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Cortical direction selectivity emerges at convergence of thalamic synapses

Naturevolume 558pages8086 (2018) | Download Citation


Detecting the direction of motion of an object is essential for our representation of the visual environment. The visual cortex is one of the main stages in the mammalian nervous system in which the direction of motion may be computed de novo. Experiments and theories indicate that cortical neurons respond selectively to motion direction by combining inputs that provide information about distinct spatial locations with distinct time delays. Despite the importance of this spatiotemporal offset for direction selectivity, its origin and cellular mechanisms are not fully understood. We show that approximately 80 ± 10 thalamic neurons, which respond with distinct time courses to stimuli in distinct locations, excite mouse visual cortical neurons during visual stimulation. The integration of thalamic inputs with the appropriate spatiotemporal offset provides cortical neurons with a primordial bias for direction selectivity. These data show how cortical neurons selectively combine the spatiotemporal response diversity of thalamic neurons to extract fundamental features of the visual world.

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  1. 1.

    Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. (Lond.) 160, 106–154 (1962).

  2. 2.

    Hubel, D. H. & Wiesel, T. N. Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. (Lond.) 148, 574–591 (1959).

  3. 3.

    Livingstone, M. S. Mechanisms of direction selectivity in macaque V1. Neuron 20, 509–526 (1998).

  4. 4.

    Movshon, J. A., Thompson, I. D. & Tolhurst, D. J. Spatial summation in the receptive fields of simple cells in the cat’s striate cortex. J. Physiol. (Lond.) 283, 53–77 (1978).

  5. 5.

    McLean, J. & Palmer, L. A. Contribution of linear spatiotemporal receptive field structure to velocity selectivity of simple cells in area 17 of cat. Vision Res. 29, 675–679 (1989).

  6. 6.

    Albrecht, D. G. & Geisler, W. S. Motion selectivity and the contrast-response function of simple cells in the visual cortex. Vis. Neurosci. 7, 531–546 (1991).

  7. 7.

    Reid, R. C., Soodak, R. E. & Shapley, R. M. Directional selectivity and spatiotemporal structure of receptive fields of simple cells in cat striate cortex. J. Neurophysiol. 66, 505–529 (1991).

  8. 8.

    Saul, A. B. & Humphrey, A. L. Evidence of input from lagged cells in the lateral geniculate nucleus to simple cells in cortical area 17 of the cat. J. Neurophysiol. 68, 1190–1208 (1992).

  9. 9.

    DeAngelis, G. C., Ohzawa, I. & Freeman, R. D. Spatiotemporal organization of simple-cell receptive fields in the cat’s striate cortex. II. Linearity of temporal and spatial summation. J. Neurophysiol. 69, 1118–1135 (1993).

  10. 10.

    Jagadeesh, B., Wheat, H. S., Kontsevich, L. L., Tyler, C. W. & Ferster, D. Direction selectivity of synaptic potentials in simple cells of the cat visual cortex. J. Neurophysiol. 78, 2772–2789 (1997).

  11. 11.

    Priebe, N. J. & Ferster, D. Direction selectivity of excitation and inhibition in simple cells of the cat primary visual cortex. Neuron 45, 133–145 (2005).

  12. 12.

    Reichardt, W. in Sensory Communication (ed. Rosenblith, W. A.) 303–317 (MIT Press, Cambridge MA, 1961).

  13. 13.

    Barlow, H. B. & Levick, W. R. The mechanism of directionally selective units in rabbit’s retina. J. Physiol. (Lond.) 178, 477–504 (1965).

  14. 14.

    Adelson, E. H. & Bergen, J. R. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2, 284–299 (1985).

  15. 15.

    Suarez, H., Koch, C. & Douglas, R. Modeling direction selectivity of simple cells in striate visual cortex within the framework of the canonical microcircuit. J. Neurosci. 15, 6700–6719 (1995).

  16. 16.

    Maex, R. & Orban, G. A. Model circuit of spiking neurons generating directional selectivity in simple cells. J. Neurophysiol. 75, 1515–1545 (1996).

  17. 17.

    Watson, A. B. & Ahumada, A. J. Jr. Model of human visual-motion sensing. J. Opt. Soc. Am. A 2, 322–341 (1985).

  18. 18.

