Two different and stable population activity patterns in the LPFC were observed in Delay 1 and Delay 2 (Figure 2a), which implied that a downstream region would need to use different decoders in the two periods to extract the working memory information (neural codes supporting the discrimination of different intended items), and would need to know which of them to use in the appropriate delay period. Alternatively, the difference observed between Delay 1 and Delay 2 activity could be explained by a superposition of different types of information in independent subspaces, such that each downstream region can use the same decoder to extract a specific type of information invariantly across time, even if the mixing of different types of information is dynamic across time. We have previously shown that a time-invariant (henceforth stable) working memory subspace can be identified in the LPFC (Parthasarathy, 2019). However, significant information about the target was present outside of this space (null space decoding performance of 35.7 ± 1.7% in Delay 1, and 31.6 ± 1.5% in Delay 2) (Parthasarathy, 2019) suggesting the existence of a non-trivial additional subspace that contains target information. The incorporation of the new information from the additional subspace into the neuronal population, alongside the existing information from the working memory subspace, would have then resulted in code morphing in the full space (illustrated in Figure 2b). One possible source of the new information could be motor preparation activity. The stable population activity in Delay 2 suggested that the animals could have initiated preparatory activity right after distractor offset, as the distractor was the last signal that reliably predicted the Go cue. In order to assess this possibility, we looked for a subspace decomposition that could maximally differentiate the neural codes for working memory and motor preparation. Standard methods for such decompositions are regression (Mante et al., 2013; Brody et al., 2003) (resulting in a one-dimensional component for each task-dependent variable) and, more interestingly, Demixed Principal Component Analysis (dPCA) (Brendel et al., 2011; Kobak et al., 2016), which selectively isolates and constructs a subspace for one task-dependent variable at a time by averaging out all the other task-dependent variables. However, these methods are not suitable for our data, because we always had the same target location label for working memory and motor preparation in each trial. As a result, for regression, the working memory and motor preparation variables will not have different coefficients; for dPCA, we cannot differentially represent the neural activity by averaging trials according to working memory or motor preparation locations to find different subspaces. Instead, we developed a novel method to identify the two subspaces even given that working memory and motor preparation always had the same target labels in each trial by regarding the trial-averaged and time-averaged Delay 1 and Delay 2 activity (each had a size 226 × 7, where 226 was the number of neurons, and seven was the number of target locations) as a mixture of working memory and motor preparation activity, and assumed that working memory and motor preparation activity themselves to be minimally dependent on each other. Our objective was then to find, through an optimization technique, the best unmixing coefficients to apply to Delay 1 and Delay 2 activity that could recover the working memory and motor preparation activity with the lowest mutual information possible between them (see Materials and methods). The original Delay 1 and Delay 2 activity exhibited 0.33 bits of mutual information. Using our method, we found two unmixed elements (representations of unmixed population activity that were each of size 226 × 7) from D1 and D2 activity with a minimum mutual information of 0.08 bits (Figure 2—figure supplement 1). The two elements we identified consisted of seven vectors in the 226-dimensional space, and according to the unmixing coefficients we identified, the magnitude of one element (Element 1) in Delay 2 was 65% of that in Delay 1, and the magnitude of the other element (Element 2) in Delay 1 was 12% of that in Delay 2. The orthonormal bases of the two elements defined two subspaces (Subspace 1 and Subspace 2). The temporal dynamics of the full-space population activity projected into these subspaces showed that the magnitude of activity in Subspace 1 increased early after target presentation and was maintained until the saccade cue, while the magnitude of activity in Subspace 2 increased after distractor presentation and stayed relatively high even after the Go cue (Figure 2c, single-session results are shown in Figure 2—figure supplement 2). Next, we used the decoding performance of a linear decoder (LDA) as a proxy of target information and evaluated target information in each subspace. We trained an LDA decoder at each time point of the trial, and tested the decoder against all other time points across the trial to evaluate the temporal generalization of the population activity (cross-temporal decoding, see Materials and methods). Cross-temporal decoding after projecting the full-space neural activity into Subspace 1 showed that information emerged right after target presentation, and although the information was higher in Delay 1 (60.5 ± 1.3%), it was present throughout the whole trial, even during the distractor period (Figure 2d). This was qualitatively consistent with our hypothesis, aside from the decrease in information in Delay 2 (39.9 ± 1.1%). Cross-temporal decoding of full-space neural activity projected into Subspace 2 showed that information emerged after distractor presentation (42.6 ± 1.1%), and was stable throughout Delay 2 (Figure 2e). In Delay 1, Subspace 1 and Subspace 2 accounted for 14.6% and 10.3% of the variance in the full space; in Delay 2, Subspace 1 and Subspace 2 accounted for 5.8% and 8.1% of the variance in the full space. Full-space data in the two subspaces had an effective dimensionality of 6 dimensions each – after projecting single-trial full-space data into the subspaces, we performed a PCA on the projected data, and the first six out of the seven principal components cumulatively accounted for more than 95% of the variance within each subspace (Figure 2—figure supplement 3). This indicated that the true dimensionality of the neural code could be smaller than the number of discrete target locations imposed by the experiment. In addition, as the number of discrete target locations increases in the experiment (for example, 24 target locations), we expect the effective dimensionality of data in the subspaces will asymptote to the true dimensionality of the neural codes supporting the cognitive processes.

