population receptive fields of on and off thalamic inputs to ......ple neurons with overlapping...
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Stimulus orientation is a feature of visual scenes that is well repre-sented in primary visual cortex. Neurons that respond to the same stimulus orientation are organized in columns, which are thought to emerge from the convergent inputs of neurons with circular receptive fields that are aligned in visual space1. Such a convergence pattern has been demonstrated in ferrets at the level of thalamic inputs2 and tree shrews at the level of layer 4 inputs to the superficial layers of the cortex3. However, such neatly organized convergent input might not be sufficient to cause orientation columns to develop if ON and OFF visual channels do not segregate in visual cortex. First, pharmacologi-cal studies have shown that orientation columns do not develop when the ON visual channel is blocked early after birth4. Second, modeling studies strongly suggest that the receptive field arrangements of ON and OFF neuronal inputs are important for the development of cortical orientation maps5–9. Third, intracellular studies have shown that the spatial ON-OFF organization of cortical receptive fields can be modi-fied by Hebbian plasticity10. And fourth, cells at the input layers of an orientation column have receptive fields with separate ON and OFF subregions that match the cortical orientation preference in ferrets, cats and primates11–17. Although these arguments support the idea that ON and OFF channels are involved in the development of cortical orientation columns, a crucial piece of experimental evidence is still missing. There is no proof that the orientation preference of a cortical column is related to the receptive field arrangement of ON and OFF visual inputs in the column. We have recently shown that ON and OFF thalamic afferents are segregated in cat visual cortex18, as predicted by several computational models5–9. Here, we show that, in addition to this segregation in cortical space, the receptive fields from ON and OFF afferents are partially segregated in visual space within each orientation column. Importantly, this partial segregation in visual space does not compromise the accuracy of the retinotopic map and can coexist with a remarkably limited receptive field scatter of the afferents.
RESULTSWe used a multielectrode array to record simultaneously from multi-ple neurons with overlapping receptive fields in the lateral geniculate nucleus (LGN) of the thalamus. In addition, we used a multichannel silicone probe with 16 vertically aligned channels to record simulta-neously from multiple cortical layers in an orientation column that was retinotopically aligned with the geniculate recordings (Fig. 1a). Geniculate cells that made monosynaptic connections at the same cortical orientation column were identified by spike-triggered current- source-density analysis (STCSD)18,19.
Identification of thalamic inputs with STCSDWe used the spikes from a single geniculate neuron to generate spike-triggered averages of the local field potentials (LFPs) recorded at each cortical channel. Then, we processed the LFPs with methods of current source density analysis (CSD)20,21 to measure the current sinks generated by each geniculate afferent through the depth of the cortex19. Geniculate cells that made monosynaptic connections with the recorded cortical column generated a characteristic current sink with three temporal components (Fig. 1b) that correspond to the axonal terminal response (on average, 1.3 ms after a spike is generated in LGN), the synaptic delay (0.5 ms duration) and the postsynaptic response (>1 ms duration)18,19. Single-axon STCSD provided several advantages over previous methods to identify geniculate afferents that made synapses in a small region of visual cortex2,22. First, it made it possible to distinguish geniculate afferents that made monosynaptic connections within an orientation column (Fig. 1c) from others that did not, even if they had axons that passed through the column (Fig. 1c). Second, the cortex was active during the entire experiment, so we could verify the orientation preference through the depth of the cortex and the location of layer 4 at any point in time (Fig. 1d). Third, geniculate afferents were recorded simultaneously in LGN and cortex, so we could
1Department of Biological Sciences, State University of New York, College of Optometry, New York, New York, USA. 2Department of Psychology, University of Connecticut, Storrs, Connecticut, USA. Correspondence should be addressed to J.M.A. ([email protected]).
Received 20 July 2010; accepted 30 November 2010; published online 9 January 2011; doi:10.1038/nn.2729
Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortexJianzhong Jin1, Yushi Wang1, Harvey A Swadlow1,2 & Jose M Alonso1,2
The primary visual cortex of primates and carnivores is organized into columns of neurons with similar preferences for stimulus orientation, but the developmental origin and function of this organization are still matters of debate. We found that the orientation preference of a cortical column is closely related to the population receptive field of its ON and OFF thalamic inputs. The receptive field scatter from the thalamic inputs was more limited than previously thought and matched the average receptive field size of neurons at the input layers of cortex. Moreover, the thalamic population receptive field (calculated as ON – OFF average) had separate ON and OFF subregions, similar to cortical neurons in layer 4, and provided an accurate prediction of the preferred orientation of the column. These results support developmental models of orientation maps that are based on the receptive field arrangement of ON and OFF visual inputs to cortex.
