WWW.JNEUROSCI.ORG
-
The Journal of Neuroscience PeproTech - Your Source for Neuroscience Research Reagents
 QUICK SEARCH:   [advanced]


     
-


HOME
  |  
SEARCH  |   ARCHIVE  |   SUBSCRIBE  |   CONTACT  |   HELP

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Submit an eLetter
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (45)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Bair, W.
Right arrow Articles by Movshon, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bair, W.
Right arrow Articles by Movshon, J. A.

 Previous Article  |  Next Article 

The Journal of Neuroscience, April 15, 2002, 22(8):3189-3205

The Timing of Response Onset and Offset in Macaque Visual Neurons

Wyeth Bair1, 2, James R. Cavanaugh2, Matthew A. Smith2, and J. Anthony Movshon1, 2

1 Howard Hughes Medical Institute and 2 Center for Neural Science, New York University, New York, New York 10003


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

We used fast, pseudorandom temporal sequences of preferred and antipreferred stimuli to drive neuronal firing rates rapidly between minimal and maximal across the visual system. Stimuli were tailored to the preferences of cells recorded in the lateral geniculate nucleus (magnocellular and parvocellular), primary visual cortex (simple and complex), and the extrastriate motion area MT. We found that cells took longer to turn on (to increase their firing rate) than to turn off (to reduce their rate). The latency difference (onset minus offset) varied from several to tens of milliseconds across cell type and stimulus class and was correlated with spontaneous or driven firing rates for most cell classes. The delay for response onset depended on the nature of the stimulus present before the preferred stimulus appeared, and may result from persistent inhibition caused by antipreferred stimuli or from suppression that followed the offset of the preferred stimulus. The onset delay showed three distinct types of dependence on the temporal sequence of stimuli across classes of cells, implying that suppression may accumulate or wear off with time. Onset latency is generally longer, can be more variable, and has marked stimulus dependence compared with offset latency. This suggests an important role for offset latency in assessing the speed of information transmission in the visual system and raises the possibility that signal offsets provide a timing reference for visual processing. We discuss the origin of the delay in onset latency compared with offset latency and consider how it may limit the utility of certain feedforward circuits.

Key words: macaque monkey; primary visual cortex; area MT/V5; lateral geniculate nucleus; spike timing; response latency; integration time; inhibition; spontaneous activity; temporal dynamics


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

In the earliest studies of neuronal response in the visual system, ON and OFF responses to light were recognized because light onset and offset both produced striking transient bursts of action potentials (Adrian and Matthews, 1927). If it had been otherwise, if cells had produced ON responses only and did not adapt so much, then perhaps response offsets would have received more attention in the last 75 years. If asked whether neurons in the visual system turn on or turn off sooner after a scene change, one might guess the former because a brief pulse of light causes a response that rises rapidly and decays slowly, outlasting the flash (Adrian and Matthews, 1927; Levick, 1973) or one might guess the latter because of the integration time required for a spiking neuron to reach threshold (Lapicque, 1907). The literature since then provides an ample account of the timing of response onsets (for review, see Nowak and Bullier, 1997) but is surprisingly quiet about response offset.

Here we study the latency of response onset and offset in several areas of the visual system because doing so offers a chance to interpret integration strategies in each area in the context of what is known about the other areas. To make this comparison across visual areas and cell classes, we attempt to generalize the notion of preferred, antipreferred, and null (or neutral) stimuli across three levels in the visual system. We optimized stimuli for the center-surround receptive fields (Barlow, 1953; Kuffler, 1953) of cells in the dorsal lateral geniculate nucleus (Hubel and Wiesel, 1961), for the spatial phase, orientation, and direction-selective (DS) cells in the primary visual cortex (V1) (Hubel and Wiesel, 1959), and for the DS cells of the extrastriate motion area MT/V5 (Zeki, 1974; Maunsell and Van Essen, 1983; Albright et al., 1984; Movshon et al., 1985). Sinusoidal gratings confined to the classical receptive field adequately activate all of these cell types (Enroth-Cugell and Robson, 1966; Movshon et al., 1978), but here we present them in rapid, random succession with their canonical opposites to facilitate the precise estimation of timing.

In the retina and LGN, there is probably no strong opponency between the ON and OFF pathways (Casagrande and Norton, 1991; Schiller, 1992). In V1, simple cells may receive push-pull feedforward inputs (Palmer and Davis, 1981; Heggelund, 1986; Ferster, 1988; Tolhurst and Dean, 1990; Hirsch et al., 1998; Troyer et al., 1998) (for review, see Ferster and Miller, 2000) or a less spatially specific inhibition (Borg-Graham et al., 1998; Troyer et al., 1998; Wielaard et al., 2001). Thus, rapid switching between preferred and counterphase stimuli would engage both pathways. Opponent circuits may operate in direction-selective cells (Sutherland, 1961; Barlow and Hill, 1963; Adelson and Bergen, 1985) (but see, Raymond and Braddick, 1996), but perhaps are different in V1 and MT (Qian and Andersen, 1994; Heeger et al., 1999).

For all cell types that we studied, response onset came later than response offset. We discuss how much of the onset delay is caused by neuronal integration time (defined to be the time from initial depolarization to spike threshold, Nowak and Bullier, 1997), how much can be attributed to inhibition, and how much is inherited with inputs from lower areas.

Some of these results have been published previously in abstract form (Bair et al., 2001).


    MATERIALS AND METHODS
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

Electrophysiology. We recorded extracellularly from single units in the dorsal LGN, primary visual cortex, and area MT of anesthetized, paralyzed, macaque monkeys (Macaca fascicularis). LGN, V1, and MT data were collected from 3, 16, and 13 monkeys, respectively. The numbers of animals for V1 and MT are large because data for this study was collected sporadically during experiments performed for other studies.

Detailed methods for this type of recording are available in Carandini et al. (1997) and O'Keefe and Movshon (1998). Experiments typically lasted 4-5 d during which anesthesia and paralysis were maintained with sufentanil citrate (4-12 µg · kg-1 · hr-1) and vecuronium bromide (Norcuron; 0.1 mg · kg-1 · hr-1), respectively, administered in lactated Ringer's solution. Infusion solutions were mixed to 2.5% dextrose concentration to provide adequate nutrition, and infusion rate was adjusted to maintain fluid balance (~4-8 ml · kg-1 · hr-1). Artificial respiration with a mixture of O2, N2O, and CO2 was maintained with rate adjustments to keep expired PCO2 between 3.8 and 4.0%. Body temperature was maintained near 37°C with a heating pad. EEG and electrocardiogram were monitored to ensure proper depth of anesthesia. The pupils were dilated with topical atropine, and the corneas protected with gas-permeable hard contact lenses. We refracted the eyes with supplementary lenses that were chosen to optimize neuronal responses to high spatial frequencies. All procedures conformed to guidelines of the New York University Animal Welfare Committee.