Figure 2f shows single-trial projections of two different target locations (purple and green locations shown in the top-left corner) onto the top three principal components (PCs). These projections were then re-projected into Subspace 1 (red plane) and Subspace 2 (blue plane). The low-dimensional visualizations are merely used to provide intuitions underlying the cross-temporal decoding results, which were all obtained using high-dimensional data (see Materials and methods). Consistent with our hypothesis, Delay 1 and Delay 2 projections into Subspace 1 clustered according to target location, although they overlapped less than we expected (we will revisit this deviation from our expectation later on). However, the separation between the projected points was small enough that target location information could be decoded in both delays, regardless of whether the classifier was trained using Delay 1 or Delay 2 activity (Figure 2d).

On the other hand, projections into Subspace 2 behaved differently, such that Delay 1 projections for multiple target locations overlapped, whereas Delay 2 projections remained separated. This explained why in Subspace 2, target location information could not be decoded in Delay 1, but could be decoded in Delay 2 (Figure 2e). Projections into Subspace 1 and Subspace 2 for all target locations confirmed that these observations generalized to the rest of the locations (Figure 2—figure supplement 4, which also illustrates the reason for the difference in performance in the two off-diagonal quadrants in Figure 2d).

Since Subspace 1 contained target information throughout the trial, and working memory of the target location was presumably required throughout the trial, we hypothesized that Subspace 1 corresponded to a working memory subspace. We previously showed that the LPFC contained a working memory subspace that encoded stable working memory information (Parthasarathy, 2019). In order to assess whether Subspace 1 corresponded to the working memory subspace previously described (Parthasarathy, 2019), we calculated the principal angles between these subspaces, as a measure of similarity (see Materials and methods). We found that Subspace 1 was significantly closer than chance to the working memory subspace, while Subspace 2 was not (Figure 3—figure supplement 1). This result supported the interpretation that Subspace 1 corresponded to a working memory subspace. Thus, henceforth, we will refer to Subspace 1 as the ‘working memory subspace’ and Element 1 as the unmixed working memory element.

Since Subspace 2 contained target information only after the distractor disappeared, and motor preparation presumably began after the last sensory cue that reliably predicted the timing of the Go cue (i.e. the offset of the distractor), we hypothesized that Subspace 2 corresponded to a motor preparation subspace. Activity between the Go cue and the saccade onset contained information about saccade execution (45% of LPFC neurons we recorded were selective in the period between the Go cue and saccade onset, assessed using a one-way ANOVA, p < 0.05). In order to test whether Subspace 2 corresponded to a motor preparation subspace, we compared the original unmixed elements using Delay 1 and Delay 2 activity with a new pair of unmixed elements using Delay 1 and pre-saccadic period activity (150 ms to 0 ms prior to saccade). If Subspace 2 corresponded to a motor preparation subspace, we should observe similarities between the second element in both pairs of unmixed elements, that is the unmixed motor preparation activity and the unmixed pre-saccade activity (see Materials and methods). In the new pair of unmixed elements, we obtained two elements with relative vector magnitudes similar to those found in the first pair of unmixed elements (for Element 1’, the vector magnitude in the pre-saccade period was 70% of that in Delay 1, while for Element 2’, the vector magnitude in Delay 1 was 0% of that in the pre-saccade period, Figure 3—figure supplement 2). We found that Element 1 and Element 1’ were significantly correlated (Figure 3a left, Pearson correlation r > 0.99, p < 0.01). Importantly, Element 2 and Element 2’ were also significantly correlated (Figure 3a right, Pearson correlation r = 0.62, p < 0.01). This result supported our hypothesis that Subspace 2 corresponded to a motor preparation subspace.

Figure 3 with 3 supplements see all

Download asset Open asset

In an additional test of the hypothesis that Subspace 2 corresponded to a motor preparation subspace, we examined the relationship between the unmixed motor preparation activity and the unmixed pre-saccade activity at the level of single cells. First, we identified cells with spatial tuning in both Delay 2 and the pre-saccade period (73 cells, two one-way ANOVAs, both p < 0.05). Then, for each cell, we measured the correlation between the unmixed motor preparation activity and the unmixed pre-saccade activity across different target locations. We found that 47% of these neurons showed significant correlation (Pearson correlation, p < 0.05), which exceeded the number expected by chance (Figure 3b, left bar, p < 0.001, g = 10.82). As a control, we carried out the same analysis between the unmixed working memory activity and the unmixed pre-saccade activity, and found no evidence of a higher number of correlated cells than expected by chance (Figure 3b, right bar, p > 0.19, g = 1.51). Examples of neurons with significant correlation are shown in Figure 3c. This result provided additional support to our hypothesis that Subspace 2 corresponded to a motor preparation subspace. Thus, henceforth, we will refer to Subspace 2 as the ‘motor preparation subspace’. Alongside the working memory and motor preparation activity for target locations, there could also be activity representing distractor locations in Delay 2. By grouping trials according to distractor labels, we indeed found significant distractor information in the full space (Figure 3—figure supplement 3). However, the distractor activity in Delay 2 was not related to the Element 2 or the motor preparation subspace we identified, because the distractor activity and the motor preparation activity were obtained from data grouped by different trial labels (target and distractor labels were uncorrelated). Very little distractor information (17.9 ± 0.7%) was successfully decoded in the motor preparation subspace (Figure 3—figure supplement 3).