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measure their axonal conduction time in addition to their response prop-erties (Fig. 1e). Finally, by making multiple thalamic penetrations with a multielectrode array, we could densely sample a cylinder of LGN that projected to the same orientation column in visual cortex (Fig. 1f).
We performed intensive searches of well-isolated cells in LGN and selected those that generated notable current sinks in the recorded corti-cal orientation column (average of 22 ± 11 afferents per column; range, 12–47 afferents). All geniculate neurons that projected to the same ori-entation column were confined within an LGN cylinder of ~200 µm diameter, whose retinotopy was precisely matched to the retinotopy of the cortical recordings. In two examples from groups of geniculate afferents, each of which made connections with a different column, OFF afferents outnumbered ON afferents and also generated stronger current sinks (Fig. 2a), consistent with the finding that the cortical representa-tion of central vision is dominated by OFF geniculate afferents18. In both groups, the geniculate receptive fields were highly overlapped and aggregated around a point in visual space (Fig. 2b), which was covered by 22 geniculate cells in one example and 18 in the other. These values of receptive field coverage are consistent with those reported in the cat retina23 and are probably amplified by retinogeniculate divergence24.
Population receptive fields of thalamic inputsAlthough the geniculate inputs to an orientation column had highly overlapping receptive fields, the ON and OFF types did not cover visual space homogeneously. In the two examples illustrated, ON receptive fields covered more restricted regions of visual space than OFF receptive fields and also aggregated in slightly different spatial
positions (Fig. 2c). The average ON – OFF receptive-field distance from pairs of thalamic inputs within an orientation column was 0.7 geniculate receptive field centers (Table 1), a value that is not far from 1, the expected ON – OFF distance from geniculate inputs connecting to a layer 4 neuron with a two-subregion simple receptive field13,25.
To measure the population receptive field of the geniculate inputs, we normalized each receptive field by its maximum value and per-formed an ON – OFF averaged subtraction (summed all receptive fields after multiplying OFF by –1). Such subtraction is commonly used to measure receptive fields in LGN and visual cortex14,25,26 and to proc-ess images in computer vision. The population receptive field of the geniculate afferents obtained by ON – OFF subtraction had separate ON and OFF subregions, like cortical receptive fields in layer 4 (Fig. 2d and see Supplementary Fig. 1 for additional examples). Moreover, the arrangement of the subregions from the population receptive field matched the preferred orientation of the column (Fig. 2e). To estimate the strength of the imbalance between ON and OFF receptive field coverage within each orientation column, we calculated the signal- to-noise ratio (SNR) of the ON – OFF population receptive field. The signal-to-noise ratio was measured as the value of the receptive field at its central pixel divided by the value of noise (measured at a pixel well outside the receptive field). If the ON and OFF receptive fields were completely superimposed and balanced in number, the ON – OFF difference would approach the noise measurement and the SNR would be 1. Instead, the ON – OFF population receptive fields had large SNRs that were compara-ble to those obtained with an ON + OFF sum (average SNR for ON – OFF: 8.85; average SNR for ON + OFF: 11.31; P = 0.2413, Wilcoxon rank sum
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Figure 1 Geniculate afferents making monosynaptic connections with a cortical orientation column were identified by STCSD. (a) Recording method. (b) Spike-triggered local field potentials (STLFPs) and spike-triggered current source densities (STCSDs) generated by a single geniculate axon through the depth of the cortex. (c) Two afferents passing through an orientation column but only one (left) making a monosynaptic connection. (d) Cortical layers were identified with CSD analysis by stimulating the cortex with a full-field flash (see Online Methods). The orientation tuning was measured through the depth of the cortical column from multiunit activity responses to sweeping bars. (e) Example of simultaneously recorded geniculate afferents making monosynaptic connections with the same orientation column at different depths of layer 4. The afferents had different axonal conduction times, receptive field sizes and positions and receptive field response latencies. (f) Multiple recordings within a 200-µm cylinder of LGN allowed us to sample populations of geniculate afferents that made monosynaptic connections with the same orientation column.
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test, Table 1). These results show that the ON and OFF receptive fields from the geniculate inputs to a cortical orientation column are partially segregated in visual space. In addition, as a consequence of the partial seg-regation, the ON – OFF population receptive field has separate subregions that match the preferred orientation of the cortical column.
