Intrinsic signal imaging of cone-isolating V1 population responses
As a first step in assessing S-cone contributions to layer 2/3 of V1, we used intrinsic signal imaging to characterize the population response to S-cone-isolating stimuli, which selectively modulate S-cone responses while keeping the responses of ML-cones constant (unmodulated). The method of color-exchange was used to produce a chromatic sine wave grating stimulus that silences the responses of ML-cones but drives the S-cones, the definition of an S-cone-isolating stimulus (see Experimental Procedures).
Single condition images of responses to single S- and ML-cone-isolating stimuli revealed similar activation patterns for both cone-isolating stimuli (). This pattern was distinctly modular, with a size and spacing consistent with previous descriptions of the columnar mapping of orientation preference in the tree shrew (Humphrey et al., 1980
; Bosking et al., 1997
). Presentation of vertical and horizontal S-cone-isolating gratings resulted in complementary modular patterns, and subtraction of the two activation patterns resulted in a strong orientation difference signal (). Moreover the cortical activation pattern for S-cone-isolating gratings appeared quite similar to that produced by ML-cone-isolating gratings of the same orientation (), suggesting that both sets of cone inputs are represented within a single map of orientation preference.
Figure 1 Intrinsic signal images of superficial V1 responses to cone-isolating stimuli. Single-condition images of superficial V1 of the same tree shrew (left) showing responses to S- (A) and ML- (B) cone-isolating, sinusoidal grating stimuli of two different (more ...)
To compare these patterns more comprehensively, we obtained intrinsic signal images with S- and ML-cone stimulation to 8 stimulus orientations (0° – 157.5° in 22.5° steps, with sinusoidal gratings of 0.2 cycles per degree (cpd), drifting at 3Hz). To extract orientation preference at each pixel, we fit responses using a circular Gaussian (). Most pixels were well fit by this function (mean ML-cone R2 = 0.68 ± 0.21 (s.d.), mean S-cone R2 = 0.61 ± 0.23 (s.d.)). Low R2 values (poor fits), were found to correspond to locations of blood vessels, as well as fractures and singularities, regions where there was a high rate of change in orientation preference. Comparing orientation preference values obtained from S- and ML-cone stimulation revealed a strong correspondence (). Taken together, these results indicate that both S- and ML-cone inputs contribute to the orientation-tuned responses of layer 2/3 neurons and do so via circuits that construct a single uniform representation of stimulus orientation.
Figure 2 The functional mapping of orientation preference is similar for ML- and S-cone signals. A, B) False color orientation maps for one animal constructed from pixel-by-pixel Gaussian fits to intrinsic signal responses. Color indicates orientation preference. (more ...)
S- and ML-cone cortical responses exhibit distinct spatial and temporal tuning properties
While the difference in retinal sampling was not evident in the orientation-selective responses to S- and ML-cone stimulation, it was evident in the magnitude of responses at different spatial frequencies (). Spatial frequency tuning curves for S- and ML-cone-isolating stimuli derived from the population responses were strikingly different, with S-cone responses peaking at much lower spatial frequencies than ML-cone responses (2-factor ANOVA interaction, p<0.001). Although there were animal-to-animal differences in signal magnitude and optimal spatial frequency, the average activation from S-cone stimuli (N=4 animals) is restricted to low spatial frequencies (0.025–0.55 cpd) (). In contrast, activation with ML-cone-isolating stimuli varying in spatial frequency is band-pass, with peak activation near the S-cone high-frequency cutoff (0.4 cpd peak, with a response range of 0.025–1.6 cpd) (). These spatial frequency tuning curves also emphasize the large difference in the magnitude of intrinsic signals derived from ML- and S-cone stimulation. At their optimal spatial frequencies, the average strength of ML-cone responses was roughly three times that of S-cone responses.
The population responses to S- and ML-cone-isolating stimuli also exhibited clear differences in temporal properties (). Responsiveness to S-cone stimulation was relatively constant at low temporal frequencies, decreased significantly at drift rates greater than ~5 Hz, and was undetectable at 10 Hz and higher. On the other hand, ML-cone-isolating stimuli were much more effective at eliciting responses at high temporal modulations. At low temporal frequencies, the relative S:ML intrinsic signal modulation magnitude varied across different individuals (N = 3); but the high-frequency S-cone fall-off is consistent across individual animals. Tuning curves shapes for the S- and ML-isolating stimuli were significantly different (2-factor ANOVA interaction, p<0.05). These data indicate that at high temporal frequencies, S-cone stimuli are poorly represented in the visual cortex. The difference between S- and ML-cone activation at high temporal frequencies could be due to differences in the temporal properties of the S- and ML- pathways that supply cortex, or because the overall level of activity in the S-cone pathway is insufficient to drive cortical responses at high temporal frequencies.