Tungsten-in-glass microelectrodes (Merrill and Ainsworth, 1972) were advanced with a hydraulic microdrive downward through a craniotomy of diameter 9-10 mm. In some experiments, we used a mechanical microdrive system with quartz-platinum-tungsten microelectrodes (Thomas Recordings, Marburg, Germany). For V1 recordings, the craniotomy was typically centered 4 mm posterior to the lunate sulcus and 10 mm lateral to the midline. For LGN recordings, the craniotomy was centered 7 mm anterior to ear-bar zero and 11 mm lateral to the midline. For MT recordings, the craniotomy was centered 15 mm lateral to the midline, 4 mm posterior to the lunate sulcus, and the angle of advance was 20° down and forward in the parasagittal plane. Action potentials were discriminated using a hardware dual-window time-amplitude discriminator (Bak, Germantown, MD) and time stamped at a resolution of 0.25 msec. Electrolytic lesions were made for histological verification and estimation of cortical layer. V1 neurons were recorded on the operculum and in the calcarine sulcus (typical receptive field eccentricities were 2-5° and 8-24°, respectively). LGN cells were recorded from magnocellular and parvocellular layers at eccentricities ranging from 1 to 23°. MT cells were recorded at eccentricities ranging from 2 to 33° but typically between 3 and 12°. For each cell whose action potential waveform was well isolated from the noise, we ran stimuli as described below.

Visual stimuli. Visual stimuli were generated by custom software on a CRS 2/2 board (Cambridge Research Systems, Kent, UK) under the control of an Intel 86-based host computer. Stimuli were presented on a standard cathode ray tube at a resolution of 1024 × 731 pixels and a video frame rate of 100 Hz vertical refresh, with a mean luminance of 33 cd/m2. The display was gamma-corrected with a lookup table. We used a front surface mirror to bring the receptive field (RF) of each cell into register with the center of a video monitor placed between 80 and 180 cm from the animal's eye, where it subtended between 10 and 22°. The graphics board that generated our stimulus also generated synchronization pulses that were time-locked to the start of the first frame of our stimulus. These pulses were time-stamped and recorded by the same system used for collecting action potential times.

The RF size was estimated by hand before beginning quantitative characterization with sinewave gratings. Quantitative estimates of RF properties were computed from tuning curves resulting from a series of randomly interleaved stimuli. Responses to drifting sinusoidal stimuli were quantified with DC (mean firing rate minus the baseline rate for a mean gray stimulus) and F1 (amplitude of the Fourier component of the response at the temporal frequency of the stimulus) tuning curves.

For V1 cells, drifting sinusoidal gratings were randomly interleaved under computer control to obtain response tuning curves first for orientation, next for spatial frequency, and then for temporal frequency. Next, we chose the smallest patch of optimized grating that elicited a response easily distinguishable from the spontaneous rate, and we alternated between adjusting the vertical and horizontal position of the patch by hand until the maximal response was obtained. At these coordinates, circular patches of various diameters were interleaved to obtain a tuning curve for size. The classical receptive field (CRF) of the V1 cell was defined to be the smallest circular patch that gave a response no <95% of the maximum.

Cells were classified as simple or complex on the basis of the modulation index computed at the optimal spatial frequency (Skottun et al., 1991). For simple cells, the optimal phase of the grating was subsequently determined from an eight-point tuning curve for static, contrast modulated gratings. For complex cells and MT cells, we quantified directionality using the index 1 - a/p, where p and a were the responses to preferred direction (that which gave the highest response) and the antipreferred direction (opposite to preferred), respectively, in excess of the spontaneous rate (Maunsell and Van Essen, 1983). Cells were called DS if their directionality was >0.5.

LGN cells were characterized in a manner similar to V1 simple cells with two exceptions. First, LGN cells were mapped with a white noise stimulus in which squares in a 16 × 16 spatial grid were independently assigned zero or maximum luminance on every video frame on the basis of a pseudorandom number generator (Reid et al., 1997). The grid was scaled so that two or three boxes of the grid spanned the center of the LGN RF. A spatial map was computed using reverse-correlation to determine the location, size, and ON or OFF character of the RF center and surround. Second, orientation was set to be vertical (with rightward drift) unless our by-hand characterization or the white-noise spatial map indicated a significant orientation bias. Magnocellular and parvocellular cells (hereafter, m-cells and p-cells) were distinguished using a white-noise stimulus, as described below.

Random sequence stimuli. After the initial characterization of a cell, we studied response timing using random binary and ternary stimulus sequences. For the binary sequences, either the preferred stimulus (P) or the antipreferred stimulus (A) was presented on each video frame (i.e., every 10 msec). The choice between A and P was governed by a pseudorandom sequence generated using the ran2 algorithm of Press et al. (1992). In later experiments, the randomization was governed by a binary m-sequence (Sutter, 1987; Reid et al., 1997). Ternary sequences, which included a null stimulus (N) were governed by the ran2 algorithm and consisted of the equiprobable and independent presentation of A, N, or P on each video frame.

For LGN cells, we ran binary sequences with three types of P and A stimuli: spots, annuli, and gratings. (1) Spots: P was a disk of maximum or minimum luminance (for ON or OFF cells, respectively) presented on a gray background and confined to the central region of the RF determined from the reverse-correlation map. A was the disk of opposite contrast to P. (2) Annuli: P was an annulus of maximum or minimum luminance that was confined to the surround determined from the spatial reverse-correlation, and A was the annulus of opposite luminance. (3) Gratings: P was a centered, circular patch of sinusoidal grating having optimal spatial period and phase and covering the RF center and surround. A was counterphase to P, i.e., it had 180° opposite phase.

For V1 simple cells, we used two sets of P and A stimuli. For both sets, P was the optimal sinusoidal grating confined to the CRF (see above), and A was either counterphase or orthogonally oriented to P. We will refer to these two stimuli as the phase and orientation stimuli, respectively. For V1 complex DS cells and for MT cells, P and A were optimal gratings that differed in their direction of movement: P moved in the preferred direction, and A moved in the opposite direction. The amount of movement between video frames was typically 90° of phase but was set to 45° if the cell responded poorly to 90° movements.