Thalamic population predicts orientation preferenceOn average, the preferred orientation estimated from the ON – OFF population receptive field of the thalamic afferents was just 17.4° different from the orientation preference measured with moving bars in cortical layer 4 (R2 = 0.92, slope: 1; Fig. 3a). The orientation preference could also be estimated after disregarding the contrast polarity of the geniculate inputs (ON + OFF average), consistent with previous measurements in ferret2. However, the ON + OFF estimate was sometimes ~90° apart from the measured cortical orientation and the average estimation error was 2.6 times larger than for the
ON – OFF prediction (orientation difference = 45.6° versus 17.4°, P = 0.0004, χ2 test; Fig. 3b). We could also predict the cortical orien-tation preference from the ON – OFF population receptive field of non-connected thalamic afferents with cell bodies neighboring the connected ones (recorded in the same thalamic region), as predicted by a recent computational model8. However, in this case the predicted orientation preference also had an error 2.3 times larger than the prediction based on the ON – OFF receptive field from connected inputs (40.6° versus 17.4°, P = 0.0023, χ2 test; Fig. 3c). Moreover, calculating the ON – OFF population receptive field with both con-nected and unconnected afferents did not improve the prediction of orientation preference (39.8° versus 40.6°, P = 0.9289, χ2 test; Fig. 3d). A simple visual inspection of examples from thalamic receptive field populations makes it clear that the orientation preference can be better predicted from the ON – OFF than the ON + OFF population receptive field (Fig. 3e and Supplementary Fig. 1).
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Figure 2 The ON – OFF population receptive field of the geniculate inputs predicts the preferred orientation of the cortical column. (a) Strength of the current sinks generated by ON (red) and OFF (blue) geniculate afferents in two cortical orientation columns (top and bottom). (b) Receptive field coverage of the geniculate afferents that make monosynaptic connections with each column. (c) Superimposed ON and OFF receptive fields from the geniculate afferents. (d) Population receptive field calculated as ON – OFF average. (e) Orientation preference from multiunit activity recorded in cortical layer 4. The inset (top left) illustrates the difference between the orientation preference measured in cortex (continuous line) and predicted from the LGN population (LGNp, discontinuous line). AC, area centralis.
Table 1 Comparative measurements of ON and OFF geniculate inputs in each cortical orientation column studiedOrientation column
ON – OFF distance
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ON – OFF versus ON + OFF SNR Number of LGN inputs Ten strongest inputs
Eight strongest inputs
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1 0.74 0.84 9.82, 11.80 5 ON, 7 OFF 4 ON, 6 OFF 4 ON, 4 OFF 0.64, 0.942 0.72 1.65 6.83, 11.96 7 ON, 6 OFF 6 ON, 4 OFF 4 ON, 4 OFF 1.90, 1.663 0.91 0.52 31.96, 14.00 8 ON, 5 OFF 7 ON, 3 OFF 5 ON, 3 OFF 1.65, 1.484 0.85 0.37 3.90, 3.40 6 ON, 10 OFF 3 ON, 7 OFF 3 ON, 5 OFF 1.28, 0.845 0.78 0.28 5.01, 3.49 9 ON, 8 OFF 6 ON, 4 OFF 5 ON, 3 OFF 1.26, 0.816 0.74 0.39 3.21, 3.36 9 ON, 10 OFF 2 ON, 8 OFF 1 ON, 7 OFF** 1.29, 0.917 0.74 1.09 13.25, 15.29 15 ON, 10 OFF 6 ON, 4 OFF 5 ON, 3 OFF 1.33, 1.558 0.63 1.16 11.41, 21.30 10 ON, 22 OFF** 1 ON, 9 OFF*** 1 ON, 7 OFF** 1.40, 1.379 0.64 0.82 1.67, 5.54 16 ON, 24 OFF 1 ON, 9 OFF*** 1 ON, 7 OFF** 1.41, 1.5210 0.55 0.91 1.47, 22.92 20 ON, 27 OFF 3 ON, 7 OFF 2 ON, 6 OFF 1.28, 1.37Average 0.73 0.80 8.85, 11.31 105 ON, 129 OFF 39 ON, 61 OFF** 31 ON, 49 OFF* 1.34, 1.24
ON – OFF distance is the average distance among all possible ON – OFF pairs within a geniculate group, in units of geniculate center diameters (same units as in Fig. 5). ON/OFF size is the ratio of receptive field sizes. SNR is signal-to-noise ratio calculated by dividing the peak of the receptive field (the pixel with maximum response) by the noise measure-ment (a pixel outside the receptive field). The strongest thalamic inputs were selected on the basis of the strength of the current sinks generated in cortex. Conduction time is measured in milliseconds as the difference between the time of the spike recorded in LGN (at threshold-crossing) and the time of the axonal response in the cortex (at the peak of the axonal current sink). *P = 0.04, **P = 0.03, ***P = 0.01 (χ2 test).