    Li, Y. T., Fang, Q., Zhang, L. I. & Tao, H. W. Spatial asymmetry and short-term suppression underlie direction selectivity of synaptic excitation in the mouse visual cortex. Cereb. Cortex (2017).

  19. 19.

    Goodwin, A. W., Henry, G. H. & Bishop, P. O. Direction selectivity of simple striate cells: properties and mechanism. J. Neurophysiol. 38, 1500–1523 (1975).

  20. 20.

    Emerson, R. C. & Gerstein, G. L. Simple striate neurons in the cat. II. Mechanisms underlying directional asymmetry and directional selectivity. J. Neurophysiol. 40, 136–155 (1977).

  21. 21.

    Ganz, L. & Felder, R. Mechanism of directional selectivity in simple neurons of the cat’s visual cortex analyzed with stationary flash sequences. J. Neurophysiol. 51, 294–324 (1984).

  22. 22.

    Li, Y. T., Liu, B. H., Chou, X. L., Zhang, L. I. & Tao, H. W. Strengthening of direction selectivity by broadly tuned and spatiotemporally slightly offset inhibition in mouse visual cortex. Cereb. Cortex 25, 2466–2477 (2015).

  23. 23.

    Sillito, A. M. Inhibitory processes underlying the directional specificity of simple, complex and hypercomplex cells in the cat’s visual cortex. J. Physiol. (Lond.) 271, 699–720 (1977).

  24. 24.

    Saul, A. B. & Humphrey, A. L. Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus. J. Neurophysiol. 64, 206–224 (1990).

  25. 25.

    Alonso, J.-M., Usrey, W. M. & Reid, R. C. Rules of connectivity between geniculate cells and simple cells in cat primary visual cortex. J. Neurosci. 21, 4002–4015 (2001).

  26. 26.

    Stanley, G. B. et al. Visual orientation and directional selectivity through thalamic synchrony. J. Neurosci. 32, 9073–9088 (2012).

  27. 27.

    Ferster, D., Chung, S. & Wheat, H. Orientation selectivity of thalamic input to simple cells of cat visual cortex. Nature 380, 249–252 (1996).

  28. 28.

    Marshel, J. H., Kaye, A. P., Nauhaus, I. & Callaway, E. M. Anterior–posterior direction opponency in the superficial mouse lateral geniculate nucleus. Neuron 76, 713–720 (2012).

  29. 29.

    Cruz-Martín, A. et al. A dedicated circuit links direction-selective retinal ganglion cells to the primary visual cortex. Nature 507, 358–361 (2014).

  30. 30.

    Sun, W., Tan, Z., Mensh, B. D. & Ji, N. Thalamus provides layer 4 of primary visual cortex with orientation- and direction-tuned inputs. Nat. Neurosci. 19, 308–315 (2016).

  31. 31.

    Hillier, D. et al. Causal evidence for retina-dependent and -independent visual motion computations in mouse cortex. Nat. Neurosci. 20, 960–968 (2017).

  32. 32.

    Lien, A. D. & Scanziani, M. Tuned thalamic excitation is amplified by visual cortical circuits. Nat. Neurosci. 16, 1315–1323 (2013).

  33. 33.

    Niell, C. M. & Stryker, M. P. Highly selective receptive fields in mouse visual cortex. J. Neurosci. 28, 7520–7536 (2008).

  34. 34.

    Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G. & Deisseroth, K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat. Neurosci. 8, 1263–1268 (2005).

  35. 35.

    Bruno, R. M. & Sakmann, B. Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312, 1622–1627 (2006).

  36. 36.

    Sedigh-Sarvestani, M. et al. Intracellular, in vivo, dynamics of thalamocortical synapses in visual cortex. J. Neurosci. 37, 5250–5262 (2017).

  37. 37.

    Piscopo, D. M., El-Danaf, R. N., Huberman, A. D. & Niell, C. M. Diverse visual features encoded in mouse lateral geniculate nucleus. J. Neurosci. 33, 4642–4656 (2013).

  38. 38.

    Li, Y. T., Ibrahim, L. A., Liu, B. H., Zhang, L. I. & Tao, H. W. Linear transformation of thalamocortical input by intracortical excitation. Nat. Neurosci. 16, 1324–1330 (2013).