LGN m- and p-cells were distinguished on the basis of the presence of contrast gain control (of the type described by Shapley and Victor, 1978) in m-cells and its absence in p-cells (Benardete et al., 1992; Lee et al., 1994; Lee, 1996; Benardete and Kaplan, 1997, 1999; Levitt et al., 2001). Classification was based on the time-domain kernels (Benardete and Kaplan, 1997, 1999) computed for the binary counterphase stimulus and always agreed with our assessment on the basis of contrast sensitivity, transience, temporal resolution, and estimates of the laminar location of the recording.

Data analysis. For all analyses, the times of action potentials were expressed in milliseconds relative to the time at which the raster scan of the video display illuminated the center of the screen, where each neuronal receptive field had been centered. We determined the timing of our stimulus relative to the video synchronization pulses by plotting the luminance at the center of the screen (measured with a photometer) on an oscilloscope that was triggered by the video synchronization pulses. We then measured the timing of our data collection system by passing the video synchronization pulses through the same amplifiers and spike discrimination hardware that we used to detect action potentials. These two measurements allowed us to compensate all spike times for the delays associated with stimulus generation and data collection.

We estimated the mean instantaneous firing rate (i.e., the peristimulus time histogram at the millisecond resolution) associated with particular stimulus patterns that occurred at random in our binary and ternary sequences. For example, the sequence AP indicates 30 msec (three video frames) of the antipreferred stimulus followed by 20 msec (two frames) of the preferred stimulus. The time of occurrence of the transition, defined to be the time of appearance of the first frame of P, was taken as the reference time (t = 0). Spike train segments for each occurrence of a stimulus pattern were aligned to the reference time and averaged to estimate the mean instantaneous firing rate associated with that pattern. Average responses to stimulus transitions were always compared with the response for a reference pattern (the reference response) that had no transition. For example, the reference stimulus pattern for AP was 50 msec (five frames) of A. Figure 1 shows examples of responses to stimulus transitions and reference responses. Traces were smoothed with a Gaussian of SD 1 msec before further analysis.



View larger version (23K):
[in this window]
[in a new window]
 
Figure 1.   The estimation and comparison of response timing for turning on and turning off are demonstrated for an LGN p-cell. Top, Stimulus icons show preferred and antipreferred stimuli, P and A, one of which was chosen randomly for presentation every 10 msec. A, Average firing rate versus time is plotted for two 50 msec stimulus sequences, which are depicted below the abscissa. Solid lines indicate the stimulus and response for a transition from A to P (i.e., a change from the left to right stimulus icon, top), whereas dashed lines indicate the reference stimulus, 50 msec of A, and its response. Time 0 is when the transition to P occurred. Response latency was ~30 msec; therefore, the first and last 20 msec of the response traces are averages of responses to a random set of sequences that occurred before and after the 50 msec trigger sequences (see arrow and label ongoing random sequence) and should not be confused with spontaneous firing rate. Epochs of A were associated with near zero firing rate, and the onset of P caused a rapid rate increase (solid response curve). Response curves were based on 253 occurrences of the stimulus sequences (see Materials and Methods). B, Responses of the same cell to the PA transition (a change from the right to left stimulus icon, top) are shown in the format of A. Firing rate was high for P epochs and dropped rapidly after the transition to A. Response curves were based on 253 occurrences of the stimulus sequences. C, The difference between the response to the AP transition and to its reference stimulus (in A, solid minus dotted line) is plotted here as the thin line. The analogous difference between the traces in B is plotted as the thick line. These response difference traces allow direct comparison of the timing of the onset of signals evoked by the AP and PA stimulus transitions. We defined Delta AP to be the difference in timing between the onsets of the AP and PA response (thick bar; see Materials and Methods).

The response difference trace for a stimulus transition was computed by subtracting the reference response from the response to the transition. Response onset latency was determined automatically by finding the maximum point on the response curve (or on the response difference curve) and searching backwards in time from the maximum for the first point that was above 5% of the maximum response. A similar procedure was used to find response offset latency, except the minimum point and the 5% drop to minimum were used. We also computed latencies for the rise (and fall) to 50% and found that our results were not significantly changed.

Our measure of the timing difference for turning on relative to turning off was Delta AP, the onset latency for the response to the AP transition minus the offset latency for the response to the P-to-A (PA) transition. For ternary sequences, which contained antipreferred and null (in addition to preferred) stimuli, we computed Delta AP and a comparable measure for the N stimuli, Delta NP, which we defined as the latency for turning on from N minus the latency for turning off based on the PA transition. The PA transition latency was used for the off reference time for both Delta NP and Delta AP because it was on average not different from, yet less variable than, the P-to-N (PN) latency, and it provided a common reference for both measurements.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

We measured response timing for 31 cells in the LGN (14 p-cells and 17 m-cells), 63 cells in V1 (18 simple, 11 complex non-DS, 34 complex DS), and 39 DS cells in MT. All cells were tested with random, binary sequences of visual stimuli in which either the preferred stimulus P or the antipreferred stimulus A appeared every 10 msec. We will begin by demonstrating the computation of our response timing measurements for the binary stimulus and will summarize these measurements across cell types. For each cell type, we will show how timing correlated with spontaneous and evoked firing rates. We will then assess the influence of antipreferred stimuli on response timing by comparing them with N, which are hypothetically neutral. Finally, we show that timing is influenced in different ways across areas and cell types by the temporal history of the stimulus.

Latency for response onsets and offsets

Figure 1 demonstrates our method for measuring onset and offset latency for an LGN p-cell that was tested with the phase stimulus. Every 10 msec, either the preferred stimulus P (the optimal sinusoidal grating) or the antipreferred stimulus A (the grating opposite in phase) was presented (Fig. 1, Top, right and left icons). We estimated the onset latency by comparing the response for the A-to-P (AP) transition with that for a reference stimulus that contained no transition to P. The AP transition was defined to be 30 msec of A followed by 20 msec of P, and the reference stimulus, lacking the transition, was 50 msec of A (Fig. 1A, solid and dashed lines, respectively; below abscissa). Because these 50 msec stimulus sequences appeared in an ongoing random sequence, the stimuli preceding and after them were random. In Figure 1A, the time at which the response to the AP transition (solid line) turns upward (open arrow) from the reference response (dashed line) is the onset latency. The latency is ~27 msec relative to the stimulus transition, which occurs at time 0. The upturn in the reference trace (dashed line) near 45 msec results from responses to random sequences that followed the trigger sequence and does not influence our timing measurements. We estimated the offset latency in a similar manner from the response to the P-to-A (PA) transition (30 msec of P followed by 20 msec of A) and its reference stimulus (50 msec of P) (Fig. 1B). The response to the PA transition diverges from the reference response ~25 msec after the stimulus transition (B, open arrow). To facilitate the comparison of onset and offset latencies, we plotted the AP response minus its reference response together with the PA response minus its reference. These response difference plots (Fig. 1C) make it apparent that the response decrease for the PA transition (thick line) occurred before the response increase for the AP transition (thin line) for this neuron.