The existence of two minimally dependent subspaces could be mediated by one of two possible mechanisms: (1) two distinct subpopulations of neurons with exclusive working memory or motor preparation selectivity within the LPFC, or (2) the same population of LPFC neurons with mixed selectivity to both working memory and motor preparation. In order to distinguish between these two possible mechanisms, we projected the unit vector representing each neuron in the full space into the working memory and motor preparation subspaces, and quantified the magnitude of the two projections for each neuron (i.e. loading weight, Figure 4a). A clustering of the loading weights along the x- and y-axes would support the first mechanism, and if not this case, a non-significant or positive correlation would support the second mechanism. In order to test if the points clustered along the x- and y-axes, we computed the ratio of points near the x- or y-axes (above 67.5° line or below 22.5° line) for 1,000 bootstraps, with a random 10% data exclusion in each bootstrap. We found that there were significantly more points away from the x- or y-axes (88% to 90%, corresponding to the 5^th and 95^th percentile of 1,000 bootstraps), which rejected the first mechanism. In addition, to our surprise, we found a significant positive correlation between the loading weights in each subspace (r = 0.68, which was significantly higher than the 95^th percentile of 1,000 shuffles, where in each shuffle we randomly permuted the population’s loading weights for both subspaces), which not only supported the second mechanism, but further suggested that neurons with stronger contribution to the working memory subspace would also have a stronger contribution to the motor preparation subspace. As a measure of the relative contribution to each subspace, we calculated the ratio between the loading weights for each cell, and analyzed their distribution (Figure 4b). We found that only 14 (6%) of the neurons had ‘exclusive’ loading for the working memory (red) or motor preparation (blue) subspaces. However, these cells were not necessary to identify the subspaces (Figure 4—figure supplement 1).

Figure 4 with 4 supplements see all

Download asset Open asset

In order to understand how a single population of neurons with mixed selectivity could have contributed minimally dependent information to the two subspaces, we created a simple illustration (Figure 4c). Working memory and motor preparation information were read out by separate readout neurons with different connection weights to Neurons 1 and 2 that reflected the loading weights of each subspace. In isolation, the activity of Neuron 1 would be ambiguous for both readout neurons, as an increase of activity in Delay 2 could be interpreted as a new memory at a different spatial location, or as the same memory as in Delay 1, but with superimposed motor preparation activity. In order to disambiguate the meaning of a change in the activity of one neuron, it would be necessary to interpret that change in the context of changes in the activity of the rest of the neuronal population (i.e. in this example, Neuron 2). In the illustration, a superimposed increase of activity in Neurons 1 and 2 signals a change in memory (i.e. only the readout activity in the working memory subspace changed), whereas the same increase in Neuron 1, but with a superimposed decrease of activity in Neuron 2, signals that the memory has not changed, but that a motor preparation plan has emerged in Delay 2 (i.e. only the readout activity in the motor preparation subspace changed). This concept can be extended to the 212 neurons with mixed selectivity to understand how the coordinated activity between those neurons can contribute minimally dependent information to the working memory and motor preparation subspaces through different loading weights that we found in the LPFC (low-dimensional visualizations of neural data provided in Figure 2f and Figure 2—figure supplement 4).

Since one population of neurons with mixed selectivity contributed to both the working memory and motor preparation subspaces, it was possible that information in one of the subspaces interfered with information in the other, and vice versa. We checked if the two subspaces were orthogonal to each other by comparing their principal angles with those between two random subspaces of the same dimension (Figure 5a). All the principal angles between the working memory and motor preparation subspaces were significantly smaller than chance, indicating non-orthogonality between the two subspaces and the likelihood of interference of information between them. One way to assess the interference between the information in both subspaces is to ask whether the emergence of motor preparation activity in Delay 2 (added on top of working memory activity) changes the amount of readable information in the working memory subspace, compared to the situation where there is only working memory activity in Delay 2. In order to quantify this interference of information, we compared the decoding performance of an LDA classifier trained and tested on the unmixed single-trial working memory activity (see Materials and methods) projected into the working memory subspace (Figure 5a: proj_MSub(M)), with decoding performance of a classifier trained and tested on single-trial working memory activity plus motor preparation activity (i.e. Delay 2 activity) projected into the working memory subspace (Figure 5a: proj_MSub(M+P)). A similar analysis was carried out in the motor preparation subspace (Figure 5b: proj_PSub(P) and proj_PSub(M+P)). We found no evidence of a drop in performance between proj_MSub(M) and proj_MSub(M+P) (p > 0.73, g = 0.61), and between proj_PSub(P) and proj_PSub(M+P) (p > 0.22, g = 2.63), suggesting a lack of interference between these subspaces.

Figure 5 with 3 supplements see all

Download asset Open asset

As we used LDA’s decoding performance as a proxy of target information, the lack of interference between two non-orthogonal subspaces indicated that the shift of clusters in the state space caused by superimposed activity were not large enough to cross the classification boundaries, and thus did not affect the classification performance. So we performed a more sensitive state-space analysis on Delay 2 activity to assess whether the working memory and motor preparation subspaces interfered with each other. We quantified interference by projecting the unmixed single-trial activity into the two subspaces, and calculated the average distance between clusters of points corresponding to different target locations (inter-cluster distance). The inter-cluster distance was then normalized by the average intra-cluster distance for all clusters, which was a measure of trial-by-trial variability in the population activity (see Materials and methods). This inter-to-intra cluster distance ratio was compared between projections of unmixed single-trial working memory activity into the working memory subspace (Figure 5d: proj_MSub(M)), and projections of single-trial working memory plus motor preparation activity (i.e. Delay 2 activity) into the working memory subspace (Figure 5d: proj_MSub(M+P)). A similar analysis was carried out in the motor preparation subspace (Figure 5e). We found a small decrease (7.1%) of the inter-to-intra cluster distance ratio when both working memory and motor preparation activity were projected into the subspaces, suggesting the existence of a small, but significant interference between both subspaces (p < 0.001, g = 4.81 between proj_MSub(M) and proj_MSub(M+P), p < 0.001, g = 6.63 between proj_PSub(P) and proj_PSub(M+P)).

