fig. 2) for 213 cells. One hundred and sixty-nine cells (79%) were stimulus selective (P < 0.01 by ANOVA) and showed significant responses (P < 0.01 by K-S test) to at least one of the 100 stimuli. Altogether, 3118 significant responses were obtained from the 169 cells. Among these significant responses, 2584 responses were excitatory (with an averaged firing rate during the 400 ms response window exceeding the spontaneous firing level), while the remaining 534 responses were inhibitory (with an averaged firing rate during the 400 ms response window below the spontaneous firing level). Excitatory responses were obtained from 156 cells and inhibitory responses from 55 cells. Forty-two cells showed both excitatory and inhibitory responses. Eighty- two of the 169 responsive cells were recorded from TEav and 87 from TEad.
Time Course of Responses
We will first describe the stimulus dependence of the response time course commonly found in TEav and TEad cells and then compare various aspects of the responses between TEav and TEad.
An example of responsive cells recorded from TEav is shown in Figure 3. Only one of the 100 visual stimuli (stimulus 53, an image of a chair) evoked a strong excitatory response. This sharp stimulus selectivity contrasted with the broad stimulus selectivity of another TEav cell shown in Figure 4. This latter neuron showed excitatory responses to more than half of the visual stimuli.
As shown by the two examples above, the sharpness of selectivity varied considerably among cells. The sharpness of stimulus selectivity of excitatory responses was quantified by two measures. One measure was the number of stimuli that evoked at least 25% of the maximal excitatory response in individual cells (TW25). The TW25 of the cell shown in Figure 3 was 1.9 and that of the cell shown in Figure 4 was 6.3. The other measure was the number of effective stimuli that evoked statistically significant (P < 0.01 by K-S test) excitatory responses in individual cells (TWP<0.01). The cell shown in Figure 3 showed significant excitatory responses to three stimuli; the one shown in Figure 4 showed significant excitatory responses to 76 stimuli. TW25 ranged from 1.9 to 88.0 among the 156 cells with a mean ± SD of 24.6 ± 19.7 (median 19.0) and TWP<0.01 ranged from 1 to 100 with a mean ± SD of 16.6 ± 21.3 (median 7).
The amount of information transmitted by individual cells about the 100 visual stimuli was also calculated for the averaged responses during the 400 ms response window minus the spontaneous firing level in individual trials. The calculated value represents the reduction in stimulus uncertainty provided by the response measure in a single trial. The calculation supposes that the observer knows the probabilistic distribution of the response measure for each of the 100 stimuli but does not know which of the 100 stimuli was presented in a given trial. It then calculates the averaged reduction in uncertainty afforded by the response measure in a single trial. The average was taken across all trials and stimuli. Calculation of the transmitted information was limited to 54 responsive cells that saw each stimulus 10 times. The uncertainty for stimuli selected with equal probability from among 100 stimuli is 6.64 bits. The information transmitted by the responses of the cells shown in Figures 3 and 4 were 0.36 and 0.50 bits, respectively. The amount of information ranged from 0.15 to 0.94, with a mean ± SD of 0.43 ± 0.18 bits (median 0.41) among the 54 cells. There was a strong positive correlation between the amount of information and the TWP<0.01 of individual cells (r = 0.58, P < 0.001), while there was no correlation between the amount of information and TW25 (r = 0.016, P = 0.27).
Several of the excitatory responses of the cell in Figure 4 showed both an initial transient component (responses at ~130 ms after stimulus onset) and a later, more sustained component (responses after 240 ms from stimulus onset), whereas the remaining majority of the responses had only an initial transient component. Thus, not only did the response time course depend on the stimulus, but also the initial transient portion of the excitatory response showed lower stimulus selectivity than the later, more sustained response period.
To quantitatively compare the sharpness of stimulus selectivity in the excitatory responses between the initial and the later response period we set time windows for the initial and later response components by referring to the averaged time course of the 2584 significant excitatory responses (Fig. 5, top). There was a trough at ~240 ms between the peak of the initial response (located at ~130 ms) and the peak of the later response (at ~350 ms). Thus, we set a 40 ms time window for the initial component at 110–150 ms after stimulus onset and another 40 ms window for the late component at 330–370 ms. Although the peak of the later response was wider than 40 ms, the length of the time window was set identical for the two components to permit an unbiased comparison.