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To further compare the significance of the orientation predictions obtained with different population receptive fields, we performed Monte Carlo simulations. We estimated the cortical orientation preference from shuffled ON – OFF population receptive fields calculated by randomizing the receptive field positions of ON and OFF afferents within the same region of visual space covered by all afferents. The shuffled receptive field populations served to test the null hypothesis that the orientation preference of the column is unrelated to the clustering of ON and OFF afferents; this null hypothesis was rejected by our findings. The multiple comparisons between the orientation measured in the cortex and the orientation estimated from shuffled population thalamic receptive fields revealed
distributions expected by chance for both the average R2 values (Fig. 4a) and orientation differences (Fig. 4b). These distributions were then compared with the average R2 and orientation difference obtained with non-shuffled thalamic receptive field populations calculated using different methods. Clearly, the ON – OFF popu-lation receptive field from connected geniculate afferents provided the most accurate and most significant estimate of the columnar orientation preference (Fig. 4a,b).
Limited receptive field scatter from thalamic inputsThe accuracy with which the orientation preference of a cortical column can be estimated from ON and OFF geniculate inputs is
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Figure 3 The ON – OFF population receptive field from connected geniculate inputs provides the most accurate prediction of cortical orientation preference. (a,b) Orientation predictions obtained with population receptive fields of monosynaptically connected geniculate neurons, calculated as an ON – OFF subtraction (a) or an ON + OFF sum that disregards contrast polarity (b). Each panel quantifies the relationship between measured and predicted cortical orientation preference through linear correlation (top) and distribution of orientation differences (bottom). Examples of population receptive fields are shown around each scatter plot (red: ON, blue: OFF, purple: ON + OFF). (c) Orientation predictions from ON – OFF population receptive fields from geniculate neurons that were not connected but were retinotopically aligned with the cortical recordings. (d) ON – OFF population receptive fields from all geniculate neurons, connected and not connected. (e) Examples of population thalamic receptive fields shown in more detail as color maps. The population thalamic receptive field for each column is shown as ON – OFF (contour plot and top color map), ON + OFF (second row of color maps, starting from the top), ON sum (third row of color maps) and OFF sum (fourth row of color maps). The lines illustrate the 20% contour for the ON sum (continuous, red) and OFF sum (dotted, blue).
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particularly striking given the limited receptive field scatter of genicu-late afferents to a column revealed by single-axon STCSD analysis. According to the STCSD measurements, the probability that two geniculate cells will make monosynaptic connection with the same orientation column is exponentially related to the distance between their receptive fields. This exponential relationship could be seen within single orientation columns (Fig. 5a,b) and in the entire cell population (Fig. 5c). Almost all geniculate cell pairs (~95%) that made monosynaptic connection with an orientation column had receptive fields separated by less than 1.5 receptive field centers and covered an area of visual space of 2.5 receptive field centers (Fig. 5c). This area of visual space matches the average size of a cortical recep-tive field in layer 4 of the primary visual cortex in ferrets, cats and primates11–13,16,17,27,28. The percentage of unconnected geniculate afferents was also exponentially related to receptive field distance (Supplementary Fig. 2).
In the past, it was assumed that the receptive fields of geniculate affer-ents were always scattered along the preferred line of a cortical orien-tation column2,3,25, but our results show that this is not the case (for example, Fig. 2 and Supplementary Fig. 1). Just as the ON and OFF subregions of a cortical simple cell provide the most reliable estimate of the neuron’s orientation preference1,15,26, the receptive field subregions of an ON – OFF geniculate population provide the most accurate esti-mate of a column orientation preference. If the arrangement between ON and OFF subregions is disregarded, it is sometimes difficult to estimate the orientation preference of the receptive field from either geniculate populations or single cortical cells (Supplementary Fig. 3).
A clear consequence of the limited receptive field scatter of the geniculate inputs is that ON and OFF receptive fields must overlap. This extensive overlap provides an opportunity to build cortical recep-tive fields with diverse ON – OFF structures within the limits imposed by the ON – OFF population receptive field of the thalamic inputs. Consistent with this notion, the multiunit receptive fields measured in a cortical column showed considerable diversity, but the ON – OFF structures were far from being randomly distributed (data not shown).
This limited receptive field diversity is consistent with previous find-ings that ON and OFF afferents are not homogeneously distributed in cortical space18,29,30 and that OFF afferents dominate the cortical representation of central vision18. We found that the OFF dominance was most pronounced among the strongest thalamic afferents, both in the average of all columns and in individual columns (Table 1). For example, in one of the orientation columns (Fig. 2), there were 13 OFF and 2 ON thalamic afferents with STCSD strength >0.2 mV mm–2 (P = 0.004, χ2 test) but the predominance of OFF afferents was more modest when weaker afferents were included (16 ON, 24 OFF, P = 0.23, χ2 test). Similarly, in the other orientation column illus-trated in Figure 2, there were 19 OFF and 4 ON afferents with STCSD strength >0.1 mV mm–2, but the percentage of OFF afferents dropped from 82% to 69% when weaker afferents were included (4 ON, 19 OFF versus 10 ON, 22 OFF). We measured the correlation between ON-OFF number and average ON-OFF strength for each group of thalamic afferents that made monosynaptic connection with the same orientation column (Fig. 6). Consistently with the examples illus-trated above, the ratio of ON-OFF afferent number was significantly correlated with the ratio of ON-OFF afferent strength (R2 = 0.48, P = 0.01). This correlation supports the notion that the center of an OFF cortical domain receives input from more and stronger OFF than ON thalamic afferents (Fig. 6).