  39. 39.

    Smith, S. L., Smith, I. T., Branco, T. & Häusser, M. Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo. Nature 503, 115–120 (2013).

  40. 40.

    Cleland, B. G., Dubin, M. W. & Levick, W. R. Sustained and transient neurones in the cat’s retina and lateral geniculate nucleus. J. Physiol. (Lond.) 217, 473–496 (1971).

  41. 41.

    Mastronarde, D. N. Two classes of single-input X-cells in cat lateral geniculate nucleus. I. Receptive-field properties and classification of cells. J. Neurophysiol. 57, 357–380 (1987).

  42. 42.

    Marrocco, R. T. Sustained and transient cells in monkey lateral geniculate nucleus: conduction velocites and response properties. J. Neurophysiol. 39, 340–353 (1976).

  43. 43.

    Hu, H. & Agmon, A. Differential excitation of distally versus proximally targeting cortical interneurons by unitary thalamocortical bursts. J. Neurosci. 36, 6906–6916 (2016).

  44. 44.

    Zhao, X., Chen, H., Liu, X. & Cang, J. Orientation-selective responses in the mouse lateral geniculate nucleus. J. Neurosci. 33, 12751–12763 (2013).

  45. 45.

    Hippenmeyer, S. et al. A developmental switch in the response of DRG neurons to ETS transcription factor signaling. PLoS Biol. 3, e159 (2005).

  46. 46.

    Zhao, S. et al. Cell type–specific channelrhodopsin-2 transgenic mice for optogenetic dissection of neural circuitry function. Nat. Methods 8, 745–752 (2011).

  47. 47.

    Saleem, A. B. et al. Subcortical source and modulation of the narrowband gamma oscillation in mouse visual cortex. Neuron 93, 315–322 (2017).

  48. 48.

    Margrie, T. W., Brecht, M. & Sakmann, B. In vivo, low-resistance, whole-cell recordings from neurons in the anaesthetized and awake mammalian brain. Pflugers Arch. 444, 491–498 (2002).

  49. 49.

    Fee, M. S., Mitra, P. P. & Kleinfeld, D. Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability. J. Neurosci. Methods 69, 175–188 (1996).

  50. 50.

    Hill, D. N., Mehta, S. B. & Kleinfeld, D. Quality metrics to accompany spike sorting of extracellular signals. J. Neurosci. 31, 8699–8705 (2011).

  51. 51.

    Olsen, S. R., Bortone, D. S., Adesnik, H. & Scanziani, M. Gain control by layer six in cortical circuits of vision. Nature 483, 47–52 (2012).

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We thank J. Evora for help with genotyping and mouse husbandry, S. Hestrin for allowing us to perform some of the experiments in his laboratory, R. Beltramo for helping with extracellular recordings, J. S. Isaacson, B. L. Bloodgood and R. A. Nicoll for comments on the manuscript, E. J. Chichilnisky and members of the Scanziani and Isaacson laboratories for discussions of this project. This research was supported by the Gatsby Charitable Foundation, the Howard Hughes Medical Institute and the Jane Coffin Childs Fellowship to A.D.L.

Reviewer information

Nature thanks J. Alonso, S. Hofer and B. Roska for their contribution to the peer review of this work.

Author information


  1. Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA

    • Anthony D. Lien
    •  & Massimo Scanziani
  2. Center for Neural Circuits and Behavior, Neurobiology Section and Department of Neuroscience, University of California San Diego, La Jolla, CA, USA

    • Anthony D. Lien
    •  & Massimo Scanziani
  3. Department of Physiology, University of California San Francisco, San Francisco, CA, USA

    • Anthony D. Lien
    •  & Massimo Scanziani
  4. Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA

    • Massimo Scanziani


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A.D.L. and M.S. designed the study. A.D.L. conducted all experiments and analysis. A.D.L. and M.S. wrote the paper.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Anthony D. Lien or Massimo Scanziani.