The plots of the averaged time courses of TW25, TWP<0.01 and the amount of transmitted information (Fig. 6) were similar in shape to that of the averaged discharge rate time course (Fig. 5, top). They started to rise at response onset, reached their peaks at ~130 ms, decayed to troughs at ~240 ms and recovered to levels lower than the initial peaks.
To test the statistical significance of the differences between the initial and late peaks, each of the three parameters measured in the 110–150 ms window in individual cells was compared with that in the 330–370 ms window. Figure 7 shows this comparison as a scatter diagram in which each dot stands for one cell, with its x position the measure in the 110–150 ms window and its y position the measure in the 330–370 ms window. In all three scatter diagrams there were more cells in the region below the diagonal line than above. Paired comparison with the Wilcoxon signed rank test showed that there was a significant decrease in TW25, TWP<0.01 and the amount of transmitted information from the initial to late responses (P = 0.019 for TW25; P < 0.001 for TWP<0.01 and the amount of information). The decays in TW25, TWP<0.01 and the amount of transmitted information from the initial peaks to the troughs (at 230–270 ms) and their recovery from the troughs to the late peaks were also statistically significant (P < 0.001 for all). Thus, the present results consistently indicate changes in the stimulus selectivity of cells over time: it is lowest in the initial peaks, highest in the troughs after the initial peak and middling in the later peaks.
The tuning of excitatory response stimulus selectivity tended to be broader and the amount of transmitted information larger in cells with greater maximal excitatory responses. This conclusion is based on the results that TW25, TWP<0.01 and the information content were significantly correlated with the maximal responses in individual cells (r = 0.40 for TW25, r = 0.72 for TWP<0.01 and r = 0.46 for information, all P < 0.001). They were also significantly correlated with the maximal peak response in individual cells (r = 0.48, P < 0.001 for TW25; r = 0.62, P < 0.001 for TWP<0.01; r = 0.34, P = 0.022 for information).
Inhibitory responses had slower time courses than excitatory responses. The averaged time course reached a near maximum (94% of maximum) at 170 ms after stimulus onset and stayed high (>75% of maximum) until offset of the stimulus (Fig. 8). The tuning width of inhibitory responses at 25% of maximal inhibition ranged from 12 to 90 among the 55 cells, with a mean ± SD of 44.8 ± 20.3 (median 40.5), while the number of significant inhibitory responses in individual cells ranged from 1 to 62, with a mean ± SD of 9.7 ± 14.5 (median 4). The tuning widths of inhibitory responses at 25% were significantly larger than TW25 for excitatory responses (P < 0.001 by U-test), while the number of significant inhibitory responses in individual cells was significantly smaller than TWP<0.01 for excitatory responses (P = 0.007 by U-test). This inconsistency is likely due to the small magnitudes of inhibitory responses. Thus, we refrain from drawing a conclusion comparing the relative sharpness of the inhibitory response stimulus selectivity with that of the excitatory responses.
Onset Latency, Peak Latency and Decay Time
The time courses of TW25, TWP<0.01 and transmitted information shown in Figure 6 suggest that a stimulus-dependent variance in the response time course exists not only in the balance between the initial and late components, but also in the rise and decay speed of the initial peaks. The onset and peak latencies were reliably determined over the spike density function for 1762 of the 2584 significant excitatory responses (see Materials and Methods). The 1762 responses were obtained from 122 cells. The shortest onset latency was 59 ms and the mean ± SD was 121.0 ± 57.8 ms (median 105; Fig. 9, top). The shortest peak latency was 82 ms and the mean ± SD was 148.5 ± 77.7 ms (median 122; Fig. 9, middle). The median of the peak latency was 17 ms longer than the median of the onset latency. The decay time, defined as the time from stimulus onset to the bin at which the response returned to baseline after the initial peak, was calculated for the spike density function of 1675 responses in 113 cells with onset latencies <240 ms (see Materials and Methods). The mean ± SD of the decay time was 161.4 ± 52.5 ms (median 152.0; Fig. 9, bottom).