Figure 2 shows response difference plots for cells from LGN, V1, and MT that were tested with random, binary sequences of P and A stimuli that were suited to the properties of the cells on the basis of initial characterization with sinusoidal stimuli. The phase stimulus, presented to LGN p- and m-cells and to V1 simple cells (A-C, respectively), covered the classical center and surround of the LGN RF and was restricted to the CRF for V1 cells. V1 simple cells were also tested with the orientation stimulus (D), which differed from the phase stimulus only by a 90° rotation of the antipreferred sinusoid. V1 complex DS cells (E) and MT cells (F) were tested with the direction stimulus (icon above E), which was an optimally oriented grating that moved in either the preferred or opposite direction every 10 msec. For each example in Figure 2, the response difference trace for the PA transition (thick lines) dropped from zero before the trace for the AP transition rose (thin lines). We quantified the offset and onset times for each cell from the respective response difference plots as the time to reach 5% of the maximum excursion from zero (see Materials and Methods).



View larger version (40K):
[in this window]
[in a new window]
 
Figure 2.   Response decreases occurred sooner than response increases when switching between P and A. For five classes of neurons, response difference plots (defined in Fig. 1) for PA and AP transitions (thick and thin lines, respectively) are shown for example cells responding to binary random sequences of optimized sinusoidal grating stimuli. A, Difference plots for an LGN p-cell responding to the phase stimulus (transitions between opposite phases, icons, top of left column) show that the PA response occurred before the AP response. B, For an LGN m-cell responding to the phase stimulus, a smaller timing asymmetry is present. The sign reversal at ~40 msec resulted from the combination of the transient nature of the m-cell response and the chance transitions that followed the reference stimuli (e.g., 50 msec of A was sometimes followed by P and vice versa). C, Difference plots for a V1 simple cell responding to the phase stimulus show a timing asymmetry larger than that observed for the LGN cells in A and B. D, Responses of a V1 cell to transitions between orthogonal orientations (icons in center) also show a large timing asymmetry. Responses of a V1 complex DS cell (E) and an MT cell (F) to transitions between opposite directions of motion (icons, top of right column) show a timing asymmetry as well.

Onset latency is plotted against offset latency for all cells in Figure 3. The points fell mainly above the diagonal line of equality, indicating that offset time was less than onset time. For each cell class, onset and offset times were significantly correlated (Pearson's r ranged from 0.60 to 0.97, p < 0.004, for each cell class). Our median LGN onset latencies (24 and 34 msec for m- and p-cells, respectively) fell within the range of medians reported by Maunsell et al. (1999), and our minimum onset latency (20 msec) was larger than theirs (16-18 msec). In V1, our shortest onset latencies (26-30 msec) were consistent with the shortest reported in the literature (Bartlett and Doty, 1974; Maunsell and Gibson, 1992; Nowak et al., 1995). For MT, however, 5 of 39 cells responded before 35 msec, which was faster than most published minimum latencies for MT (Raiguel et al., 1989, 1999; Lagae et al., 1993; Schmolesky et al., 1998; Raiguel et al., 1989, 1999; Lisberger and Movshon, 1999) but was consistent with a report of responses from 30 to 40 msec in area MST (Kawano et al., 1994). Overall, our mean onset latencies (Table 1) were smaller than most published mean values but were not inconsistent with published minimum latencies. This was expected for our rapidly changing stimulus because visual responses are known to be faster for rapid and broadband temporal stimuli (Shapley and Victor, 1978; Sestokas and Lehmkuhle, 1986; Movshon et al., 1990; Reid et al., 1992; Lagae et al., 1993; Kawano et al., 1994; Bair et al., 1997; Lisberger and Movshon, 1999).



View larger version (23K):
[in this window]
[in a new window]
 
Figure 3.   A comparison of onset and offset latencies across cell types and stimulus categories. The latency of the response to the transition from antipreferred to preferred (onset latency) is plotted against the latency of the response to the opposite stimulus transition (preferred to antipreferred, offset latency). Nearly all points fell above the diagonal line of equality, indicating that onset latency is longer than offset latency. The mean onset and offset latencies for each cell class and stimulus type are reported in Table 1.


                              
View this table:
[in this window]
[in a new window]
 
Table 1.   Average response offset and onset latencies for the data plotted in Figure 3

Our offset latencies, being consistently smaller than onset latencies, were smaller than the minimum latency values reported in nearly all previous extracellular studies of these visual areas in the macaque monkey. Specifically, our earliest offset latencies were between 15 and 20 msec in the LGN and between 20 and 25 msec in V1 and MT (Fig. 3, horizontal axis; see Table 1 for mean values). This is not inconsistent with physical limitations of the circuitry, and, given early responses in the LGN, is consistent with there being only several milliseconds of delay from LGN to cortex (Reid and Alonso, 1995, reported 1-4.5 msec for cat) and a 1-2 msec conduction delay from V1 to MT (Movshon and Newsome, 1996). Thus, offset latencies for our dynamic stimuli indicate that the flow of information through visual cortex can be faster than revealed by most extracellular studies of onset latency.

To quantify the difference between onset and offset latency for each cell, we defined Delta AP (Fig. 1C, thick bar) to be onset time minus offset time. Measurements of Delta AP across cell types and stimuli are summarized in Figure 4. For the LGN, in addition to the sinusoidal stimuli just described, cells were also tested with a constant-luminance disk in the center of the RF (A) and an annulus that omitted the center (B). The average value of Delta AP was significantly smaller for m-cells than for p-cells when the stimulus was limited to the center of the RF (Fig. 4A, compare white with gray histograms; arrows show means; see legend for statistics). For surround stimulation, the distributions of Delta AP for p- and m-cells were statistically indistinguishable (B). Results for the phase stimulus (C) were well matched to those for the center disk. ON and OFF center LGN cells showed no differences in timing measurements and were grouped together. Compared with the LGN, V1 simple cells had larger and more varied values of Delta AP. This was true for responses to the phase stimulus (D) and the orientation stimulus (E). Complex DS cells in V1 (F), however, had an average Delta AP that was significantly less than that for simple cells and was more similar to the distribution for the LGN. The distribution of Delta AP for MT cells (G) was similar to that for V1 complex DS cells.