In a subset of trials, the animals maintained fixation until the Go cue, but failed to report the correct target location with a saccade. These failures could be due to the animals reporting other locations, including the location of the distractor, or simply saccading to a completely different location, such as the edge of the monitor. Classifiers trained on unmixed single-trial working memory activity of correct trials projected into the working memory subspace, proj_MSub(M), were tested on unmixed single-trial working memory activity of error trials (see Materials and methods), which was also projected into the working memory subspace, proj_MSub(M_E). Decoding performance was significantly reduced in error trials (Figure 5a,) compared to correct trials (Figure 5a, p < 0.001, g = 13.9 between proj_MSub(M) and proj_MSub(M_E)), suggesting that failures in memory encoding occurred during error trials. A similar analysis on the motor preparation subspace yielded equivalent results (Figure 5b, p < 0.001, g = 13.8 between proj_PSub(P) and proj_PSub(P_E)), which were consistent with the fact that in error trials, saccades were made to different locations than in correct trials. These results suggested that the subspaces we found could have been used by the animals to perform the task.

An unexpected observation in our results was that decoding performance in the working memory subspace decreased in Delay 2 compared to Delay 1 (Figure 2d). This decrease coincided with an increase of decoding performance in the motor preparation subspace (Figure 2e). The reduction of working memory decoding performance was not expected in the schematic diagram of the subspace dynamics (Figure 2b), but was captured by the state space visualization of real neural data (Figure 2f, inter-cluster distance in Subspace 1 reduced in Delay 2). A more rigorous state space analysis revealed that the decrease of working memory decoding performance was due to a decrease in the inter-to-intra cluster distance ratio of working memory activity in Delay 2, and the increase of motor preparation decoding performance was due to an increase in the inter-to-intra cluster distance ratio of motor preparation activity in Delay 2 (Figure 2—figure supplement 5). In addition, we noticed that the mean population firing rate did not change between Delay 1, Delay 2 and pre-target fixation periods (Figure 2—figure supplement 6). This observation was consistent with a population normalization mechanism to maintain the mean population firing rate at a constant level in the LPFC (Ruff and Cohen, 2017; Duong et al., 2019). In order to assess whether a normalization mechanism was responsible for the decrease of working memory information in Delay 2, we built artificial neural network models with and without population normalization, and compared their behavior with the LPFC data.

Bump attractor models have been shown to replicate several properties of LPFC activity, including code-morphing in the full space, the existence of a stable subspace with stable working memory information, and non-linear mixed selectivity of individual neurons (Parthasarathy, 2019; Compte et al., 2000; Wimmer et al., 2014). Here, we created a model that incorporated subsets of neurons that represented information in the working memory and motor preparation subspaces (Figure 4—figure supplement 2), and looked at the effect of adding divisive normalization to keep the mean population firing rate constant. We constrained the attractor model to utilize neurons with mixed selectivity by matching the selectivity properties to those found in the LPFC data (Figure 4—figure supplement 3). We also used the same unmixing method to identify working memory and motor preparation subspaces from the activity in the attractor model. We found that only the attractor model with divisive normalization qualitatively replicated all the features of the neural data (Figure 4—figure supplement 2). We also explored an alternative model that supported the implementation of subspaces – a linear subspace model (Murray et al., 2017). Similarly, only the linear subspace model with divisive normalization replicated all the main findings in the neural data (Figure 4—figure supplement 4). The results of the model simulations supported the idea that divisive normalization was needed to faithfully replicate the properties of neural data, and suggested that such a mechanism constituted part of the function of the LPFC. The similarity between the results obtained from two different categories of models – the bump attractor model and the linear subspace model, also indicated the conceptual convergence between the two models in replicating the sustained-activity aspect of the LPFC activity.