Extended data figures and tables

  1. Extended Data Fig. 1 Cortical silencing

    a, Experimental configuration. Channelrhodopsin-2 (ChR2) is expressed in cortical inhibitory neurons to suppress neuronal activity upon illumination with a blue LED while performing extracellular recordings. b, Visually evoked activity (full-field drifting gratings) from units isolated throughout the cortical depth is suppressed upon LED illumination. Black lines, ChR2 was expressed in all GABAergic neurons (GABA, γ-aminobutyric acid; VGAT-ChR2 mice; 138 units recorded with silicon probes; 98.9 ± 2.7% silencing in 25 units recorded above 500 μm depth; 99.4 ± 2% silencing in 113 units recorded below 500 μm; 5 mice). Red lines, ChR2 was conditionally expressed in parvalbumin (PV)-expressing neurons through viral injection into the visual cortex of the PV-Cre mouse line (13 loose patch recordings in layer 4; 2 mice; 100% silencing). c, As in b but specifically for units recorded between 450–650 μm depth (black; 99.8 ± 0.6% silencing; n = 26; 3 mice) and 650–950 μm depth (green; putative layer 6; 99.8 ± 2.4% silencing; n = 48; 3 mice). These units are a subset of the units from VGAT-ChR2 mice illustrated in b for which the recording depth could be estimated. d, Percentage of visually evoked spikes remaining during LED illumination across cortical depths deeper than 450 μm. The units are the same as in c. e, Peristimulus time histogram of two units located at 615 μm (left) and 890 μm (right) depth in response to drifting gratings under control conditions (black) and during cortical silencing (blue). The duration of the visual stimulus and of the LED illumination is illustrated by the horizontal bars. f, Experimental configuration. As in a but whole-cell recordings from layer 4 neurons instead of extracellular recordings. g, Whole-cell voltage-clamp recording (Vholding = −70 mV) of a layer 4 neuron (same neuron as in Fig. 2a). Response to drifting gratings (left; two identical cycle averages are shown for clarity) and static gratings (right; average of 10 traces). The grey indicates control conditions; the black trace was recorded during LED illumination to isolate thalamic excitation. h, Distribution of remaining excitatory charge upon LED illumination for drifting gratings (66 recordings as in g, left) and static gratings presented at the preferred spatial phase (53 recordings as in g, right). The visually evoked excitation was reduced by about 65% (drifting grating, 63 ± 16%; static grating, 68 ± 16).

  2. Extended Data Fig. 2 The contribution of the directional preference of the thalamic charge to the direction selectivity index of F1Thal.

    a, The same plot as in Fig. 1g, right. The data points for which QThal and F1Thal prefer the same direction (positive values on the y axis) are shown in red; data points for which QThal and F1Thal prefer opposite directions (negative values on the y axis) are orange. Note that the average absolute value of DSI QThal is slightly but significantly larger for the red data points (red points: 0.087 ± 0.053; n = 32; orange points: 0.048 ± 0.037; n = 20; P = 0.003; t-test), indicating a slight bias of QThal in the preferred direction. To determine the effect of this slight bias in thalamic charge on DSI F1Thal, we have equalized the thalamic charge evoked by gratings drifting in both directions (see bd). b, Example recording from a cortical neuron for which the charge of thalamic excitation is larger in the preferred versus the non-preferred direction of F1Thal. Top, thalamic excitation as recorded (non-equalized) in response to a grating drifting in the preferred (left) and non-preferred (right) direction. Bottom, same as top but after scaling the response to the non-preferred direction such that the charge is the same in either direction (charge equalization). c, Cycle average of thalamic excitation with superimposed sinusoidal fit (red). Top, as recorded; bottom, after charge equalization. After charge equalization the direction preference is maintained but, for this particular example, the DSI is reduced (see filled data point in d). d, Scatter plot for all recordings (green points indicate DSI F1Thal >0.3; blue points indicate DSI F1Thal < 0.3). The filled data point is the example above. The equalization leads to only a small change in DSI F1Thal (all points: 0.28 ± 0.20 before and 0.26 ± 0.21 after equalization; P = 0.034; paired t-test; n = 66; green points: 0.49 ± 0.15 before and 0.46 ± 0.17 after equalization; P = 0.022; paired t-test; n = 25; blue points: 0.16 ± 0.09 before and 0.14 ± 0.11 after equalization; P = 0.300; paired t-test; n = 41).