DISCUSSIONTo our knowledge, this study provides the first quantitative analy-sis of the population receptive field from convergent ON and OFF thalamic inputs to an orientation column in visual cortex. It shows that the receptive fields from ON and OFF thalamic inputs overlap extensively and are restricted to a small region of visual space. In spite of the extensive receptive field overlap, the population receptive field, calculated as ON – OFF average difference, has separate ON and OFF subregions that resemble those of cortical neurons in layer 4. Moreover, the arrangement of the ON and OFF subregions accurately matches the preferred orientation of the cortical column. Our results
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Figure 4 The orientation prediction from the ON – OFF population receptive field of geniculate inputs is highly significant and accurate, as demonstrated by Monte Carlo simulations. (a,b) Distributions of R2 (a) and orientation differences (b) calculated by comparing measurements of cortical orientation preference with predictions from shuffled population receptive fields of the geniculate inputs. The arrows show the R2 values and average orientation differences for non-shuffled population receptive fields. The ON – OFF population receptive field of connected geniculate afferents provides the largest R2 value and smallest orientation difference. The peak of the orientation-difference distribution is determined by the average and s.d. of the 10 orientation preferences measured in cortex.
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a cbFigure 5 The probability that two geniculate cells will make monosynaptic connections with the same orientation column is exponentially related to the distance between the geniculate receptive fields. (a,b) Exponential functions measured for the afferents to two orientation columns (in two different animals). (c) Exponential function measured in the entire geniculate cell population. Receptive field distance was measured using the diameter of the largest geniculate center within each pair as a unit. Inset: 95% of geniculate cell pairs had receptive fields separated by ≤1.5 geniculate centers and covered an area of visual space of 2.5 geniculate centers.
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provide a better understanding of how thalamic inputs are organized in visual cortex and support developmental models that predict a tight link between the ON-OFF organization of thalamic inputs and the organization of cortical orientation maps5–9.
Our results do not aim to show that cortical layer 4 performs a simplistic subtraction of ON and OFF receptive fields from thalamic inputs. There is strong evidence that layer 4 cortical receptive fields are constructed not only from thalamic inputs but also from intracortical inputs (including feedforward inhibition31) that were not measured in this study10,15–17,28,32,33. What our results show is that the popula-tion receptive field of ON and OFF thalamic inputs to an orientation column matches the orientation preference, size and structure of the receptive fields that emerge in cortical layer 4.
Limited receptive field scatter of thalamic inputsOne main result of this study is that the receptive fields from the thalamic inputs to a cortical orientation column cover a restricted region of visual space of just 2.5 geniculate centers (Fig. 5). This value of receptive field scatter is much more restricted than previously reported2 and could reflect differences in species (cat versus ferret) or methods (single-axon STCSD versus recordings from geniculate axons in muscimol-silenced cortex). The spatial coverage of 2.5 geniculate centers that we report matches the average receptive field size of a layer 4 neuron in the primary visual cortex of ferrets, cats and primates11–13. Moreover, the center-to-center receptive field distance (1.5 geniculate centers) matches the distance that separates the receptive field centers of connected geniculate and layer 4 cortical neurons along the long axis of the cortical receptive field (1.66)13. A similar center-to-center distance (1.6 geniculate centers) has been estimated in connections between layer 4 and layer 2+3 neurons in tree shrews3.
Our new measurements of geniculate receptive field scatter provide a constraint to models of cortical function that, in the absence of direct experimental measurements, have used estimates of receptive field scatter from 1.36 (ref. 8) to 3 (refs. 34,35) and 4 geniculate centers36. The limited receptive field scatter that we report, com-bined with the tendency of the geniculate receptive fields to aggregate around a single point in visual space (Fig. 2), seem ideal to construct accurate retinotopic maps in visual cortex.