We used two male adult macaques (Macaca fascicularis), Animal A (age 4) and Animal B (age 6), in the experiments. All animal procedures were approved by, and conducted in compliance with the standards of the Agri-Food and Veterinary Authority of Singapore and the Singapore Health Services Institutional Animal Care and Use Committee (SingHealth IACUC #2012/SHS/757). The procedures also conformed to the recommendations described in Guidelines for the Care and Use of Mammals in Neuroscience and Behavioral Research (Van Sluyters and Obernier, 2003). Each animal was implanted first with a titanium head-post (Crist Instruments, MD) before arrays of intracortical microelectrodes (MicroProbes, MD) were implanted in multiple regions of the left frontal cortex. In Animal A, we implanted six arrays of 16 electrodes and one array of 32 electrodes in the LPFC, and two arrays of 32 electrodes in the FEF, for a total of 192 electrodes. In Animal B, we implanted one array of 16 electrodes and two arrays of 32 electrodes in the LPFC, and two arrays of 16 electrodes in the FEF, for a total of 112 electrodes. The arrays consisted of platinum-iridium wires with either 200 or 400 µm separation, 1–5.5 mm of length, 0.5 MΩ of impedance, and arranged in 4 × 4 or 8 × 4 grids. Surgical procedures followed the following steps. 24 hr prior to the surgery, the animals received a dose of Dexamethasone to control inflammation during and after the surgery. They also received antibiotics (amoxicillin 7–15 mg/kg and Enrofloxacin 5 mg/kg) for 8 days, starting 24 hr before the surgery. During surgery, the scalp was incised, and the muscles retracted to expose the skull. A craniotomy was performed (~2×2 cm). The dura mater was cut and removed from the craniotomy site. Arrays of electrodes were slowly lowered into the brain using a stereotaxic manipulator. Once all the arrays were secured in place, the arrays’ connectors were secured on top of the skull using bone cement. A head-holder was also secured using bone cement. The piece of bone removed during the craniotomy was repositioned to its original location and secured in place using metal plates. The skin was sutured on top of the craniotomy site, and stitched in place, avoiding any tension to ensure good healing of the wound. All surgeries were conducted using aseptic techniques under general anesthesia (isofluorane 1–1.5% for maintenance). The depth of anesthesia was assessed by monitoring the heart rate and movement of the animal, and the level of anesthesia was adjusted as necessary. Analgesics were provided during post-surgical recovery, including a Fentanyl patch (12.5 mg/2.5 kg 24 hr prior to surgery, and removed 48 hr after surgery), and Meloxicam (0.2–0.3 mg/kg after the removal of the Fentanyl patch). Animals were not euthanized at the end of the study.

Neural signals were initially acquired using a 128-channel and a 256-channel Plexon OmniPlex system (Plexon Inc, TX) with a sampling rate of 40 kHz. The wide-band signals were band-pass filtered between 250 and 10,000 Hz. Following that, spikes were detected using an automated Hidden Markov-Model-based algorithm for each channel (Herbst et al., 2008). The eye positions were obtained using an infrared-based eye-tracking device from SR Research Ltd. (Eyelink 1000 Plus). The behavioral task was designed on a standalone PC (stimulus PC) using the Psychophysics Toolbox (Brainard, 1997) in MATLAB (Mathworks, MA). In order to align the neural and behavioral activity (trial epochs and eye data) for data analysis, we generated strobe words denoting trial epochs and performance (rewarded or failure) during the trial. These strobe words were generated on the stimulus PC and were sent to the Plexon and Eyelink computers using the parallel port.

For arrays positioned in the prearcuate region (FEF), we used standard electrical microstimulation to confirm that saccades could be elicited with low currents. These electrodes had a depth of 5.5 mm inside the sulcus and tapered to 1 mm away from the sulcus. We conducted these microstimulation experiments after we finished our recording experiments. During the microstimulation experiment, each electrode implanted in the FEF was tested for its ability to evoke fixed-vector saccadic eye movements with stimulation at currents of 50 μA. Electrical microstimulation consisted of a 200 ms train of biphasic current pulses (1 ms, 300 Hz) with no interphase delays, delivered with a Plexon Stimulator (Plexon Inc, TX). We mapped the saccade vector elicited via microstimulation at each electrode to verify that the electrodes were implanted in the FEF. Sites at which stimulation of 50 μA or less elicited eye movements at least 50% of the time, plus regions within 2–3 mm of these locations, were considered to be in the FEF.

Each trial started with a mandatory period (500 ms) where the animal fixated on a white circle at the center of the screen. While continuing to fixate, the animal was presented with a target (a red square) for 300 ms at any one of eight locations in a 3 × 3 grid. The center square of the 3 × 3 grid contained the fixation spot and was not used. The presentation of the target was followed by a delay of 1,000 ms, during which the animal was expected to maintain fixation on the white circle at the center. At the end of this delay, a distractor (a green square) was presented for 300 ms at any one of the seven locations (other than where the target was presented). This was again followed by a delay of 1000 ms. The animal was then given a cue (the disappearance of the fixation spot) at the end of the second delay to make a saccade toward the target location that was presented earlier in the trial. Saccades to the target location within a latency of 150 ms and continued fixation at the saccade location for 200 ms was considered a correct trial. An illustration of the task is shown in Figure 1a. One of the animals was presented with only seven of the eight target locations because of a behavior bias in the animal.

A decoder based on linear discriminant analysis (LDA) was built using the classify function in MATLAB to predict the location of the target. We trained a decoder for each time point in the trial, and tested the decoder with all other time points throughout the trial. We pooled the activity across recording sessions to create a pseudo-population of 226 neurons. In the pseudo-population, for each pseudo-trial with target location T, we randomly picked one trial from each neuron with target location T, and stacked the activity from all neurons together as if they were simultaneously recorded. We constructed 1,750 pseudo-trials (250 for each target location) as the training set, and 1,750 pseudo-trials as the testing set. The training set and testing set were sampled from non-overlapping sets of trials from each neuron. When performing cross-temporal decoding in the full space (226 dimensions, Figure 1b), we denoised the training and testing data using principal components analysis (PCA) at every time point by reconstructing the data with the top n principal components that explained at least 95% of the variance. When performing cross-temporal decoding in the subspace (seven dimensions, Figure 1e,f), the PCA projection matrix described in the previous step was replaced by the matrix specifying the desired subspace (working memory or motor preparation subspace), and the resulting data in the subspace would thus be seven dimensional.