  3. Extended Data Fig. 3 Predicting the DSI for various temporal frequencies from the response to static gratings.

    The amplitude of the F1 modulation was determined from the algebraic sum of the thalamic EPSCs evoked by each of the 16 phases of the static grating, as in Fig. 2c, d. The thalamic EPSCs were staggered in time to mimic different temporal frequencies of a drifting grating (for example, at 4 Hz a cycle lasts 250 ms and hence the response to each one of the phases is staggered by 15.6 ms (250/16 ms) relative to the preceding one). The DSI was computed by comparing the F1 modulation of the sum in which EPSCs were ordered according to the spatial phase sequence simulating the motion of the grating in one direction against the sum simulating motion in the opposite direction. Green and blue traces, average of all cells for which DSI F1Thal to drifting gratings was larger (n = 18) or smaller (n = 28) than 0.3, respectively. For the dotted traces, the computed DSI was normalized to the peak for each cell; right ordinate. a, The full 250-ms response to static gratings was used to compute the DSI at each temporal frequency. Note the reversal of direction preference at higher temporal frequencies. b, Only the initial x milliseconds of the response to static gratings were used to compute the DSI, x being the half period of the temporal frequency to be computed (for example, for 4 Hz, x = 125 ms). The rationale for this approach is that the interactions between excitatory inputs that are relevant for the emergence of direction selectivity probably occur within a half cycle.

  4. Extended Data Fig. 4 The time course of the activity of thalamic units recorded in dLGN in response to static gratings is similar across spatial phases.

    a, Example thalamic unit with transient response to static gratings during cortical silencing. Top, spatiotemporal receptive field. Bottom, PSTHs in response to each phase of the static grating used to construct the spatiotemporal receptive field illustrated above. The PSTH at the preferred phase is highlighted by a thicker trace. The preferred phase is defined as the phase closest to the vector average of the response at each phase. The brackets show the time windows over which the early (30–110 ms) and late (110–230 ms) firing rates were averaged (Re and Rl, respectively) to compute the early/late index [(ReRl)/(Re + Rl)]. This unit has an early/late index of 1 for static gratings presented at the preferred phase and of 0.88 and 1 for gratings presented at phases of ±45° from the preferred phase. b, As in a but for an example thalamic unit with sustained response. The arrows illustrate the preferred spatial phase and the phases separated by ±45°. This unit has an early/late index of −0.3 for static gratings presented at the preferred phase and of −0.08 and 0.22 for gratings presented at phases of ±45° from the preferred phase. c, Heat maps of responses to static gratings for 177 thalamic units (24 mice) during cortical silencing. Left, each row is the amplitude of the PSTH of one of the thalamic units in response to the preferred phase of the static grating. The units are ordered according to their early/late index in response to the preferred phase. Middle, same as left but in response to a static grating, the phase of which is 45° below the preferred phase. The order of the units has not been changed; that is, it is the same as on the left. Right, same as left but in response to a static grating with a phase 45° above the preferred phase. The order is the same as on the left. Note that transient and sustained units maintain their characteristic firing dynamics even in response to static gratings presented at phases of ±45° from the preferred phase. d, Scatter plots of the early/late index computed in response to static gratings presented at the preferred phase and at phases of ±45° from the preferred phase. Note that in all plots the data are close to the unity line. e, Distribution of direction selectivity indexes of the firing rates (DSI F0) of the 177 thalamic units in d. The vertical dotted line is DSI F0 = 0.3. In 22 units DSI F0 was greater than 0.3. f, As in d but specifically for those thalamic units with DSI F0 values greater than 0.3. Colours indicate the value of DSI F0, according to the colour scale on the right.

