Coarse-to-Fine Changes of Receptive Fields in Lateral Geniculate Nucleus Have a Transient and a Sustained Component That Depend on Distinct Mechanisms

Visual processing in the brain seems to provide fast but coarse information before information about fine details. Such dynamics occur also in single neurons at several levels of the visual system. In the dorsal lateral geniculate nucleus (LGN), neurons have a receptive field (RF) with antagonistic center-surround organization, and temporal changes in center-surround organization are generally assumed to be due to a time-lag of the surround activity relative to center activity. Spatial resolution may be measured as the inverse of center size, and in LGN neurons RF-center width changes during static stimulation with durations in the range of normal fixation periods (250–500 ms) between saccadic eye-movements. The RF-center is initially large, but rapidly shrinks during the first ∼100 ms to a rather sustained size. We studied such dynamics in anesthetized cats during presentation (250 ms) of static spots centered on the RF with main focus on the transition from the first transient and highly dynamic component to the second more sustained component. The results suggest that the two components depend on different neuronal mechanisms that operate in parallel and with partial temporal overlap rather than on a continuously changing center-surround balance. Results from mathematical modeling further supported this conclusion. We found that existing models for the spatiotemporal RF of LGN neurons failed to account for our experimental results. The modeling demonstrated that a new model, in which the response is given by a sum of an early transient component and a partially overlapping sustained component, adequately accounts for our experimental data

RF dynamics for an on-center X-neuron.

<p>Similar plots as for the Y-neuron in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024523#pone-0024523-g001" target="_blank">Fig. 1</a>. Number of presentations of each spot 125.</p

Predicted ‘one-dimensional impulse response’, i.e., impulse response for long and thin bars, for the transient-sustained (TS) model for example on-center Y and X neurons in <b>Figs. 1</b> and <b>2</b>.

<p>This impulse-response function of the form given in Eq. (16), but with the spatial functions <i>g_m</i>(<i>r</i>) replaced by the function <i>g_bar,m</i>(<i>x</i>) listed in Eq. (21). The test bar in the example has a length <i>L</i> = 10 deg. All model parameters correspond to the fit depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024523#pone-0024523-g010" target="_blank">Fig. 10</a> and are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024523#pone-0024523-t001" target="_blank">Table 1</a>. <b>A.</b> Predicted receptive-field function for full TS-model for Y neuron. <b>B.</b> Contribution from transient part (<i>f_t1</i>(<i>t</i>) <i>g_bar,t1</i>(<i>x</i>)+<i>f_t2</i>(<i>t</i>) <i>g_bar,t2</i>(<i>x</i>)). <b>C.</b> Contribution from sustained part (<i>f_s</i>(<i>t</i>) <i>g_bar,s</i>(<i>x</i>)). <b>D</b>–<b>F. </b><b>S</b>ame as (A)–(C) for the X-neuron. Notice that (i) the color scale in C and F differ from the scale in the other corresponding color maps and (ii) that the negative response for the Y-neuron has been truncated at the numerical value −100 spikes/s/deg in panels A and B.</p

Principal components analysis (PCA) for example on-center Y and X neurons in <b>Figs. 1</b> and <b>2</b>.

<p><b><i>A</i></b>, <b><i>B</i></b>, 1^st and 2^nd principal components, respectively, for the Y-neuron response data, i.e., contributions from terms with <i>n = 1</i> and <i>n = 2</i> in Eq. (3). <b><i>C</i></b>, Sum of contributions from two first principal components (and background activity) for Y neuron. <b><i>D</i></b>, Deviation between experimental results for Y neuron and PCA results in (<i>C</i>). Error <i>ε</i> (cf. Eq. 1) is 0.036. <b><i>E–H</i></b>, Same as (<i>A</i>)–(<i>D</i>) for the X-neuron response data. The deviation between experimental results and PCA results (<i>G</i>) corresponds to an error <i>ε</i> = 0.015.</p

Principal components analysis (PCA) of early part of response data (t<100 ms) for example on-center Y and X neurons in <b>Figs. 1</b> and <b>2</b>.

<p><b>A</b>,<b>B</b>, 1^st and 2^nd principal components, respectively, for the Y-neuron response data, i.e., contributions from terms with <i>n = 1</i> and <i>n = 2</i> in Eq. (3). <b>C</b>, Sum of contributions from two first principal components (and background activity) for Y neuron. <b>D</b>, Deviation between experimental results for Y neuron and PCA results in (C). Error <i>ε</i> (cf. Eq. 1) is 0.044. <b>E</b>, Fitted transient temporal function <i>F_t1</i>(<i>t</i>) (Eq. 11, blue dashed line) to 1^st temporal PCA component (blue solid line), and fitted transient temporal function <i>F_t2</i>(<i>t</i>) (Eq. 12, green dashed line) to 2^nd temporal PCA component (green solid line) for early part ( t<97.5 ms) of Y-neuron data. <b>F</b>, Blue dashed line: Fitted DOG spatial functions (Eq. 10) to 1^st spatial PCA component of early part (t<97.5 ms) of Y-neuron data (blue solid line). Green dashed line: Corresponding DOG function fit to the 2^nd spatial PCA component (green solid line). The best fit of a DOG function (red dashed line) to the 1^st spatial PCA component of the <i>last</i> part of the Y-neuron data is also shown (red line). <b>G–L</b>, Same as (A)–(F) for X-neuron response data. The deviation between experimental results and PCA results (I) corresponds to an error <i>ε</i> = 0.021.</p