Stronger excitatory responses tended to have shorter onset and peak latencies but longer decay times than weaker excitatory responses. We base this conclusion on: (i) a significant negative correlation between onset latency and the height of the response peak among the 1762 excitatory responses (Fig. 10, top; r = –0.33, P < 0.001); (ii) a significant negative correlation between peak latency and the height of the response peak among the 1762 excitatory responses (Fig. 10, middle; r = –0.26, P < 0.001); (iii) a significant positive correlation between decay time and the height of the response peak among the 1675 excitatory responses (Fig. 10, bottom; r = 0.32, P < 0.001). There were also significant correlations when the response magnitude was measured as the averaged firing rate during the 400 ms response window minus the spontaneous firing level (r = –0.24, P < 0.001 for onset latency; r = –0.11, P < 0.001 for peak latency; r = 0.60, P < 0.001 for decay time), but the correlation was weaker. Also, since a correlation between decay time and magnitude of response in the 400 ms response window is logically expected, we use the response peak height as a measure of the response in this part of the Results.
A similar correlation between onset latency, peak latency and decay time versus height of the response peak was also found within individual cells. An example is shown in Figure 11. The top of Figure 11 shows the time courses of: the response with the maximum peak response; a response with 60% maximal peak response; a response with 40% maximal peak response. The onset latency, peak latency and decay time of all the significant excitatory responses of the cells are plotted against the height of their response peaks in the three scatter diagrams in Figure 11. There were significant negative correlations between onset latency and height of the response peak (r = –0.72, P < 0.001; Fig. 11, second from top) and between peak latency and height of the response peak (r = –0.61, P < 0.001; Fig. 11, third from top). There was also a significant positive correlation between decay time and height of the response peak (r = 0.71, P < 0.001; Fig. 11, bottom).
A correlation between the temporal parameters and peak response magnitude was observed in most cells. The correlation coefficients between onset latency and peak response magnitude were calculated for each of the 42 cells that showed significant excitatory responses to at least 10 stimuli. The distribution of the correlation coefficients was significantly shifted to the negative side of 0 (P < 0.001 by t-test) with a mean ± SD of –0.41 ± 0.22 (Fig. 12, top). Out of the 42 cells, 26 showed a significant (P < 0.05) negative correlation and no cell showed a significant (P < 0.05) positive correlation. The correlation coefficients between peak times and their magnitudes were calculated for the same 42 cells. The distribution of the correlation coefficients was also significantly shifted from 0 to the negative side (P < 0.001 by t-test; Fig. 12, middle), with a mean ± SD of –0.23 ± 0.21. Among the 42 cells, 17 had a significant (P < 0.05) negative correlation and none had a significant (P < 0.05) positive correlation. The correlation coefficients between the decay times and peak response magnitudes were calculated for 39 of the 42 cells that showed significant excitatory responses to at least 10 stimuli. Another three cells had an onset latency >250 ms and were excluded from the analysis. The distribution of the correlation coefficients was significantly shifted from 0 to the positive side (P < 0.001 by t-test; Fig. 12, bottom), with a mean ± SD of 0.29 ± 0.34, although it had a more complex shape than those of the onset and peak latencies versus response magnitudes. Among the 39 cells, 19 had a significant (P < 0.05) positive correlation, whereas only one had a significant (P < 0.05) negative correlation.
The differences in onset latency between the maximal and smallest significant excitatory responses were determined based on the regression line fitted to the scatter diagrams of onset latency versus peak response magnitude for individual cells. The regression line was used because there was ambiguity in determining onset latency of the smallest significant excitatory response due to a large variation in onset latencies among small excitatory responses. The latencies of the largest and smallest significant excitatory responses were estimated by introducing their magnitudes into the formula for the regression line. For the cell shown in Figure 11, for example, the regression line was y = –0.21x + 124.1 and the estimated latency of the maximal (155 spikes/s peak response magnitude) and smallest significant excitatory responses (59 spikes/s peak response magnitude) were 91.6 and 111.7 ms, respectively. Thus, the difference in estimated latency of the cell was 20.1 ms. Latency differences between the maximum and minimum significant excitatory responses were calculated for each of the 26 cells that showed significant excitatory responses to at least 10 stimuli and for which onset latency and peak response magnitude were significantly correlated (P < 0.05). The mean ± SD of the difference was 48.8 ± 41.1 ms. Similar estimates were made for the difference in peak latency and in decay time between the maximal and minimal excitatory responses. The average difference in peak latency calculated for the 17 cells that showed significant correlation was 67.0 ± 61.1 ms. The average difference in the decay time calculated for the 20 cells that showed significant correlation was 15.3 ± 24.8 ms.