View larger version (59K):
[in this window]
[in a new window]
 
Figure 4.   The distribution of the timing asymmetry, Delta AP, across cell types and stimulus categories. A, LGN p-cells (gray bars, n = 13) and m-cells (white bars, n = 12) had Delta AP > 0 when driven by a disk in the RF center. The mean for p-cells, 8.9 msec (black arrow), was significantly greater than that for m-cells, 4.8 msec (white arrow; t test, p = 0.017). B, When tested with an annulus in the RF surround, p- and m-cells had similar values of Delta AP (means, 7.8 and 7.2 msec, respectively). Arrows showing means overlap. C, For the phase stimulus, p-cells (n = 14) had a significantly larger Delta AP than m-cells (n = 17; means, 10.1 and 6.5 msec; t test, p = 0.007). Black and white arrows show means for p- and m-cells, respectively. D, V1 simple cells tested with the phase stimulus had more varied and larger Delta AP values on average (mean, 22 msec; n = 12) than Delta AP for the LGN. E, The distribution of Delta AP for V1 cells tested with the orientation stimulus was similar (mean, 22 msec; n = 14) to that for the phase stimulus. F, V1 complex DS cells (n = 32) tested with the direction stimulus had on average a smaller (9.5 msec) and less scattered value of Delta AP than simple cells tested with static gratings. G, MT cells (n = 34) tested with the direction stimulus had a distribution of Delta AP similar to that for V1 complex DS cells (mean, 10.9 msec).

Response latencies in V1 are known to be widely distributed, and Figure 4, D and E, shows that Delta AP was widely distributed for V1 simple cells. Do cells with long latencies have large Delta AP? For LGN p-cells and V1 simple cells, we found a positive correlation between Delta AP and onset latency (r = 0.70, p = 0.006, n = 14 for LGN p-cells; r = 0.88, p = 0.0004, n = 11 for V1 simple cells tested with the phase stimulus; r = 0.87, p = 0.00001, n = 16 for simple cells tested with the orientation stimulus). There was a weak correlation between Delta AP and onset latency for area MT (r = 0.36; p = 0.02; n = 39), but no significant correlation for LGN m-cells (r = 0.17; p = 0.51; n = 17) or for complex DS cells in V1 (r = 0.22; p = 0.22; n = 34). Thus, the correlation between Delta AP and onset latency was strongest for cell types that had the largest variations in onset latency, namely LGN p-cells and V1 simple cells. This is apparent in Figure 3, where the open and filled red circles and the filled black circles lie farther from the diagonal line at higher onset latencies.

In summary, for almost all of our cells, responses turned off faster than they turned on: only rarely was Delta AP less than zero. Cells with conspicuously long onset latencies tended to have large Delta AP values, suggesting that the time required to initiate a response can be substantially longer than the time required to decrease, or maintain, an ongoing response.

Eleven non-DS complex cells were tested with the orientation stimulus, but none responded to the fast changes between preferred and antipreferred stimuli in a manner similar to that of the other cell types. In particular, their firing rates decreased for both PA and AP transitions. Results for these cells cannot be compared directly to those presented here; therefore, complex non-DS cells are not included in our analysis.

The relationship between Delta AP and firing rate

A linear system would not have a latency asymmetry, Delta AP > 0, as observed in our data, but an otherwise linear system with a high threshold for response could. This is depicted in Figure 5, which shows an input (A) that is convolved with a rectangular pulse (data not shown) to yield an output (B) that is thresholded. The threshold (dashed line) is set high, so the onset latency, ton, is longer than the offset latency, toff, and Delta AP > 0. The spontaneous firing rate of a neuron is a potential indication of the height of the threshold of the "system" that drives the output of that neuron, with a low spontaneous rate indicating a high threshold. We therefore tested for a correlation between Delta AP and the spontaneous firing rate, which we computed during the 500 msec epoch that preceded stimulus onset.



View larger version (11K):
[in this window]
[in a new window]
 
Figure 5.   A system with a high threshold takes longer to turn on than to turn off. If the input in A, plotted as a function of time, is delayed and smoothed by convolution with a boxcar function, the trace in B results. The delay from the rise in the input to the rise in the output is equal to that from the fall in the input to the fall in the output. However, if the system responds only when the output is above a high threshold (dashed line), the latency to response onset (ton) is longer than the latency to offset (toff) by an amount approximately equal to the time to rise to threshold. Thus, Delta AP approximates the integration time of the system in this simple demonstration.

A plot of Delta AP against spontaneous rate for five classes of neurons (Fig. 6A) shows that cells with lower spontaneous rates tended to have larger Delta AP values. This was clearest in the LGN, where spontaneous rates were higher and more varied than in cortex. The histograms in Figure 6B show the distributions of spontaneous rates in LGN, in V1 for simple and complex DS cells, and in MT. It is well established that spontaneous rate decreases from retina to LGN to cortex (Herz et al., 1964). Because of the low spontaneous firing rates in V1, we were not surprised to find a lack of correlation between Delta AP and spontaneous rate. However, as a population, V1 simple cells were consistent with the trend for LGN cells, they typically had lower firing rates and higher Delta AP values than LGN cells (compare red circles with filled and open circles in Fig. 6A). V1 complex DS cells also alone showed no significant correlation between spontaneous rate and Delta AP, but in MT, where spontaneous rates were moderately higher (Fig. 6B, bottom), there was a significant correlation between Delta AP and spontaneous rate. Correlation coefficients are shown by black bars in the left column of Figure 6C (see legend for statistical significance). We also computed the correlation between Delta AP and stimulus evoked firing rate, which was measured in the 20 msec epoch after the onset of response to an AP transition. The correlation coefficients for the evoked rate, shown in the right-hand column of Figure 6C, were significantly negative for LGN P-cells, V1 simple cells, and MT cells, again showing that lower firing rates were associated with larger Delta AP values.

The association of low firing rate with large Delta AP is intuitively appealing because a mechanism that acts to pull a cell farther from threshold, e.g., by reducing its average resting potential, would not only decreases the firing rate but also could delay response onset and advance response offset, thereby increasing Delta AP. Spontaneous firing rate reflects a steady-state condition of a cell, but antipreferred stimuli can momentarily suppress the spontaneous discharge and could, in effect, increase the distance to threshold. Therefore, we will now test whether Delta AP depended on the antipreferred stimulus.

Assessing the influence of the antipreferred stimulus

To test the hypothesis that the antipreferred stimulus contributed to the delay of response onset, we compared responses to AP transitions with those to null-to-preferred (NP) transitions. The N was either a mean gray field or, for DS cells, a static grating, and N can be thought of as a candidate neutral stimulus for the cell being tested. Each cell was tested with a random, ternary sequence in which A, N, or P was presented with equal probability every 10 msec. Average responses and response difference traces were computed for the NP and PN transitions by the same methods used for AP and PA transitions, with N taking the place of A.