ON-OFF receptive field and cortical orientationMost cortical layer 4 neurons within an orientation column have separate ON and OFF subregions whose geometrical arrangement matches the neuron’s orientation preference1,15,16,26,37. The source of this ON/OFF receptive field arrangement has been investigated by
measuring the receptive fields of thalamic inputs to individual layer 4 neurons in primary visual cortex13,25. These studies revealed great specificity in thalamocortical connections regarding receptive field sign (that is, ON thalamic cells made connections with layer 4 cortical neurons that had ON receptive field subregions in the same position of visual space). However, the source of this specificity was unclear. Did each layer 4 neuron ‘choose and pick’ ON and OFF thalamic affer-ents from a random sample available within the orientation column? Or were the ON and OFF thalamic afferents already sorted by recep-tive field position and sign, imposing the same ON-OFF receptive field structure on all layer 4 neurons within the column?
Our results suggest that ON and OFF thalamic axons impose a bias toward a common receptive field structure through the depth of the cortex. We found that the receptive fields from ON and OFF thalamic axons overlapped extensively but were partially segregated in visual space. As a consequence of this partial segregation, the population geniculate receptive field has a specific ON – OFF structure that limits the diversity of cortical receptive fields that can be constructed within each orientation column. This limited diversity is consistent with pre-vious studies in minks29, ferrets30 and cats18 that have shown that ON and OFF geniculate axons are partially segregated in cortical space. Our results also show that the ON-OFF segregation in cortical space is most pronounced among the strongest thalamic afferents within the orientation column (those that generate the strongest current sinks). Because synaptic density (and synaptic strength) is highest at the center of the thalamic axon terminal38, our results suggest that the center of an OFF cortical domain receives input from the central axon-terminals of OFF thalamic afferents (Fig. 6). It is not clear why we could not demonstrate cortical segregation of ON and OFF thalamic afferents in more than three cortical orientation columns (Table 1). Perhaps cortical current sinks have to be measured very close to the center of the axonal cluster (where the strongest thalamic afferents are) to reveal such segregation. It is also possible that the cluster size depends on eccentricity, which was restricted to <4° in the current study but ranged from 2° to 10° in previous work18.
The extensive ON-OFF receptive field overlap that we report among thalamic inputs to a cortical orientation column is in sharp contrast to the ON-OFF segregation reported among thalamic inputs to a single cortical cell13,25. Together, these studies suggest that neighboring ON and OFF thalamic afferents that have overlapping receptive fields coexist within the same orientation column but provide highly specific input to different layer 4 neurons. This organization of thalamic afferents could explain the diversity of receptive field structures that has been reported among neighboring neurons in cortical layer 4 (refs. 14,18).
The STCSD method is also more sensitive at detecting weak thalamic inputs than spike cross-correlations. Therefore, we cannot discard the possibility that some weak thalamic inputs to individual layer 4 neurons may have been undetected in previous studies because they failed to generate spikes13,25. These weak thalamic inputs could have overlap-ping receptive fields of opposite sign, as some intracellular measures suggest17,28. Future intracellular studies will be needed to investigate whether the match in receptive field sign is less precise in weak than in strong thalamocortical connections to single cortical neurons.
ON-OFF thalamic inputs and orientation mapsOur results provide support to developmental models of cortical ori-entation maps that rely on the organization of ON and OFF thalamic inputs in cortex5–9. Specifically, we provide proof for three main predictions from these models. First, we show that the receptive fields of ON and OFF thalamic inputs to a cortical orientation column do not cover visual space homogeneously and tend to aggregate in slightly
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Figure 6 The ratio of ON/OFF afferents within each orientation column was correlated with the ratio of ON/OFF afferent strength, estimated by STCSD. That is, in columns where OFF afferents were more numerous, the OFF afferents also generated stronger current sinks than the ON afferents. This finding is consistent with previous studies18,29,30 indicating that ON and OFF afferents cluster in different cortical domains (right).
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different positions of visual space (Table 1, Fig. 2 and Supplementary Fig. 1). Second, we show that the orientation preference of the column can be accurately predicted from the ON – OFF population recep-tive field of the thalamic inputs. Third, we show that the population receptive field of the thalamic inputs has an absolute spatial phase that differs across orientation columns (for example, the central subregion of the population receptive field is ON in some columns and OFF in others). In addition, we provide support for an intriguing prediction from a random-wiring model8 by showing that cortical orientation preference can sometimes be estimated from the population receptive field of unconnected thalamic afferents that are neighboring (and matched in retinotopy) to the connected ones.
Our results depart from ON-OFF segregation models in their prediction of a pronounced clustering for ON-OFF receptive field structure (or spatial phase) at the level of single cortical neurons. According to these models, most cortical neurons within an orienta-tion column should have the same absolute spatial phase (that is, in an OFF-dominated orientation column, most cortical cells should have overlapping OFF-subregions). This prediction is particularly strong in Miller’s model5,6, where a cortical column is modeled as a single corti-cal cell with a single phase. Ringach’s model8,9 has more receptive field diversity, but the receptive field coverage is considerably lower than that shown here and therefore the receptive field diversity is also more limited. Both models predict similar ON-OFF receptive field struc-tures within a cortical orientation column, a prediction that lacked experimental support14,18,39. Our results support this prediction by showing that the population receptive field of the thalamic afferents provides each cortical orientation column with a single spatial phase that resembles the ‘single cortical cell’ of Miller’s model and the aver-age cortical cell of Ringach’s model. In summary, the organization of thalamic afferents that we describe provides each cortical column with a dominant orientation preference and a dominant spatial phase and gives the column enough flexibility to construct a diverse family of cortical receptive fields to extract visual information efficiently.