Comparison Between TEav and TEad
Magnitude and Variance
Of the 213 cells examined in this study, 113 were recorded from TEav and 100 from TEad. Eighty-two of the 113 TEav cells (73%) and 87 of the 100 TEad cells (87%) were stimulus selective (P < 0.01 by ANOVA) and showed significant responses (P < 0.01 by K-S test) to at least one of the 100 stimuli. The proportion of such responsive cells was significantly smaller in TEav than in TEad (P = 0.009 by χ2 test). The proportion of cells with significant excitatory responses was significantly smaller in TEav (73/113) than in TEad (83/100, P = 0.002), while the proportion of cells with significant inhibitory responses did not differ significantly between TEav (30/113) and TEad (20/100, P = 0.797). Thus, the difference in the proportion of significant responses between the two areas was mostly due to the difference in the proportion of significant excitatory responses.
The magnitude of the maximal excitatory response of individual TEav cells tended to be smaller than that of individual TEad cells, while the spontaneous firing rate averaged over 100 responses and the magnitude of the maximal inhibitory response did not differ significantly between TEav and TEad cells. The magnitude of the maximal excitatory response, measured as the mean firing rate in the 400 ms response window minus the spontaneous firing level for individual cells, ranged from 2.5 to 141.0 spikes/s. The means ± SD were 22.5 ± 16.9 spikes/s (median 17.9) in TEav and 35.0 ± 27.9 spikes/s (median 27.0) in TEad. The two distributions differed significantly (P = 0.002 by U-test). On the other hand, the means ± SD of the averaged spontaneous firing rate were 7.2 ± 7.5 spikes/s (median 5.0) in TEav and 7.6 ± 6.9 spikes/s (median 6.0) in TEad. The two distributions were not significantly different (P = 0.275 by U-test). The magnitude of the maximal inhibitory response, measured as the spontaneous firing level minus the mean firing rate in the 400 ms response window, ranged from 4.0 to 28.4 spikes/s. The means ± SD were 10.8 ± 5.9 spikes/s (median 9.0) in TEav and 12.2 ± 6.4 spikes/s (median 11.5) in TEad. The two distributions were not significantly different (P = 0.208 by U-test).
There was no significant difference between TEav and TEad in the trial-by-trial variance of excitatory responses to specific stimuli. For each cell the trial-by-trial variances of responses to the 100 stimuli were plotted against their means on a log10 scale and the slope of the regression line was calculated. The variance was calculated for 52 TEav and 61 TEad cells. The remaining 30 TEav and 26 TEad cells had some responses smaller than 0, which could not be plotted on a log10 scale. The variances and means were significantly (P < 0.05) correlated in 51 of 52 TEav cells and 60 of 61 TEad cells. The slope of the regression line represents the magnitude of the variance in comparison to the mean. The larger the slope, the more variable the cell responses were. The slope was 1.01 ± 0.31 for TEav and 0.96 ± 0.34 for TEad, with no significant difference between the two distribuions (P = 0.375 by U-test).
To compare the overall effectiveness of individual stimuli in activating TEav cells versus TEad cells, the number of cells in which a stimulus evoked statistically significant (P < 0.01) excitatory responses was counted separately for each stimulus. The counts were determined separately for 73 TEav and 83 TEad cells that showed significant excitatory responses to at least one stimulus and normalized to the total number of cells with significant excitatory responses in each area. We refer to these normalized values as ‘overall effectiveness’. A stimulus effective for all the responsive cells would thus have an overall effectiveness of 1. An overall effectiveness of 0 means that the stimulus failed to elicit excitatory responses in any responsive cell.
The overall effectiveness in TEav and TEad is plotted in Figure 13 with the 100 stimuli aligned along the horizontal axis in the same order as in Figure 2. The overall effectiveness was generally lower in TEav than in TEad, which is consistent with a lower TWP<0.01 in TEav than in TEad (see below). The overall effectiveness of the complex object images (stimuli 1–74) tended to be larger than that of the 2-dimensional geometrical shapes (stimuli 75–100). The mean ± SD of the overall effectiveness in TEav was 0.13 ± 0.03 for the object images and 0.07 ± 0.04 for the geometrical shapes. In TEad it was 0.22 ± 0.05 for the object images and 0.18 ± 0.04 for the geometrical shapes. The two distributions were significantly different in both areas (P < 0.001 in TEav and P = 0.001 in TEad by U-test), although the difference was more prominent in TEav (with a z value of 5.7 for TEav but only 3.3 for TEad). There was a significant correlation between the overall effectiveness of individual stimuli in TEav and in TEad (r = 0.33, P = 0.001). However, a large part of this correlation was due to the common preference for images of complex objects compared to 2-dimensional geometrical shapes in both areas. There was no significant correlation between the overall effectiveness in TEav and in TEad among the 74 object images (r = 0.20, P = 0.089).