  5. Extended Data Fig. 5 Criteria for identifying monosynaptically connected thalamocortical pairs.

    Thalamocortical pairs were identified on the basis of two criteria: Criterion 1 (illustrated in c) sets a threshold for the thalamic unit spike-triggered average of the time derivative of the current recorded in L4 cortical neurons. Criterion 2 (illustrated in d) sets a threshold and a time window for the distribution of events detected in the time derivative of the L4 current around the time of the spike in the thalamic unit. Both criteria have to be satisfied for the thalamic unit and the L4 cortical neuron to be considered as a connected pair. a, Isolation of thalamic units in the dLGN. Left, first two principal components illustrating three separable clusters attributed to three independent thalamic units (units x, y and w in red, grey and blue, respectively). Right, electrophysiological recording illustrating the average spike shape recorded from seven electrodes for the three thalamic units. b, Differentiation of the current recorded in L4 neurons, the same experiment as in a. Top trace, the current recorded in the whole-cell configuration from an L4 cortical neuron (Vholding = −70 mV) during the presentation of a drifting grating (single trial). Middle trace, the temporal derivative of the above current (dI/dt). Lower, the times at which each one of the three thalamic units from a (x, red; y, grey; w, blue) fired during the same trial. c, Criterion 1. Left, spike triggered average of dI/dt of the current recorded in the L4 neuron for the three thalamic units illustrated in a. Time 0 denotes the time of the spike. Right, same spike-triggered averages shown on the left after high-pass filtering and z-scoring (see Methods). Note that only unit w (blue) crosses the 5z threshold. d, Criterion 2. Top left, seven individual time derivatives of currents (dI/dt) recorded in the L4 neuron (same as in b) aligned relative to seven spikes recorded in unit w (time 0 denotes the time of the spike). Each asterisk shows an event crossing the threshold of −36 pA ms−1. Top right, same as left but represented as a heat map of the amplitude of dI/dt for 761 traces (the heat map colour scale ranges from +50 pA ms−1 to −200 pA ms−1). This heat map clearly illustrates an increase in event probability around 2 ms after the spike in unit w. Bottom left, the PSpTH for the events detected in the 761 traces illustrated above. The peak of the PSpTH is used to determine the latency (that is, the time interval between the spike recorded in the thalamic unit and the occurrence of a postsynaptic response detected in the L4 cortical neuron). The half width at half maximum is used to determine the jitter of that response (in this example the latency is 2 ms and the jitter is 188 microseconds). Bottom right, same as left but z-scored. The PSpTH must cross a threshold of 3.5z within 1–4 ms after the spike in the thalamic unit for the thalamic unit to be considered as synaptically connected to the L4 neuron. e, Left, unit w spike-triggered average of the response recorded in the same L4 neuron as in a. The continuous blue line represents unshuffled trials, the dotted line represents shuffled trials (see Methods). Right, the difference between the shuffled and unshuffled trials is used to isolate the uEPSC between unit w and the recorded L4 neuron.

  6. Extended Data Fig. 6 Unitary EPSCs, EPSPs and spatiotemporal receptive fields of presynaptic thalamic units.

    a, Each panel illustrates one of the 23 paired recordings. The upper part of the panel shows the shift-subtracted uEPSP (red; Methods) and the shift-subtracted uEPSC (blue; Methods) recorded during visual stimulation. The spatiotemporal receptive field (heat map) of the presynaptic thalamic unit, obtained from the response to static gratings, is shown on the bottom. For some pairs the uEPSC recorded during spontaneous activity (spont uEPSC; grey; Methods) is also shown. uEPSCs are recorded during cortical silencing; uEPSPs are recorded under control conditions. The vertical line at time 0 marks the time of the peak of the extracellularly recorded action potential in the presynaptic thalamic unit. Pair numbers of the same colour were recorded in the same postsynaptic layer 4 cortical neuron. The heat map shows the spatiotemporal receptive field of the thalamic unit in response to static gratings. Each spatiotemporal receptive field is centred (157.5°) on the preferred spatial phase (defined as the phase that produced the most spikes) of its unit except for converging pairs which are aligned to the average preferred phase of the converging units. The response of pairs marked by an asterisk were not included in the analysis of static gratings because of their poorly defined spatiotemporal receptive field. The early/late index (see Extended Data Fig. 4) for the preferred phase of the presynaptic thalamic unit is given for each pair on the top right in black except for pairs with an asterisk. uEPSCs (blue) are the average of 49–970 spike-triggered traces. uEPSPs (red) are the average of 101–1,496 spike triggered traces. Spontaneous uEPSCs (grey) are the average of 30–412 spike triggered traces. Units of pairs 10, 11, 12, 13, 14, 18, 21 and 22 are the eight presynaptic units to the compound neuron in Fig. 4 and correspond to the units numbered in Fig. 4 as 6, 7, 1, 2, 8, 5, 4 and 3, respectively. Pair 4 corresponds to the pair shown in Fig. 6 and in Extended Data Fig. 5. b, Top, correlation between the amplitude of the visually evoked uEPSC (blue in a) and the spontaneous uEPSC (grey in a) for those pairs in which both could be recorded (r = 0.95; P = 5.9 × 10−9; n = 17). The diagonal line indicates unity. Bottom, correlation between the amplitude of the visually evoked uEPSC (blue in a) and the uEPSP (red in a) for all pairs (r = 0.59; P = 0.0028; n = 23). The diagonal line is a linear fit to the data with a slope of 0.056 mV pA−1.