In vivo 2-photon imaging of cone-isolating responses
Although intrinsic imaging is a powerful tool for providing a clear picture of the average responses of neurons, it is unable to reveal responses of the individual neurons that contribute to this average. For example, the correspondence in the orientation maps driven by S- and ML-cones might arise if all neurons in layer 2/3 received input from S- and ML-cones; alternatively, this correspondence could result from a mixture of neurons, only some of which respond to S-cone stimulation.
To address the distribution of S-cone responses at single-cell resolution, we used 2-photon imaging of calcium signals (). This technique offers several advantages for the characterization of S-cone inputs. First, previous studies suggest S-cone inputs to V1 cells might be weak or sparse (Lennie et al., 1990
; Johnson et al., 2004
; Solomon and Lennie, 2005
; Hashemi-Nezhad et al., 2008
), and single cell imaging allows one to characterize responses of dozens of neighboring cells to reveal both abundant and sparse response types. Second, 2-photon imaging allows one to record cells located within 300μm of the cortical surface. These superficial cells are difficult to record with electrodes due to dimpling and other factors, but are likely to be important for S-cone processing because the K inputs from the LGN specifically target layer 2/3 (Fitzpatrick et al., 1983
; Hendry and Carder, 1993
; Martin et al., 1997
; Chatterjee and Callaway, 2002
Only a subset of visually responsive layer 2/3 neurons, roughly 43% (164/383, N=15 tree shrews), was found to give significant responses to S-cone-isolating gratings (0.2–0.4 cpd, drifting at 2–4 Hz). We were unable to discern any spatial pattern to the distribution of S-cone responsive neurons; they appeared to be intermingled with neurons that were not responsive to S-cone stimulation. Consistent with results from intrinsic imaging, the vast majority of S-cone responsive neurons were orientation-tuned, whether measured with S-cone-isolating or achromatic stimuli. The orientation preference of S-cone-responsive neurons was similar to that of neighboring neurons that were unresponsive to S-cone stimulation (), and orientation preference values for cells that responded to both achromatic and S-cone-isolating stimulation were not statistically different (t-test, p = 0.1).
Quantitative features of orientation tuning curves that were measured in S-cone-responsive and S-cone non-responsive cells were only modestly different, and tuning curve shapes for S-cone responsive cells that were measured using achromatic or S-cone-isolating gratings were very similar (). We examined three indices of orientation selectivity: circular variance (the fraction of the total response that occurs at angles other than the preferred), tuning width (the range of orientations to which the cell responds strongly), and orthogonal to preferred ratio (how the tuning curve flanks compare to the peak response) (). No differences were found in achromatic tuning width or achromatic orthogonal to preferred ratio between S-cone-responsive cells and S-cone-non-responsive cells (Mann-Whitney, p = 0.05, 0.9, respectively), but achromatic circular variance values were slightly higher for S-cone-responsive cells (means: 0.37 vs. 0.41; Mann-Whitney, p <0.004). Circular variance values measured in S-cone-responsive cells were also modestly larger when measurements were conducted with S-cone-isolating gratings rather than achromatic gratings (means 0.43 vs. 0.37; Wilcoxon signed-rank test, p <0.001), as were orthogonal to preferred ratios (means 0.04 vs. 0.06; p<0.02) but differences in tuning width were not significant (p = 0.9).
The diverse chromatic response properties of layer 2/3 neurons
The results presented so far indicate that many neurons in V1 are driven by S-cones signals, but they do not reveal the chromatic response properties of V1 neurons or the nature of interactions between S- and ML-cone signals. Given the color vision capabilities of this species, one would expect that at least some of these S-cone-responsive neurons would exhibit cone-opponent responses, but, in principle, cone-opponent, cone-summing, or single cone configurations could underlie the responses observed with S-cone-isolating stimulation. To establish the chromatic properties of individual layer 2/3 neurons, 2-photon calcium imaging was used to determine the responses of V1 neurons to a family of 12 sinusoidal gratings where S- and ML-cone contrasts and their relative chromatic phases were systematically varied (, see (Diller et al., 2004
)). S- and ML-cone contrasts were presented in-phase at different relative contrasts for stimuli 1–3 and 11–12, while S- and ML-cone contrasts were presented out-of-phase for stimuli 5–9. Stimulus 4 provided drive only to S-cones, that is, it was an S-cone-isolating stimulus, while stimulus 10 provided drive only to the ML-cones (ML-cone-isolating stimulus). This stimulus suite allowed us to explore a wide extent of possible cone interaction space, albeit at a single orientation, spatial and temporal frequency, and stimulus size (the full screen).