Figure 7A-F shows results for the cell types and stimuli that were examined previously in Figure 2. Each panel shows the familiar AP and PA response difference traces and the new NP and PN traces. In Figure 7C, where the traces are labeled, the firing rate of a V1 simple cell decreased for transitions from the preferred stimulus to the antipreferred (PA) (downward deflected black line) and to the null stimulus (PN) (downward deflected gray line). The response decrease began at about the same time regardless of whether A or N was the final stimulus. However, the timing of rate increases caused by transitions to the preferred stimulus depended on whether the initial stimulus was N or A: the responses to the NP transition (thick gray line) occurred sooner than the response to the AP transition (thin black line). All six examples in Figure 7 showed qualitatively similar behavior. The onset latency after antipreferred stimuli was longer than that after null stimuli, whereas the offset latency (after preferred) was approximately the same for PA and PN transitions. The latter observation is consistent with the idea that offset latency is determined by the loss of excitation (or activation of inhibition) caused by removal of the preferred stimulus. If A has a stronger influence than N, it does not act rapidly enough to advance the timing of the initial decline in firing rate when P is removed.



View larger version (35K):
[in this window]
[in a new window]
 
Figure 6.   Firing rate is negatively correlated with Delta AP. A, For five classes of neurons, Delta AP is plotted as a function of spontaneous rate. LGN p- and m-cells and V1 simple cells were tested with the phase stimulus. V1 complex DS and MT cells were tested with the direction stimulus. B, The distribution of spontaneous rate varies across cell class. LGN p- and m-cell data were combined into one histogram because their distributions were statistically indistinguishable (t test for mean, p = 0.34; F test for variance, p = 0.89). LGN cells had high and varied spontaneous rates compared with cortical cells. C, Correlation coefficients computed between Delta AP and spontaneous rate (left column of bars) and between Delta AP and evoked rate (right column) were always negative. Asterisks indicate statistical significance: *p < 0.05; **p < 0.01; ***p < 0.001. Evoked firing rate was computed in the 20 msec period after the onset of response to the AP transition.



View larger version (34K):
[in this window]
[in a new window]
 
Figure 7.   The response to a preferred stimulus is delayed more when it follows an antipreferred stimulus than when it follows a null stimulus. A-F, For six example cells, black lines show response difference traces for PA and AP transitions (medium and thin lines, respectively), and gray lines show response differences for PN and NP transitions (medium and thick lines, respectively). Cell types (top left corners) and stimulus categories match those in Figure 2. The phase stimulus applies to A-C, the orientation stimulus to D, and the direction stimulus to E and F. Bars in C indicate the latency of the NP (gray bar) and AP (open bar) responses relative to the PA latency. G, For all cells tested with ternary stimuli, Delta NP is plotted against Delta AP. For all cells, Delta NP < Delta AP. When Delta AP was low, Delta NP was on average near zero. m-Cells and p-cells were grouped together (filled black circles) because there was no significant difference between their measurements plotted here (5 p-cells, 2 m-cells).

We compared the onset timing for NP transitions to AP transitions quantitatively using Delta NP and Delta AP, which are schematized on the right of Figure 7G. Both measurements were made relative to the offset time (arrow) determined from the PA transition (see Materials and Methods). As defined earlier, Delta AP is the onset time for the AP response minus the offset time, whereas Delta NP is the onset time for the NP response minus the offset time. The markings in the schematic in G are similar to those used for the neuronal data in panel C (horizontal brackets show timing measurements). If N and A stimuli had equivalent effects on response timing, then Delta NP = Delta AP. If the null stimulus caused no delay in response onset relative to offset, then Delta NP = 0.

A cell-by-cell comparison of Delta NP and Delta AP is provided by the scatterplot in Figure 7G. For all cells, Delta NP < Delta AP, and Delta NP was near zero for many cells that had small Delta AP (approximately Delta AP < 10 msec). For each cell, we computed the ratio Delta NP:Delta AP to asses the fraction of the delay, Delta AP, that was present for the NP transition. We reasoned that a ratio measure would be justified if, as Figure 5B implies, Delta AP primarily represents a neuronal integration time (as defined in Nowak and Bullier, 1997) and cannot be negative. Thus, if Delta AP is small because a cell has an intrinsically short integration time, even a large effect of the null stimulus cannot cause a large absolute decrease in the already short integration time. The distributions of the ratio for all cell types fell mainly between 0 and 1, consistent with the scatter of points in G. Interestingly, for V1 simple cells, the ratio was significantly larger for the orientation stimulus (mean 0.71, SD 0.12, n = 5) than it was for the phase stimulus (mean 0.31, SD 0.14, n = 4; t test, p = 0.005). Taking the logarithm of the ratio, or using the absolute timing difference (because ratio measures are sensitive to noise in the denominator), did not destroy the significance of this result. This suggests that an orthogonal grating is more akin to mean gray than is a counterphase grating, and that the counterphase grating is the more appropriate antipreferred stimulus for V1 simple cells. It would be premature to draw any firm conclusion based on the low number of cells, but if this difference holds up, it provides quantitative evidence that afferent inhibition, associated with push-pull circuits (Ferster and Miller, 2000), is stronger than the recurrent inhibition associated with normalization and believed to underlie cross-orientation inhibition (Carandini et al., 1997).

We have just observed that responses to preferred stimuli were delayed more by A than by N stimuli, and we also observed that firing rate was related to response timing (Fig. 6). We will now examine the relationship between firing rate and delay for N and A stimuli. The mean firing rate in response to NP and AP transitions is plotted as a function of time in Figure 8A for a V1 complex DS cell. The firing rate just before the response to the AP transition (black lines below arrow) was lower than the rate just before the response to the NP transition (gray lines below arrow). This was typical across cell types, but a few counterexamples were present. Figure 8B shows a counterexample for which the firing rate just before the response to the AP transition was higher on average than the rate for the NP transition, yet the NP response still occurred sooner. For each cell, we computed the firing rates in the 10 msec period before the onset of response to the NP and AP transitions and subtracted the rate for the AP case from the rate for the NP case. Figure 8C shows the ratio of the timing measures, Delta NP:Delta AP, plotted against the firing rate difference (N-A) for cells marked by class and stimulus (for symbol legend, see Fig. 7G). There was a weak but significant correlation (r -0.43) between these measures across all cells and a significant correlation for MT cells alone (blue triangles, r = -0.66; see legend for statistics). Points for the LGN (black circles) and V1 simple cells (red circles) contributed to the overall trend, whereas V1 complex DS cells (green squares) did not appear to follow the trend.



