A Modality-Specific Feedforward Component of Choice-Related Activity in MT

Abstract

The activity of individual sensory neurons can be predictive of an animal's choices. These decision signals arise from network properties dependent on feedforward and feedback inputs; however, the relative contributions of these inputs are poorly understood. We determined the role of feedforward pathways to decision signals in MT by recording neuronal activity while monkeys performed motion and depth tasks. During each session, we reversibly inactivated V2 and V3, which provide feedforward input to MT that conveys more information about depth than motion. We thus monitored the choice-related activity of the same neuron both before and during V2/V3 inactivation. During inactivation, MT neurons became less predictive of decisions for the depth task but not the motion task, indicating that a feedforward pathway that gives rise to tuning preferences also contributes to decision signals. We show that our data are consistent with V2/V3 input conferring structured noise correlations onto the MT population.

Figure 1. Experimental Design

(A) Schematic of the two major cortical inputs to MT. The cube icon indicates data related to depth and the arrows icon indicates data related to motion throughout this paper. (B) Behavioral task design. Each panel depicts a phase of the trial. The gray region indicates the inactivation “scotoma”, the dotted circle indicates the edges of a neuron’s receptive field, and the solid circle depicts the extent of the visual stimulus. (C) Experimental timeline. “RF” indicates receptive field mapping and “tasks” refers to the epoch in which the animal performed the motion and depth tasks.

Figure 2. Behavioral Performance

(A) Sample behavioral performance during the depth and motion task is shown on the left and right, respectively. Psychometric thresholds are shown in the upper right corner. (B) Comparison of pre-cool and cool psychometric thresholds during the depth (left) and motion (right) tasks, color-coded by monkey. Error bars are 95% confidence intervals on the threshold from bootstraps on the function fit, shown for every fifth data point. Note log-log axes. (C) Psychometric thresholds for performance in the visual hemifield ipsilateral to the cryoloops, where we did not expect effects of cooling. Same conventions as in (B). (D) Paired comparison of the Effect Indices (EI; see text) in the scotoma. (E) Histogram of changes in EI in the ipsilateral visual field. See also Table S1, Figure S1.

Figure 3. Neuronal Effects of Inactivation

(A) Each neuron’s neurometric performance (NP) during the depth (left) and motion (right) tasks, color-coded by monkey. Error bars are the SEM, shown for every fifth data point. (B) Histogram of NP effect indices (EI) combined across monkeys (left) and for each monkey individually (right two panels). Depth task effects shown in black and motion task effects shown in grey. Triangles indicate medians. (C) Tuning discrimination indices (DI) for binocular disparity (left) and direction (right) tuning. Same conventions as in (A). (D) Paired comparison of the effect of cooling between the direction and binocular disparity tuning DI. Same marker conventions as in (A). See also Table S2, Figure S2.

Figure 4. Detect Probability

(A) Time course of detect probability (DP) ±SEM, aligned to signal onset at t = 0. The gray region indicates the time window used to calculate the population DP reported in the text, the individual neurons’ DP in (C) and the DP in (D). Only neurons that contributed data to both tasks and conditions (pre-cool and cool) are included in this analysis. (B) DP time course shown separately for each monkey. (C) DP for each of the 75 neurons, color-coded by monkey. Filled symbols indicate DPs that were statistically significantly different between pre-cool and cool conditions. (D) Grand DP ±SEM computed in the time window indicated by the gray region in (A). Note only the 9 neurons for which we had data for all three conditions were included here. See also Table S3, Figure S3.

Figure 5. Trial-to-Trial Variability

(A) Average firing rate (top) and Fano factor (FF) ± SEM (bottom) aligned separately to onset of the visual stimulus and the time of the change during the depth (left) and motion (tasks). (B) Scatter plot of the variance and mean of the spike count collapsed across tasks and time. Each data point corresponds to a unique stimulus presented to one neuron and there are 2-14 data points per neuron.

Figure 6. Feedforward model framework

Correlated inputs from V1 and V2 determine the output (response) correlations in MT. Cooling reduces the firing rate of V2 and MT neurons and thereby the relative weighting of correlated V1 and V2 inputs. Since our data suggest no change in read-out weights with cooling, the observed changes in DP must be due to the reduction in correlated V2 input. Quantities affected by cooling are indicated in blue. See also Figure S4.

Figure 7. Simulation of the effect of V2 inactivation

(A) Correlation structure of inputs from V1 to MT. MT neurons sorted by preferred motion direction. (B) Correlation structure of inputs from V2 to MT. Since MT neurons are sorted by preferred direction, and since we assumed no systematic relationship between direction and disparity tuning in MT, the limited-range correlation structure with respect to disparity (analogous to the one with respect to motion direction in A) is shuffled in this view. (C) Resulting input correlation structure to MT in the control condition. During cooling, the influence of V2 decreases, and with complete cooling, the total input correlations to MT are identical to those provided by V1 shown in A. (D) Read-out weights depend on the preferred stimulus in each of the motion and depth tasks (different for each task) such that most informative neurons are preferentially read out. Qualitatively identical results are obtained in panels E and F for random weights, and optimal weights (see also Figure S5). (E) Motion DP change due to cooling. Blue: simulation, red: analytical approximation. (F) Depth DP change due to cooling. For simulation parameters see Experimental Procedures. For DP calculations we assume the total input correlations to MT to be equal to the response correlations since the effect of the decrease in firing rate on output correlations is small in our data.