Responses from 4 model neurons are illustrated in . A neuron that receives equal drive from S- and ML-cones in an opponent configuration would be expected to exhibit a response peak near stimulus 7 (top left), while a neuron that receives similar cone drive in a summing configuration should exhibit peaks at the edges (near Stimuli 1 and 11). A neuron that receives excitatory input from S-cones and no input from ML-cones should exhibit a peak at Stimulus 4 and no response at Stimulus 10, and, conversely, a neuron that receives excitatory input from ML-cones but no input from S-cones should exhibit a peak at Stimulus 10 and no response to Stimulus 4.
We observed a wide range of responses to this family of gratings. In the imaging field in , neuron `a' responded strongly to stimuli 7 and 10, while neuron `c' was maximally responsive to stimuli 2 and 12. Other cells such as `i', `j', `k', and `l' were active for all of these stimuli.
Tuning curves for several cells are shown in . Some neurons (`a', `b') exhibited peak responses for stimuli 5–9, consistent with opponent interactions, and would be most strongly driven by chromatic boundaries. Others (`c', `d') exhibited peak responses for stimuli 1–3 or 1–12, consistent with summation of S- and ML-cones, and would be selective for achromatic boundaries. A few neurons, such as `e' and `f', responded to the S-cone-isolating stimulus, but exhibited weaker responses when ML-cone signals were included, and were particularly suppressed when ML-cone signals were presented out-of-phase from S-cone signals. Several neurons (`g', `h') responded to stimuli with ML-cone drive but did not respond to the S-cone-isolating stimulus, suggesting that these cells receive input exclusively from ML-cones. Finally, many neurons exhibited broad tuning and responded strongly to all stimuli (`i' – `l'). Neurons with these broad tuning curves would be expected to respond to both color and achromatic boundaries.
We next determined whether these response profiles could be divided neatly into discrete classes or spanned a continuum. We developed a “cone summation index” that describes how similar a cell's tuning curve is to the model neuron that has equal cone weights that sum (value 1) or has equal cone weights that are opponent (value −1) (see Materials and Methods, ). We created a similar “S/ML response index” that describes the similarity of a cell's tuning curve to the model neuron that has only S-cone drive (1) or only ML-cone drive (−1), as in . A scatter plot () indicates a continuum of response profiles.
The scatter plot in also indicates the large variety of these different tuning curve shapes. Cells along the bottom of the graph exhibited strongly opponent responses, while cells at the top exhibit summing responses; however, these “purely” opponent or “purely” summing cells comprise a relatively small percentage of the total population. Cells in the middle of the cone summation axis are neither purely opponent nor purely summing. Cells on the left of this group exhibit strong responses when presented with strong ML-cone drive, while cells on the right exhibit strong responses when presented with strong S-cone drive. Cells with broad tuning, located near the center, were most common and make up the densest portion of the plot. There is a continuous distribution of index values, and the median cell (50%) exhibits some opponency ().
Modeling the interactions between S- and ML cone inputs responsible for chromatic properties of layer 2/3 neurons
While the tuning curves emphasize the diverse chromatic response properties of V1 neurons, they do not directly address the interactions between S- and ML-cone inputs that are responsible for a given chromatic response. For example, if a cell receives suppressive input from one cone class, the cell would not respond to that cone's isolating stimulus, yet the cone's influence might be clear if its impact were considered across all stimuli. To probe possible underlying cone interactions, we created three simple models of cone inputs (). For all models, we supposed that each cell received linear input from the two cone types. Although responses from our grating stimuli do not allow us to infer the exact spatial configuration of cone inputs in the cells, for ease of presentation we assumed that the spatial extent of the receptive field matched the spatial period of our gratings, and then divided the receptive field profile for each cone type into two halves; at any particular time, one half of the receptive field was stimulated by one half phase of the sinusoidal grating, and the other half of the receptive field was stimulated by the opposite phase. To estimate a cell's response to the drifting gratings we used in our experiments, we added the responses of the simulated cell to two temporal phases of the grating stimulus, 0° and 180°, corresponding to the times when the grating was aligned with the receptive field profile. To compute the response of the cell to a grating at each of these temporal phases, we multiplied the signed grating cone contrast for each cone type by its cone weight, added these values, and rectified above 0 to account for the spike threshold.
Figure 6 Models of cone interactions. A) Simple cone-contrast (CC) model; receptive fields are divided in half, but contributions from a given cone must be equal and opposite across halves. Responses depend only on relative S- and ML-cone contrast. B) The 2-phase (more ...)