The subspaces where identified using a pseudo-population of 226 neurons. For each trial condition (which was one of seven possible target locations), we trial-averaged and time-averaged the neural activity in Delay 1 (800 to 1300 ms from target onset) and Delay 2 (2000 to 2500 ms from target onset) for each neuron to obtain two activity matrices of 226 x 7. We then normalized the two activity matrices to the mean of the baseline by subtracting neural activity in the fixation period (300 ms before target onset), and obtained activity matrices ${\displaystyle \overline{D1}}$ and ${\displaystyle \overline{D\text{2}}}$ of size 226 x 7, where each column represented the change in population activity under one condition. After flattening ${\displaystyle \overline{D\text{1}}}$ and ${\displaystyle \overline{D\text{2}}}$ activity into 1-D arrays (each of size 1 x 1,582, denoted as ${\displaystyle \overrightarrow{D1}}$ and ${\displaystyle \overrightarrow{D2}}$), we found high mutual information (0.33 bits) between ${\displaystyle \overrightarrow{D1}}$ and ${\displaystyle \overrightarrow{D2}}$ (Figure 2—figure supplement 1). We hypothesized that the highly correlated ${\displaystyle \overrightarrow{D1}}$ and ${\displaystyle \overrightarrow{D2}}$ resulted from a mixture of working memory and motor preparation activity, while working memory and motor preparation activity themselves were minimally dependent on each other. In matrix expression, we have:

where ${\displaystyle \overrightarrow{M}}$ and ${\displaystyle \overrightarrow{P}}$ are the underlying working memory and motor preparation activity in a flattened shape (each with size 1 x 1,582); $a$ and $b$ are the mixing coefficients of ${\displaystyle \overrightarrow{M}}$ and ${\displaystyle \overrightarrow{P}}$. We used a standard optimization function fmincon in MATLAB to find out a pair of (a, b) that would recover the two activity arrays ${\displaystyle \overrightarrow{M}}$ and ${\displaystyle \overrightarrow{P}}$ with the least mutual information between them. The objective function we used was the mutual information for two discrete distributions:

where $p_{\left(X,Y\right)}$ is the joint probability mass function of X and Y; $p_X$ and $p_Y$ are the marginal probability mass function of X and Y, respectively. To discretize our data, we chose the number of bins according to Sturges’ rule, which is conservative in estimating the number of bins (ensuring there are enough data points in each bin):

where N is the number of total data points from a distribution. Since a scaling operation on the two distributions would not change their mutual information, we could always stipulate the coefficient matrix to have an identity diagonal such that the interpretation of $a$ and $b$ would be intuitive: $a$ indicated the fraction of motor preparation activity in Delay 1 if we assumed the magnitude of motor preparation activity in Delay 2 was one; $b$ indicated the fraction of working memory activity in Delay 2 if we assumed the magnitude of working memory activity in Delay 1 was one. To obtain a robust optimization result, we ran the optimization function 1,000 times with random initialization and examined the results. The values for (a, b) were well centered around the valley of the objective function landscape (a = 0.118 ± 0.04, b = 0.654 ± 0.027, Figure 2—figure supplement 1) and the minimum mutual information obtained was 0.076 bits when we rounded a to 0.12 and b to 0.65 (Figure 2—figure supplement 1). We then reshaped ${\displaystyle \overrightarrow{M}}$ and ${\displaystyle \overrightarrow{P}}$ arrays into matrices with size 226 x 7 (denoted as ${\displaystyle \overline{M}}$ and ${\displaystyle \overline{P}}$, which were also referred to as two unmixed elements), and the orthonormal bases of ${\displaystyle \overline{M}}$ and ${\displaystyle \overline{P}}$ defined the working memory and motor preparation subspaces. The column vectors in ${\displaystyle \overline{M}}$ and ${\displaystyle \overline{P}}$ were regarded as the trial-averaged and time-averaged population activity for working memory and motor preparation, respectively, for the different target locations. The working memory and motor preparation subspaces captured all target information from time-averaged data in Delay 1 and Delay 2. This was because the columns of ${\displaystyle \overline{D\text{1}}}$ and ${\displaystyle \overline{D\text{2}}}$ essentially represented the cluster means of each target location in the state space, so all the cluster means collapsed to zero in the null space of ${\displaystyle \overline{D\text{1}}}$ and ${\displaystyle \overline{D\text{2}}}$ (there was no linearly decodable information in the null space). Hence, the space spanned by ${\displaystyle \overline{D\text{1}}}$ and ${\displaystyle \overline{D\text{2}}}$ captured all target information in Delay 1 and Delay 2. Because ${\displaystyle \overline{M}}$ and ${\displaystyle \overline{P}}$ spanned the same space as ${\displaystyle \overline{D\text{1}}}$ and ${\displaystyle \overline{D\text{2}}}$, the working memory and motor preparation subspaces also captured all target information in Delay 1 and Delay 2.