To deduce the factors that determined overall effectiveness, the stimuli were arranged according to rank order of overall effectiveness in TEav and in TEad. The upper part of Figure 14 shows this ranking for TEav. The stimuli are arranged from left to right and then from top to bottom. More colorful stimuli clustered in the upper part. In order to examine the possible correlation between colorfulness and effectiveness, the colorfulness of stimuli was quantified with a ‘Colorfulness’ index according to the formula
The lower half of Figure 14 represents Colorfulness against stimulus rank in TEav. There was a significant correlation between the overall effectiveness in TEav and Colorfulness of the stimuli (r = 0.47, P < 0.001). This significant positive correlation existed even for the 74 object images (r = 0.39, P = 0.001). These results indicate that the colorfulness of stimuli is one of the factors that determines the overall effectiveness of stimuli in activating TEav cells. There was no significant correlation between overall effectiveness in TEad and Colorfulness (r = 0.19, P = 0.056). A visual inspection of the arrangement of the stimuli sorted according to overall effectiveness in TEad did not hint at any basic factors that could determine the overall effectiveness in TEad besides the fact that some were object images and others were geometrical shapes.
Sharpness of Stimulus Selectivity
The averaged tuning curves of TEav and TEad cells (obtained by averaging response magnitudes of same rank order in individual cells) appear to have different shapes (Fig. 15, top left). However, the tuning curves obtained by averaging the magnitudes of responses, after normalization to the maximal response of individual cells, almost completely coincided with each other (Fig. 15, top right). Correspondingly, although TWP<0.01 values in individual TEav cells (median 4) were significantly lower than those of TEad cells (median 11; P = 0.002 by U-test; Fig. 15 bottom center), TW25 did not differ significantly between the two areas (median 15.3 in TEav and 22.5 in TEad; P = 0.066 by U-test; Fig. 15, bottom left). Therefore, there was no clear difference in the sharpness of stimulus selectivity between TEav and TEad cells. However, the amount of information transmitted by individual cells tended to be larger in TEad (median 0.45 bits) than TEav (median 0.34 bits; Fig. 15, bottom right). The two distributions of the amount of information were significantly different (P = 0.011 by U-test).
Changes in stimulus selectivity were observed throughout the response time course in both TEav and TEad cells. To compare the degrees of stimulus selectivity sharpening between TEav and TEad we compared the distributions of the differences between the initial and late parts of responses for TW25, TWP<0.01 and amount of information. There were no significant differences between the two areas (P > 0.05 by U-test).
There were no significant differences in the distributions of minimum onset or peak latency in individual cells between 51 TEav cells and 71 TEad cells (P = 0.96 for onset latency; P = 0.47 for peak latency; Fig. 16, top). The mean ± SD of minimum onset latency was 135.2 ± 92.1 ms (median 106) in TEav and 128.8 ± 75.3 ms (median 106) in TEad. Minimum peak latency was 160.8 ± 102.0 ms (median 121) in TEav and 140.4 ± 74.3 ms (median 115) in TEad. However, both onset and peak latencies were significantly longer in TEav than in TEad when a comparison was made between all the significant excitatory responses (P < 0.001 for both onset and peak latencies; Fig. 16, bottom). The mean ± SD of onset latencies of 520 responses obtained from 51 TEav cells was 133.2 ± 68.2 ms (median 113) and for the 1242 responses obtained from 71 TEad cells it was 115.9 ± 52.0 ms (median 102). The mean ± SD of peak latency was 161.7 ± 87.4 ms (median 131.5) in TEav and 142.9 ± 72.5 ms (median 119) in TEad. The discrepancy in significant differences between minimum latency of individual cells and latency of all significant excitatory responses was due to the abundance of TEad cells that responded to many stimuli with short latencies close to their minimum.
In both TEav and TEad cells a negative correlation was found between onset latency and peak height of responses within individual cells and between peak latency and peak height. A positive correlation was observed between decay time and peak height. The incidence of a negative correlation between onset latency and peak height of responses did not differ significantly between TEav (5/12 cells) and TEad (21/30 cells) (P = 0.088 by χ2 test). Likewise, the incidence of a negative correlation between peak latency and peak height of responses did not differ significantly between TEav (4/12 cells) and TEad (13/30 cells) (P = 0.551). Finally, the incidence of a positive correlation between decay time and peak height of responses did not differ significantly between TEav (5/11 cells) and TEad (15/28 cells) (P = 0.648).