METhODSMethods and any associated references are available in the online version of the paper at http://www.nature.com/natureneuroscience/.
Note: Supplementary information is available on the Nature Neuroscience website.
AcknowledgmenTSWe thank C. Weng for helping with some experiments. This work was supported by the US National Institutes of Health (grants EY05253 to J.M.A. and MH085357 to H.A.S.).
AUTHoR conTRIBUTIonSJ.J., Y.W. and J.M.A. performed the experiments, J.J., Y.W., J.M.A. and H.A.S. were involved in data analysis, and J.M.A., H.A.S., J.J. and Y.W. wrote the paper.
comPeTIng FInAncIAl InTeReSTSThe authors declare no competing financial interests.
Published online at http://www.nature.com/natureneuroscience/. Reprints and permissions information is available online at http://www.nature.com/reprintsandpermissions/.
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ONLINE METhODSelectrophysiological recordings. Recordings from LGN and visual cortex were performed in anesthetized cats. The surgery, anesthesia, visual stimulation and methods of spike triggered current-source-density analysis (STCSD) were as described18,19. All geniculate cells included had receptive fields within 4° of the area centralis (average: 2.15 ± 1.32°). All geniculate and cortical recordings used for analysis or illustration were new and different from those used in reference 18. Consistent with reference 18, the current sinks from OFF-OFF pairs outnum-bered the current sinks from ON-ON pairs (972 OFF-OFF versus 517 ON-ON cell pairs). Also, as would be expected from previous measurements of geniculate inputs at the cortical representation of the area centralis21, X afferents (n = 238) greatly outnumbered Y afferents (n = 54) in our sample. Cortical recordings were obtained with an array of 16 vertically aligned electrodes spanning 1.6 mm of cortex (NeuroNexus). LGN recordings were obtained with a 7-channel multielec-trode array with independently movable electrodes (Thomas Recording). All pro-cedures were performed in accordance with the guidelines of the US Deparment of Agriculture and approved by the Institutional Animal Care and Use Committee at the State University of New York, College of Optometry.
Visual stimulation and receptive field analysis. Stimuli were presented on a monitor located at 114 cm distance through a dilated pupil. LGN receptive fields were mapped with binary white noise by reverse correlation analysis (16 × 16 squares, 0.1–0.45° square) and smoothed with a cubic spline. The LGN recep-tive field center was calculated at the response peak of the LGN cell (~35 ms; monitor refresh rate, 120 Hz; stimulus update, 60 Hz). The receptive field cover-age of the geniculate inputs to an orientation column (Fig. 2b) was calculated as follows. First, the receptive field of each geniculate input was mapped with white noise and the receptive field pixels that generated >20% of the maximum response were selected. Then, we counted the selected receptive field pixels for all geniculate inputs at each position of visual space and plotted them in three-dimensional graphs (Fig. 2b). The receptive field populations of the genicu-late afferents (Figs. 2d and 3 and Supplementary Fig. 1) were calculated by averaging all the geniculate receptive fields (no threshold was used for these measurements). Each receptive field was first normalized after dividing by the maximum response. Then, ON and OFF receptive fields were either subtracted (ON – OFF) or summed (ON + OFF). The SNR of each population receptive field was calculated by dividing the value of the central pixel in the receptive field (receptive field peak) by the noise in the receptive field measurements. The noise was calculated at a pixel that was far from and completely outside the receptive field (left top corner of stimulus frame) by averaging 16 temporal delays between stimulus and response in this pixel. The SNR of ON – OFF population receptive field should approach 1 if the ON and OFF coverage was completely balanced. For example, if the population receptive field had only two ON and OFF receptive fields with identical positions, the ON – OFF difference would equal the noise measurement and SNR would be 1. The preferred orientation from the popula-tion geniculate receptive field (Figs. 2e, 3 and 4 and Supplementary Fig. 1) was calculated by performing a two-dimensional discrete Fourier transform, a method that allowed us to make comparable measurements across ON – OFF, ON + OFF and multiple shuffled ON – OFF population receptive fields. The two-dimensional discrete Fourier transform was adjusted by ± 180° for orientation differences larger than 90° (for example, the difference between 180 and 0 is 0, not 180). The distance between two geniculate receptive fields (Table 1 and Fig. 5) was measured in units of geniculate receptive field centers. First, we measured the spatial location of the pixel at the center of the receptive field that generated the maximum response (receptive field peak). Then, we measured the distance between pairs of receptive field peaks and divided the distance by the diameter of the largest receptive field.