In the simplest model, called the cone contrast (CC) model, responses depended only on the relative phase and contrasts of S- and ML-cone input, which could have different weights. In equation form, Rcc(Sc, MLc) =|MLw MLc + SwSc|, where Sc and MLc are the S- and ML-cone contrasts of the stimulus, and Sw and MLw are the free cone weights (two free parameters total). In the spatial configuration presented in , this meant the two halves of the receptive field for each cone were required to be equal and opposite, but one could also imagine other configurations, such as cone projections to a single linear subunit. If the S- and ML-cone inputs summed, then the weights Sw and MLw shared the same sign; if the cones were opponent, then Sw and MLw had opposite signs.
The CC model could describe the tuning curves of many cells that might have been expected from previous studies, such as single-opponent (opponency in cone type or space but not both), double-opponent (opponency in cone type and space), and non-opponent (summing) cells (). However, responses of a number of cells were not described adequately by the simple CC model. Some cells, such as the cell in the third row of , could be well fit by the CC model only if the model were modified to include non-linear cone contrast gain using the Hill function H(c,n,c50) = cn / (cn + c50n). The modified cone contrast model had 6 free parameters: two cone weights, n and c50 for the S-cone contrast, and n and c50 for the ML-cone contrast.
While the response profiles of approximately half of all neurons (51.4%) were best described by the CC and modified CC model, the responses of many neurons were fundamentally incompatible with these models. To fit the tuning curves of these cells, we created an extended model, called the 2-phase cone contrast model (2PCC), which posited the existence of 2 linear subunits that are not equal and opposite. In the 2-phase model, R2p (Sc, MLc) =[S+[Sc]+ + S−[−Sc] + ML+[MLc]+ + ML−[−MLc]+]+ + [S+[−Sc]+ + S−[Sc]+ ML+[−MLc]+ + ML−[MLc]+]+, where S+, S−, ML+ and ML− are the free cone weights and [x]+ denotes rectification above 0 (4 free parameters total). In the example spatial configuration used in the figures, this means the two halves of the receptive field profile were allowed to take different values and either sign ().
The 2PCC model could explain the broadly-tuned response profiles (, top two rows), which were the most common in our study. These cells were best fit when one subunit was cone-opponent, while the other summed. Furthermore, the 2PCC model could describe phase-dependent tuning properties that were observed in a minority of cells, such as cell `e' (, last row). This cell responded strongly to the S-cone-isolating stimulus, but exhibited reduced firing when in-phase ML-cone contrast was present, and was greatly suppressed if ML-cone contrast was presented out-of-phase from S-cone input. Cell `e' can be well fit by the 2PCC model when one half of the receptive field is driven by opponent S-cone and ML-cone input, and the other half of the receptive field is strongly inhibited by ML-cone input. The simpler CC model cannot capture the differential impact of ML-cone phase for cell `e', and explains only 57% of the cell's response.
To estimate the contributions of S- and ML-cones to cortical responses, we analyzed the parameters of the model fits. The models accounted for a considerable portion of observed responses; the median goodness-of-fit ranged from 84% for the CC model to 91% for the 2PCC model (). We estimate that the median cell received somewhere between 34–46% of its total cone input from S-cones (). This estimate did not change substantially if we expanded the 2PPC model to include contributions from the rods, suggesting that our decision to ignore rod contributions in the stimulus design was reasonable.
Figure 7 V1 cells exhibit a variety of S-/ML-cone configurations. A) Cumulative histogram of goodness of fit for models. All models do well, but 2PCC model explains > 90% of responses for median cell. B) Cumulative histogram of fraction of total cone input (more ...)
We next determined the best model description for each cell using a modified nested F test (). The most common fit type was the “Mixed-Opponent/Summing” configuration that described the broad tuning curves, followed by “Opponent” and “ML-only.” To ensure that the best model description for the cell's measured responses was not influenced unduly by trial-to-trial variation, we also computed the best model for 100 bootstrap simulations for all cells (Press, 1992
), and observed a similar distribution of best model fits. These results indicate that the most common cell type observed in our study is not a member of the “traditional” classes of purely color-selective or achromatic-selective neurons.
Finally, we examined whether there was a relationship between cone interaction type and orientation selectivity. All of the cone interaction types exhibited orientation selectivity (), and the distribution of observed tuning parameters such as circular variance, tuning width, and orthogonal/preferred index did not vary significantly across the cone interaction types (). This result suggests that one would expect all of these cell types to exhibit elongated receptive fields.