Alert Versus Anesthetized Preparation
There are several previous studies of the stimulus selectivity of TE cells in anesthetized monkeys. By comparing the present results obtained in alert, fixating monkeys with those obtained in anesthetized monkeys we can deduce the effects of anesthesia on TE cell response. Compared with the previous results in anesthetized preparations, the proportion of responsive cells in alert, fixating monkeys was larger and the magnitudes of their maximal responses were greater. The proportion of unresponsive cells in TEad was only 16% in the present study, compared with 35% in TEad and TEpd in anesthetized monkeys (Tanaka et al., 1991). The mean of the maximal responses of responsive TEad cells was 40.5 spikes/s in the present study, whereas for the maximal responses of responsive cells in TEad and TEpd it was 17.5 spikes/s in Tanaka et al. (Tanaka et al., 1991) and 17.5 spikes/s in Kobatake et al. (Kobatake et al., 1994).
In contrast, the tunings of stimulus selectivity obtained in the present study were not very different from the tunings obtained in previous studies with anesthetized monkeys. Responses >25% of the maximal in individual cells were evoked only by one fifth of the 100 stimuli in TEad cells in the present study. Because most of the stimuli used in the present study were object images and contained multiple features, the tuning would have been sharper if we had measured the sharpness in the feature domain.
Time Course of Responses
We found that the response time course of cells in TEav and TEad depended on the stimulus. Different stimuli evoked responses with different time courses even in the same TE cells. Such stimulus-dependent temporal modulation of TE cell response was first reported by Richmond et al. (Richmond et al., 1987). In addition to confirming this previous finding, we found that a principal factor in the variation of the response time course is the difference between responses that have initial transient components only ~130 ms after stimulus onset and responses that have late components after 240 ms from stimulus onset in addition to the initial transient components. Thus, the late components are more selective than the initial components. However, the function of the later responses is not obvious. By detecting the no-go potential (Gemba and Sasaki, 1989; Sasaki et al., 1993) in the electroencephalogram of the human frontal cortex, Thorpe et al. found that perceptual judgment for the presence/absence of any animal in a complex scene ends within 150 ms after stimulus onset (Thorpe et al., 1996). Thus, the later responses may be used to recover the 3-dimensional reality of the object or to form memory traces in neuronal circuits. The sparser representation of object images in the later parts of responses has the advantage of increasing memory capacity (Field, 1994; Foldiak and Young, 1995).
Another principal factor in the response time course variation was the difference in onset and peak latencies, as reported by Richmond (Richmond et al., 1987; Liu and Richmond, 2000). The time for the firing rate to decay from the initial peak to the spontaneous firing level also varied. We found that stronger responses tended to have shorter onset and peak latencies and longer decay time from the initial peak to the spontaneous firing level. The rapid visual discrimination of complex stimuli found by Thorpe et al. (Thorpe et al., 1996) may be achieved by detecting the difference in onset and peak latencies, rather than by comparing the magnitudes of response peaks. The coding of information by response latency has also been discussed theoretically (Hopfield, 1995; Korner et al., 1999).
There are several other previous studies relevant to the stimulus-dependent time course of TE cells. Oram and Perrett analyzed the time course of cell responses in the upper bank of the superior temporal sulcus to various views of the human head (Oram and Perrett, 1992). They found that while onset latency was almost identical among responses to different head views, the stronger responses to good views had a longer decay than the weaker responses to bad views. Although the results with onset latency are not consistent with the present study, the decay time results agree. The difference in the onset latency results may be due to the difference in visual stimuli and/or differences in recording sites.
Tovee et al. and Gershon et al. calculated the cumulative information content and found that most of the information about the stimulus existed in the activity during the first 200 ms period after stimulus onset (Tovee et al., 1993; Gershon et al., 1998). Gochin et al. analyzed the time course of ensemble performance, which is the probability of successfully classifying stimuli based on the activity of a population of cells, and found a rapid increase in ensemble performance after stimulus onset (Gochin et al., 1994). Consistent with these previous findings, we found a rapid increase in information content after stimulus onset in the present study.