  7. Extended Data Fig. 7 The direction selectivity of presynaptic thalamic units does not contribute to the direction selectivity of L4 neurons.

    a, Top, the DSI of the average firing rate of each presynaptic thalamic unit (DSI F0) is plotted against DSI F1Thal of the postsynaptic L4 cortical neuron. Bottom, the DSI of the F1 modulation of the firing rate of each presynaptic thalamic unit (DSI F1) is plotted against DSI F1Thal. Note the absence of correlation between DSI F1Thal and DSI F0 (r = 0.09; P = 0.68) and DSI F1 (r = 0.16, P = 0.46) of the thalamic unit. Also, note that in only about half of the pairs (12 out of 23 in the top graph; 11 out of 23 in the bottom graph) the presynaptic thalamic unit and the postsynaptic cortical neuron share the same preferred direction, as expected by chance (P = 1.00 for both, binomial test). As such, the DSI of thalamic units does not predict DSI F1Thal of the cortical neuron. b, Top, the PSTHs of each of the eight units contributing to the compound neuron (from Fig. 4). The units were temporally aligned relative to each other using the phase of the F1 modulation of thalamic excitation recorded in their postsynaptic L4 target neurons in response to gratings drifting in the preferred (red) and non-preferred direction (black). Two identical cycles are shown for clarity. The equalized PSTHs (that is, the PSTHs that were scaled such that the firing rate of the thalamic unit is the same in either direction) are shown in green. Only the first cycle is equalized to facilitate comparison. Bottom, summed PSTHs of the eight presynaptic thalamic units (pink and grey lines are sinusoidal fits; from Fig. 4). The green traces are the summed activity of the equalized PSTHs. Note the similarity between the control and the equalized summed activity.

  8. Extended Data Fig. 8 Number of thalamic neurons contributing to the visually evoked response of an L4 neuron.

    a, Distribution of the number of unitary contributions of thalamic neurons necessary to equal the total thalamic charge recorded in an L4 cortical neuron in response to a grating drifting in the preferred direction during cortical silencing. The distribution is obtained by randomly sampling with replacement the individual contributions from each of the 23 pairs, 10,000 times. In each iteration, unitary contributions were sampled until their sum reached 100%. To compute the unitary contribution of a thalamic unit, we first convolved the spike train of the unit in response to a drifting grating with the uEPSC that that unit evoked in the postsynaptic L4 cortical neuron during cortical silencing. We then integrated the resulting current in time and normalized the obtained charge by the total charge recorded in the postsynaptic cortical neuron in response to the drifting grating, also during cortical silencing. Unitary contributions are expressed as a percentage of the total charge. On average, 80.9 ± 10.7 thalamic units (average ± s.d. of the obtained distribution) contribute to the visually evoked thalamic current in an L4 cortical neuron. b, The number of unitary contributions of thalamic neurons necessary to equal the total thalamic charge as a function of the fraction of ‘big contributors’. Because the units that contribute a large fraction of the total charge (big contributors) may have been under-sampled (as a consequence of a skewed distribution) we have arbitrarily increased their fraction in the pool of unitary contributions and determined the average number of unitary contributions necessary to equal the total charge, as above. Big contributors were defined as those thalamic units that contribute more than 2% of the total charge. They represent 26% of all unitary contributions in our dataset of 23 pairs (6 pairs; arrow). Increasing the fraction of big contributors (x axis) progressively reduces the average number of thalamic neurons necessary to equal the total thalamic charge evoked in response to visual stimulation (y axis). Each data point is the average ± s.d.

  9. Extended Data Table 1 Quantification of static grating-evoked thalamic excitation
  10. Extended Data Table 2 Quantification of thalamocortical monosynaptic connections

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