A similar optimization was performed between activity in Delay 1 and the pre-saccadic period (150 to 0 ms prior to saccade onset):

where ${\displaystyle \overrightarrow{Ds}}$ was the activity in the pre-saccade period. Using the same approach as described above, we obtained ${\displaystyle \overrightarrow{M^{\prime }}}$ and ${\displaystyle \overrightarrow{S}}$ with a minimum mutual information of 0.086 bits when ${\displaystyle \prime }$${\displaystyle a^{\prime }=0.01}$(± 0.014) and ${\displaystyle b^{\prime }=0.706}$ (± 0.033) (Figure 3—figure supplement 2). We reshaped ${\displaystyle \overrightarrow{S}}$ into a matrix with size 226 x 7 (denoted as ${\displaystyle \overline{S}}$), and regarded the column vectors as the trial-averaged and time-averaged pre-saccade activity.

In order to extract the unmixed working memory and motor preparation activity in the full space with single-trial variability, we used:

where ${\displaystyle \overline{M}}$ and ${\displaystyle \overline{P}}$ are the unmixed trial-averaged memory and preparation activity, $D1$ and $D2$ were the single-trial Delay 1 and Delay 2 activity matrices of size 226 x 1,750 (250 random single trials per condition); $M$ and $P$ (number indicates in Delay 1 and Delay 2) were the unmixed single-trial working memory and motor preparation activity in the full space (of size 226 x 1750). Subspace memory and preparation activity were obtained by projecting the unmixed full-space single-trial activity into their respective subspaces derived from ${\displaystyle \overline{M}}$ and ${\displaystyle \overline{P}}$.

In the error trial analysis, single-trial full-space memory and preparation activity in error trials were estimated by:

where ${D1}_E$ and ${D2}_E$ were similar to $D1$ and $D2$, but from error trials. The decoder was trained and validated on the data from correct trials in the subspace and tested on the data from error trials in the same subspace. Although we interpreted the decrease in decoding performance in the two subspaces in error trials to be evidence of the link between these subspaces and the behavior of the animal, an alternative interpretation could be that there was a general increase in noise in the population in error trials (perhaps due to factors like inattention), and this led to a non-specific decrease in information in all subspaces, including the memory and preparation subspaces. In order to rule this possibility out, we quantified the intra-cluster variance in the full space across locations for correct and error trials in both Delays 1 and 2 (refer to Figure 2—figure supplement 5). We found no evidence supporting the fact that the intra-cluster variance in Delay 1 was higher in error trials than in correct trials (p > 0.46, g = 0.85), and found the intra-cluster variance in error trials in Delay 2 to actually be lower than correct trials (p < 0.01, g = 6.6), presumably due to the effects of divisive normalization. These results indicated that the drop in performance in the working memory and motor preparation subspaces in error trials was not due to a non-specific increase in noise, but were more likely due to the fact that the activity in error trials deviated significantly from those in correct trials, resulting in lower information in the two subspaces.

Let X and Y be two subspaces in an N dimensional full space (X has rank $x\le N$, Y has rank $y\le N$), and all the columns of X and Y be the orthonormal bases of each subspace. We perform a Singular Value Decomposition (SVD) on the matrix product of X and Y:

where U, V are orthonormal matrices and S a diagonal matrix. The number of principal angles between X and Y is $\mathrm{m}\mathrm{i}\mathrm{n}(x,y)$, and these angles are obtained by computing the inverse-cosine of the diagonal elements of S and converting radians into degrees. The principal angles in all our analyses were arranged in ascending order such that the leading principal angles are more indicative of the overall alignment of two subspaces. To test if subspace X is significantly closer to subspace Y than chance (e.g. Figure 3—figure supplement 1), we first compute the principal angles between X and Y, and next sample the principal angles between subspace Y and 1,000 random subspaces with the same dimensionality as X to obtain $\mathrm{m}\mathrm{i}\mathrm{n}(x,y)$ number of chance distributions corresponding to the $\mathrm{m}\mathrm{i}\mathrm{n}(x,y)$ number of principal angles between X and Y. X is significantly closer to Y subspaces if the principal angels between X and Y are smaller than the 5^th percentile of their corresponding chance distributions.

To compute the inter-to-intra cluster distance ratio for working memory activity in the full space (Figure 2—figure supplement 5), we bootstrapped 250 unmixed single-trial working memory activity for seven target locations. First, to compute the mean inter-cluster distance, we computed the pairwise distances between all cluster means, and then computed the grand mean of all the pairwise cluster distances. Inter-cluster distance could be intuitively understood as the measure of separation between clusters. Second, to compute the mean intra-cluster distance, we first computed the intra-cluster distance in each cluster (mean pairwise distance among all the data points in one cluster), and then computed the grand mean of the intra-cluster distance among all the clusters. Intra-cluster distance could be intuitively understood as the trial-by-trial variability among all the target conditions. The inter-to-intra cluster distance ratio is then a concept similar to the signal-to-noise ratio of the working memory activity in the state space. We repeated this procedure for 1,000 times to get a distribution of the inter-to-intra cluster distance ratio, and presented the 5^th to 95^th percentile of this distribution in the boxplot in Figure 2—figure supplement 5. The same procedure was repeated for the motor preparation activity.

To compute the inter-to-intra cluster distance ratio in subspaces (Figure 4c,d), we first projected the full-space activity into the desired subspaces, and then repeated the procedure mentioned above.

We considered two bootstrapped distributions to be significantly different if the 95th percentile range of the two distributions did not overlap. We also computed an estimated p-value for this comparison using the following formula (Fi and Garriga, 2010),

where $X$ represents the number of overlapping data points between the two distributions and $N$ represents the number of bootstraps. With this computation, and the $N=1000$ bootstraps we used throughout the paper, two distributions with no overlap will result in a p-value < 0.001, and two distributions with x% of overlap will result in a p-value ~ x/100.