Sugase et al. have recently reported that the information content grew faster for global categories (human faces/monkey faces/simple shapes) than for fine categories (human or monkey facial expressions and identity) after stimulus onset (Sugase et al., 1999). The mean difference between the two peaks was 51 ms. The peaks of global category information coincided with the discharge peaks and the peaks of fine category information were located in the decay phase, where the discharge rate decayed from the initial peak to the spontaneous firing level or the maintained firing level. Thus, this finding of Sugase et al. (Sugase et al., 1999) may correspond to the finding by Oram and Perrett (Oram and Perrett, 1992) and ours that the speed of decay from the initial discharge peak varies depending on the stimulus. We assume that the rising phase of the initial discharge coincided between responses to faces with different expressions and identities, while some of the responses decayed faster than others and the variation in the discharge rate was greatest during the decay phase, thereby giving the most information about expressions and identities.
The difference in response onset latency and the negative correlation between onset latency and response magnitude can be explained by the difference in temporal summation requirements for action potential generation between optimal and less optimal stimuli. The optimal stimuli evoke strong excitatory synaptic potentials, which immediately trigger action potentials, while less optimal stimuli evoke weaker synaptic potentials for which temporal summation is needed to trigger action potentials. Such latency differentiation may occur not only in the cell being recorded, but also in cells located in its afferent pathway. It is also possible that weaker inputs evoked by less optimal stimuli need to be supported by additional excitatory inputs conveyed through lateral interaction to trigger action potentials, while strong inputs evoked by optimal stimuli trigger action potentials by themselves. The inputs conveyed through lateral interaction come later than the direct feed-forward afferent inputs.
We can suggest two possible mechanisms for the later sharpening of stimulus selectivity. One possibility is that there is a correlation between selectivity and onset latency in the inputs from lower stages. This supposes that less selective inputs are conducted more quickly while more selective inputs are conducted more slowly. Then, the initial responses evoked by the fast inputs will be less selective than those evoked by the slow inputs. We do not think this is plausible, because while we predict, on the same supposition, that TE cells with longer onset latencies are more selective, we did not find any significant net correlation between either TW25 or TWP<0.01 and minimum onset latency of individual cells. A second possibility is mutual inhibition between TE cells. A relatively large number of TE cells are first activated, but weaker responses disappear through mutual inhibition and only strong responses make it to the later response phase. Indeed, we found that the start of the inhibitory response coincided with decay of the excitatory response. Excitatory interactions between TE cells may also contribute to selection. Cells with excitatory connections to other activated cells would remain active beyond the initial phase. Fujita and Fujita showed extensive horizontal connections in TE (Fujita and Fujita, 1996). Mutual interactions between or within columns can explain both the differences in the decay speed from the initial peak and the magnitudes of late response components.
Functional Differences between TEav and TEad
As for differences in the properties of responses between cells in TEav and TEad, we found that: (i) TEav cells were more difficult to activate than TEad cells and the maximal responses in individual TEav cells tended to be smaller than those of TEad cells; (ii) TEav cells tended to respond more strongly to more colorful stimuli, while such a tendency was not found in TEad. These results are consistent with the previous anatomical finding that TEav and TEad have differential afferent and efferent connections. However, it seems difficult to relate the differences found in the present study with previous results that the cooling or lesioning of TEav impaired performance on a memory task while similar treatment of TEad impaired performance on a color discrimination task (Horel, 1994a,b; Buckley et al., 1997). The present results and the results of Horel and Buckley et al. may reveal different functions of TEav and TEad.
Although we did not examine the responses of cells in the superior temporal sulcus in the present study, previous studies have found some unique properties in cells of the superior temporal sulcus [reviewed by Allison et al. (Allison et al., 2000)]. Baylis et al. found that cells selectively responding to faces were more numerous in areas TPO, TEa and TEm than in other sub-regions of the inferotemporal cortex (Baylis et al., 1987). Janssen et al. found that cells sensitive to disparity gradients were more numerous in the lower bank of the superior temporal sulcus than in TEad (Janssen et al., 2000). The present results, together with the previous results described above, suggest that the inferotemporal cortex is composed of several functionally distinct sub-divisions.