The location of cortical layer 4 was determined by CSD analysis, as a strong sink in the middle of the cortex generated by repeated light flashes presented for 1 s (Fig. 1d). It is important to distinguish these full-field-evoked current sinks from those triggered by spikes from a single LGN neuron. The current sinks trig-gered by full-field stimuli were only used to quickly locate the depth of layer 4 in the cortex. The current sinks triggered by the spikes from a single LGN neuron are much more restricted in cortical space and were used to identify LGN neu-rons making monosynaptic connection with the cortical recording site that was being studied. The multiunit activity recorded in the cortex was used to measure the preferred orientation of each cortical column with moving bars presented
at 16 different directions (averaging two directions of movement). The average orientation difference across the 16 vertically arranged electrodes in cortex was 14° (range, 4–43°). It is important to emphasize that cortical multiunit activity was not used for receptive field measurements. All receptive field measurements originated from recordings in LGN, not cortex.
Spike-triggered current source density analysis. STCSD was used to identify geniculate cells that made monosynaptic connections within the recorded cortical orientation column, as described18,19. The spikes from each recorded geniculate cell were used to trigger the LFP activity recorded through the depths of the cortex. Then the spike-triggered LFPs were used to calculate the current sinks and sources generated by each geniculate afferent through the depths of the cortex with current source density analysis20,21. The spatial resolution of single-axon STCSD is around ±150 µm, as demonstrated experimentally18,19,40 and in simulations41. This high spatial resolution is consistent with recent evidence that stimulus-driven LFPs can be restricted to a radius of 250 µm42. However, the stimulus-driven LFP reflects the sum of cortical neurons that are activated by multiple geniculate axons whereas the single-axon LFP reflects the sum of cortical neurons activated by a single geniculate axon. Note that the rise time of the axonal LFP response is ≤0.25 ms43, which is much briefer than the precise synchrony among neighboring geniculate neurons (±1 ms)24,44 and it can be sometimes an order of magnitude smaller than the differences in conduction time among neighboring axons (Fig. 1e). Note also that although the STCSD signal results from the presynaptic spikes of a single geniculate afferent, the postsynaptic com-ponent reflects hundreds of cortical cells that receive input from this afferent. As each geniculate afferent is likely to connect to a different subset of layer 4 neurons with different efficacy, the strength of the single-axon STCSD also varies with each afferent. As in reference 18, we considered a geniculate afferent connected to the recorded orientation column if the amplitude of the postsynaptic sink in the initial 1 ms was larger than 40 µV mm–2. Note that a substantial postsynaptic current sink can be generated by a few strong connections or by multiple weak connections made by the same geniculate axon. However, our selection criterion has nothing to do with the number or type of cortical neurons involved in the STCSD signal. The only geniculate afferents that are not included in the sample of ‘connected geniculate neurons’ are those that do not make connections within the orientation column or make connections that are too weak to generate a signifi-cant current sink. The method of STCSD is more sensitive than other cell-to-cell connectivity methods for detecting weak connections within a cortical cylinder simply because it detects subthreshold responses, and it relies on the postsynaptic responses of multiple cortical cells and not just one. The STCSD method is very sensitive in detecting different types of thalamic inputs that originate in X cells, Y cells and cells with slow conduction velocities43 (Fig. 1e). However, some excita-tory inputs may be undetected with this technique, either because they are too weak or because they are masked by current sources (passive or actively generated by intracortical inhibition).
Statistical analysis. The Monte Carlo shuffle analysis of the receptive fields was performed by taking each group of geniculate receptive fields, randomizing their positions (within the region of visual space covered by all of the afferents) and then calculating the ON – OFF population receptive field. A two- dimensional discrete Fourier transform was used to calculate the orientation preference from the population receptive field. The predicted orientations for each of the 10 groups of geniculate afferents were compared with the orien-tation preferences measured in cortex. We performed 1,000 simulations to obtain the distributions of R2 and orientation differences illustrated in Figure 4. The maximum orientation difference allowed was 90° (for example, the dif-ference between 10 and 170 was 20 and not 160). The Wilcoxon rank sum test was used to compare means among the 10 groups of geniculate afferents (for example, comparison of SNR and ON/OFF dominance in Table 1). The χ2 test was used to compare average orientation differences predicted by different population receptive fields (for example, ON – OFF versus ON + OFF) and to compare the number of ON and OFF afferents in individual orientation columns (Table 1).
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