View larger version (20K):
[in this window]
[in a new window]
 
Figure 8.   Comparing firing rates for antipreferred and null stimuli. A, Average responses for AP (black line) and NP (gray line) transitions and for reference stimuli (dashed lines of corresponding color; see Fig. 1A for stimulus timing). Before the response to the preferred stimulus, the rate associated with N (gray lines below arrow) is higher than that associated with A (black lines below arrow). This trend held for 31 of 34 cells tested with the ternary, direction stimulus. Responses include at least 225 occurrences of each pattern. B, Format like A, but for one of three DS cells that had a higher firing rate before the AP response transition (black lines) than before the NP transition (gray lines). Responses include at least 450 occurrences of each pattern. C, The ratio Delta NP:Delta AP is plotted against the difference between null and antipreferred firing rates (calculated in the 10 msec epoch before the response to the transition) for LGN (filled circles), V1 simple cells (red circles) tested with phase (filled) and orientation (open) stimuli, V1 complex DS cells (green squares), and MT cells (blue triangles). There was a significant correlation across the combined data sets (r = -0.49; p = 0.0003; n = 50) and for the MT cells alone (r = -66; p = 0.001; n = 20). None of the V1 data sets had significant correlations by themselves. For the LGN, r = -0.60 (p = 0.15; n = 7).

In summary, for the ternary sequences, antipreferred stimuli suppressed firing rate and delayed the onset of firing more than did null stimuli that were randomly interleaved with them. There was a mild tendency for cells with larger differences in firing rate to have larger timing differences in response to N and A stimuli by our ratio measure. It is unlikely that the change in firing rate caused the change in timing, because if this were so, the scatter of points in Figure 8C should go through unity for a rate difference of zero, but it did not. These results suggest that the antipreferred stimulus, and not just the lack of the preferred stimulus, contributed to the delay and that the magnitude of the delay may provide information beyond that carried by spike rate to distinguish the effects of various antipreferred and null stimuli.

How the AP delay depends on antipreferred duration

We have seen that the response to the onset of a preferred stimulus was delayed when that onset followed an antipreferred stimulus that was at least 30 msec in duration. If the response was delayed because of suppression caused by the antipreferred pulse, then changing the duration of the antipreferred pulse might reveal the time course of the suppressive signal. Two scenarios are schematized in Figure 9, where panel A shows four stimulus sequences with antipreferred pulses (plotted downward) of various lengths. Panel B shows the time course of a hypothetical suppressive signal that simply integrates over the epoch of the A pulse. Traces are aligned to the AP stimulus transition (open arrow), so the longest duration A pulse (thick line in A) has created the strongest suppression (thick line in B) when the stimulus is about to change back to preferred. Alternatively, if a transient suppressive signal was associated with the onset of the antipreferred stimulus, a longer antipreferred epoch would result in weaker suppression (Fig. 9C, thick line), and a shorter A epoch could cause stronger suppression (thin lines in A and C). We examined the time course of suppression by measuring responses that followed antipreferred epochs of various lengths. In our stimulus, the number of times that a sequence occurred declined exponentially with length; therefore, we limited our analysis to relatively short sequences for which timing could be measured accurately.



View larger version (28K):
[in this window]
[in a new window]
 
Figure 9.   Two conceptual models of suppression activated during an antipreferred pulse make opposite predictions. A, Four stimulus sequences with various duration A pulses are aligned to the AP transition (open arrow). Thicker traces show stimuli with longer A pulses. Stimulus traces are offset vertically for clarity here, but traces in B and C have no vertical offset. B, The time course of suppression that resulted from a sustained integration of the A pulses is plotted with lines of the same thickness used for the stimuli in A. The stimuli were convolved with the gray step function, which models suppression that accumulates over time. The suppression was larger for longer A pulses (downward indicates stronger suppression). C, When the stimuli were convolved with a fast, transient function (gray line), suppression for longer A pulses had decayed more than that for shorter pulses at the time of the AP transition (right ends of lines). This trend is opposite to that in B.

A series of 60 msec stimulus sequences is plotted in Figure 10 (B, gray inset). The first sequence (thickest line) consists of an antipreferred epoch of duration TA = 40 msec followed by a preferred epoch of 20 msec. Successively thinner lines indicate stimuli with successively shorter antipreferred epochs, i.e., smaller values of TA. The dashed line is the reference stimulus, which has no antipreferred pulse. For these sequences, responses of an LGN p-cell to the phase stimulus are plotted versus time, relative to the AP transition, in Figure 10A (time scale differs from stimulus inset). The response traces dropped from the reference response (dashed line) at times that reflected the onsets of the antipreferred pulses, but unlike the stimulus traces in the inset, the responses did not rise at the same time. As TA became shorter, the response to the AP transition occurred later, i.e., the thinner lines rise later, as indicated by the gray arrow. Thus, short, i.e., recently applied, A pulses were the most effective for delaying response onset in the LGN, consistent with transient suppression schematized in Figure 9C.



View larger version (43K):
[in this window]
[in a new window]
 
Figure 10.   Latency depends on the duration of the antipreferred stimulus, and the dependency shows several trends across cell classes. A, LGN p-cell average responses for five stimulus sequences (see stimulus inset below A) for counterphase stimulus. The response delay increased (gray arrow) as the antipreferred epoch was shortened. This trend occurred for all p- and m-cells. Responses include at least 250 occurrences for each pattern. B, Responses for this V1 simple cell to the counterphase stimulus showed a trend opposite to that for LGN: the response delay decreased (gray arrow) as the antipreferred epoch was shortened. This was representative of more than half of V1 cells, whereas others showed a trend similar to that in the LGN. Responses include at least 250 occurrences for each pattern. C, Responses for a V1 complex DS cell to the direction stimulus show a third trend: response latency first increased (shorter arrow) and then decreased as the antipreferred epoch was shortened (longer arrow). Responses include at least 188 occurrences for each pattern. This was typical of V1 complex DS cells and MT cells. D, Responses are shown for six stimuli (gray inset), which each have 30 msec of P before the A epoch. The fifth stimulus (thinnest line) is unchanged from the top panels. As TA shortens, the response to the AP transition first shifts rightward and then leftward, consistent with the behavior in C. Responses include 12, 24, 48, 94, and 192 occurrences for the thickest to thinnest lines, respectively.

Some V1 simple cells tested with the phase stimulus behaved like the example LGN cell just described, whereas others showed a second trend that is depicted in Figure 10B. For this cell, responses to the AP transition occurred earlier for sequences with shorter epochs of antipreferred stimuli (Fig. 10B, gray arrow). This trend is consistent with suppression that accumulates over the time scale tested, as depicted in Figure 9B. We observed similar results for 20 V1 cells tested with the orientation stimulus. Cells tested with both stimuli behaved similarly, i.e., onset time either increased as in A or decreased as in B for both.