The tuning width defined at 25% of the maximal response (TW25) was not significantly different between cells in TEav and TEad, while the number of effective stimuli that evoked significant responses in individual cells (TWP<0.01) was significantly larger in TEad than in TEav and the information transmitted by responses of single cells was significantly greater in TEad than in TEav. TWP<0.01 and the amount of information are dependent on the trial-by-trial response variance to given stimuli as well as the shape of the tunings. TW25 might also be influenced by the trial-by-trial variance: responses with real magnitudes just below 25% may happen to exceed 25% due to the variance and responses just above 25% may happen to be smaller than 25%. However, if the number of repetitions is large enough compared with the variance, this influence will be minimal. We compared TW25 measured over the averaged responses of 10 repetitions, with TW25 measured on half of the data (five repetitions) for 54 cells for which we had completed 10 repetitions of the 100 stimuli. There was a strong correlation between the two sets of TW25 measures (r = 0.98) and the slope of the regression line was close to 1 (slope = 0.97). Therefore, we believe that TW25 was the most direct measure of the tuning shape and concluded that stimulus selectivity did not differ significantly between TEav and TEad cells.
However, because most of the stimuli used in the present study were object images, we can not deduce the features critical to selectivity. Horel suggested that TEad held information about fine stimulus features (Horel, 1994a). Although Horel (Horel, 1994a,b) did not mention it, a possibility that follows from this notion is that the global features of stimuli are processed in TEav. It is still possible, despite the present results, that the stimulus selectivity of TEav and TEad cells is determined by different stimulus domains.
The probability of a real complex object image evoking significant responses was significantly larger than the two- dimensional geometrical shapes in both TEav and TEad cells. We have previously shown that most cells in TEad respond to moderately complex features but not to whole object images (Tanaka et al., 1991; Fujita et al., 1992; Kobatake et al., 1994). The superiority of object images to geometrical shapes for TEad cells can be easily understood by considering that complex object images contain many features. The superiority of object images to geometrical shapes was more prominent for TEav than TEad cells. The above explanation for TEad cells may also hold for TEav cells, but it is also possible that TEav cells respond preferentially to whole object images rather than to moderately complex features.
A superiority of colorful object images to less colorful ones was found in TEav but not in TEad. This may appear inconsistent with the two lines of previous findings. One is the presence of color-selective cells in TEad (Desimone et al., 1984; Tanaka et al., 1991; Komatsu et al., 1992). The second is the effects of lesioning and inactivation of TEad on color discrimination tasks. Horel found that cooling TEad degraded simple red–green and blue–yellow discrimination (Horel, 1994b) and Buckley et al. found that a lesion of TEad impaired discrimination of small differences in hue or color saturation (Buckley et al., 1997). However, Horel and Buckley et al. did not examine the effects of cooling or lesioning TEav and there are no previous studies on the color selectivity of TEav cells. The color selectivity of TEav cells, especially with complex images, may be more prominent than in TEad cells. Indeed, there is an abstract (Sato and Kawamura, 1990) reporting that color-sensitive cells were more frequently observed in the ventral region than in the dorsal region.
The minimum onset latencies of individual cells were not significantly different between TEav and TEad. This result is consistent with previous anatomical studies and supports the idea that TEav and TEad are located at the same hierarchical levels of different serial pathways rather than at serial stages along a single pathway. Martin-Elkins and Horel showed that the anterior inferior temporal gyrus between the anterior middle temporal sulcus and the rhinal sulcus (which included a part of TEav) received strong projections from the posterior regions located along the occipitotemporal sulcus on the ventral surface of the brain (Martin-Elkins and Horel, 1992). The anterior inferior temporal gyrus does not receive projections from the posterior regions on the lateral surface of the brain, from which TEad receives strong projections (Desimone et al., 1980; Shiwa, 1987; Webster et al., 1991; Saleem et al., 1993). Based on these data, it has been suggested that TEav and TEad receive their visual afferent inputs through different pathways in parallel (Martin-Elkins and Horel, 1992; Yukie et al., 1992). We have recently confirmed this with more localized dye injections into TEav and TEad (Saleem et al., 2000).
We wish to thank Dr Hidekazu Kaneko for his contribution to the data analysis. This work was supported by RIKEN Brain Science Institute and the Agency of Industrial Science and Technology.
Address correspondence to Hiroshi Tamura, Department of Bio- physical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka 560-8531, Japan.
|Brodmann||Selzer and Pandya||Felleman and Van Essen|
|The correspondences are not perfect.|
|TEad||Area 21||TE2||AITv, AITd|
|Brodmann||Selzer and Pandya||Felleman and Van Essen|
|The correspondences are not perfect.|
|TEad||Area 21||TE2||AITv, AITd|