We observed a third trend for DS cells tested with the direction stimulus in V1 and MT. This is exemplified by data from a V1 complex DS cell in Figure 10C. As TA decreased from 40 to 30 msec, the response latency increased (short gray arrow), but further reduction of TA caused a decrease in latency (long gray arrow). The response for TA = 10 msec was shifted abruptly to the left (thin line).

For each cell, we computed the onset latency of the average response for TA = 40 msec and used this as a reference time. Onset latencies for TA = 30, 20, and 10 msec were expressed relative to the reference time. These data are plotted in Figure 11 for each cell that provided clear response onsets for all four TA values. For the LGN, response latency increased at shorter TA for almost every cell that we studied, and the increase was greater on average for m-cells (black lines) than for p-cells (Fig. 11A, gray lines) (see legend for statistics). The average change in onset latency for m- and p-cells is plotted in the inset at the bottom of A. For V1 simple cells tested with the phase stimulus, the results varied widely across cells (Fig. 11B). For shorter A pulses, onset latency increased for some cells but decreased for others. The thick line shows data for the example in Figure 10B. No average across cells is shown because it would not reflect the diversity found in V1 simple cells. Results for simple cells tested with the orientation stimulus showed a similar diversity (C). Complex DS cells in V1 and cells in MT that were tested with the direction stimulus showed behavior similar to each other (Fig. 11D) and were less diverse than V1 simple cells. There was an initial increase followed by a larger decrease in latency as TA decreased from 40 to 10 msec. Average changes in latency are plotted in the inset in D. The data in Figure 11 show that the signals from preferred and antipreferred stimuli do not interact in the same way across cortical areas and cell types, at least at the time scale of tens of milliseconds.



View larger version (35K):
[in this window]
[in a new window]
 
Figure 11.   Summary of duration dependence of latency across cell classes. Relative latencies for 5% rise-to-peak are plotted against the duration of the antipreferred stimulus, TA, that preceded the AP transition. All values are given relative to the value for TA = 40 msec. A, Each line emanating from the point (40,0) shows data for a p-cell (gray lines) or an m-cell (black lines). For nearly all LGN cells, latency increased as TA decreased. The average relative latencies are plotted for p-cells (gray line) and m-cells (black line) in the inset (error bars show ±1 SEM). m-cells on average had significantly longer relative latencies (t test; p < 0.00001; 7.0 > 3.7 msec; TA = 10 msec). Results shown here are for counterphase stimuli. Cell counts appear in parentheses. B, Similar data are plotted for V1 simple cells tested with counterphase stimuli. These plots show that V1 had more diverse behavior than the LGN. Latency could increase or decrease as TA decreased. The thick line corresponds to the example data in Figure 10B for which the latency decreased with TA. C, Similar to B, but for V1 simple cells tested with the orthogonal orientation stimulus. The thick line here and in B show data collected from the same cell. D, For V1 complex cells (black lines) and MT cells (gray lines) tested with the direction stimulus, response latency first increased and then decreased as TA was reduced from 40 to 10 msec. The inset shows the average relative latency for MT cells and V1 complex DS cells.

The changes in onset latency with antipreferred pulse duration can be related back to our estimates of Delta AP reported in Figure 4. Those results were derived from responses to 50 msec sequences beginning with 30 msec of A; therefore, they are most comparable with the results for TA = 30 or 40 msec here. For LGN cells, this implies that Delta AP for shorter (10-20 msec) antipreferred pulses would be on average larger than reported in Figure 4C. Interestingly, the larger increase in onset latency for m-cells compared with p-cells for short A pulses (Fig. 11A) appears to compensate for the shorter Delta AP value for m-cells shown in Figure 4C. In fact, the average values of Delta AP computed using the onset time for the TA = 10 msec responses were 12.5 and 12.3 msec for p- and m-cells, respectively (SD 3.1, n = 11 for p-cells, SD 3.0, n = 17 for m-cells). For V1 complex DS cells and MT cells, the change in onset latency from TA = 30-40 msec to TA = 10 msec was approximately -10 msec (Fig. 11D), which is equal but opposite to the mean Delta AP reported in Figure 4F and G. Thus, the early response onset after a 10 msec pulse of antipreferred motion (Fig. 10C, thin solid line) occurs very close to the response offset time computed previously from the PA transition. On average Delta AP is only ~3 msec for V1 and MT when computed using the onset time for TA = 10 msec. This suggests that 10 msec of motion reversal is too brief to activate the mechanism by which the antipreferred stimulus delays response onset.

Finally, for the stimulus sequences just examined, two factors were changing at once: the duration, TA, of A and the duration, TP, of P that preceded A. A set of control sequences in which TP was held constant at 30 msec are shown in the gray inset in Figure 10D. The rightward and then leftward shift in the latency of the AP responses to these stimuli (Fig. 10D, gray arrows) was similar to that observed in C. The longest stimulus sequence (100 msec) occurred only 12 times. The infrequent occurrence of the longer sequences made them less useful than the constant length sequences (Fig. 10, top inset) for accurate estimation of response trends. Nevertheless, the shifts in latency for the longer sequences were qualitatively similar to those for the shorter sequences for the cell types described here. This is not to say that the sequence before the antipreferred pulses had no significant effect on responses. The sequence history affected the responses, but a full account of this is not attempted here. We simply demonstrate that onset latency showed history dependence that characterized and differentiated between cell classes.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

We found that nearly all neurons in the LGN, V1, and MT responded more rapidly to a stimulus transition from preferred to antipreferred than to the opposite transition. The generality of this observation might be questioned because our rapidly changing stimulus differs from commonly used stimuli that flash on for ~1 sec after several seconds of homogeneous background. Published data for standard stimuli, however, suggest that Delta AP is positive for cat cortical neurons (von Baumgarten and Jung, 1952, their Fig. 3) and ranges from 3-9 msec for cat LGN cells (Coenen et al., 1972, their Table 1; Mastronarde, 1987a, his Table 1; Humphrey and Weller, 1988; Saul and Humphrey, 1990). Data of Adrian and Matthews (1927, their Fig. 5) and of Hartline (1938) suggest that Delta AP is positive in the early visual systems of other vertebrates as well.

The Delta AP delay is derived from latency measurements and is therefore expected to be stimulus dependent. Response latency varies with stimulus parameters in the retina (Kuffler, 1953; Levick, 1973), in the visual areas studied here in