In addition to the estimated p-value, we also computed the effect size of the comparison using a measure known as Hedges’ g, computed using the following formula (Fisher, 1936),

where

${\displaystyle \overline{x}}$ refers to the mean of each distribution, n refers to the length of each distribution, and s refers to the standard deviation of each distribution.

No statistical methods were used to pre-determine sample sizes, but our sample sizes were similar to those reported in previous publications (Murray et al., 2017; Stokes et al., 2013; Jacob and Nieder, 2014). The majority of our analyses made use of nonparametric permutation tests, and as such, did not make assumptions regarding the distribution of the data. No randomization was used during the data collection, except in the selection of the target and distractor locations for each trial. Randomization was used extensively in the data analyzed to test for statistical significance. Data collection and analysis were not performed blind to the conditions of the experiments. No animals or data points were excluded from any of the analyses. Please see additional information in the Life Sciences Reporting Summary.

For Figure 4—figure supplement 3, in order to match the selectivity properties of neurons in the model with those of LPFC data, we first quantified the selectivity of LPFC activity as follows. First, using a two-way ANOVA with independent variables of target locations (seven locations) and task epoch (Delay 1 and Delay 2), we categorized cells as those with pure working memory selectivity (those with target information in both Delay 1 and Delay 2, and those with selectivity to target location and task epoch, but no interaction, 27.6% of cells), those with mixed selectivity to target location and task epoch (those with a significant main effect of target location and task epoch, as well as a significant interaction between target location and task epoch, 43.9% of cells). And using two one-way ANOVAs of target location (one in Delay 1 and one in Delay 2), we categorized cells as those with pure motor preparation selectivity (those with significant selectivity in Delay 2, but not Delay 1, 28.6% of cells).

For the bump attractor model in Figure 4—figure supplement 2, we used two populations of firing-rate units for the memory and preparation input (N = 80 for each, and the whole population consisted of the working memory and motor preparation populations), and tested the model’s performance with different overlapping ratios between the two populations (if the overlapping ratio was 0%, then the full network consisted of 160 units, whereas if the overlapping ratio was 100%, then the full network consisted of 80 units). The firing rate of the population was characterized by:

where $\tau$ was a uniform decay constant; ${\displaystyle r}$ was the population firing rate; $W_{rec}$ was the recurrent connection weight between units; $I$ was the external input; $W_{in}$ was the loading weight of input signal to the population; ${\displaystyle \sigma }$ was a Gaussian noise term. For numerical simulation, we used Newton’s method:

where we set $\tau =20ms$ and $dt=2ms$; ${\alpha }_t$ was a scalar obtained by $mean\left(r_t\right)/mean\left(r_0\right)$, and it was applied uniformly to each unit of the whole population to maintain the mean population firing rate at the baseline level (divisive normalization). $\phi \left(x\right)$ was a piecewise nonlinear activation function adopted from Wimmer et al., 2014:

The matrix, $W_{rec}$, had a diagonal shape with stronger positive values near the diagonal, and weaker negative values elsewhere, such that only a few neighboring units were connected via excitatory weights to each other while being connected via inhibitory weights to the rest. In this way, a structured input signal to adjacent units was able to generate a local self-sustaining bump of activity. There were eight input units, representing the eight spatial target locations in the animal’s task. For each input unit, the loading weight matrix $W_{in}$ specified a random group of 10 adjacent units in the working memory population, as well as the motor preparation population, to receive the signal. The input to the working memory population was transiently active in the target presentation period, and the input to the motor preparation population was transiently active in the distractor presentation period. In each trial, the label for working memory and motor preparation inputs was always the same. Distractors used the same input loadings as the working memory input did, but the strength was only 50%, and the distractor label was always different from the target label.

For the linear subspace model in Figure 4—figure supplement 4, we used a total of N = 112 units where the firing rate of the population was characterized by:

where $\tau$ was a uniform decay constant ${\displaystyle r}$ was the population firing rate; $W_{rec}$ was the recurrent connection weight between units; $I$ was the external input; $W_{in}$ was the loading weight of input signal to the population; $\sigma$ was a Gaussian noise term. For numerical simulation, we used Newton’s method:

where we set $\tau =20ms$ and $dt=2ms$; ${\alpha }_t$ was a scalar obtained by $mean\left(r_t\right)/mean\left(r_0\right)$, and was applied uniformly to each unit of the whole population to maintain the mean population firing rate at the baseline level (divisive normalization). We constructed the recurrent weight matrix from eigendecomposition:

where $Q$ was a random square matrix whose columns were the eigenvectors of $W_{rec}$, and Λ was a diagonal matrix whose diagonal elements were the corresponding eigenvalues for each eigenvector. The first 17 eigenvalues in Λ were 1 (thus there were 17 stable eigenvectors), while the rest of the eigenvalues were randomly chosen between 0 and 1 using a uniform distribution. In a network of N neurons, the simultaneous activity of all the neurons represents a vector in an N dimensional space, and hence the vector notation and the network activity can be used interchangeably. The population activity will stay stable across time if it is a linear combination of the stable eigenvectors (Murray et al., 2017). In each simulation, we assigned 1 stable eigenvector as baseline activity (with entries selected from a uniform distribution U(0,1)), 8 stable eigenvectors for working memory activity, and 8 stable eigenvectors for motor preparation activity (with entries selected from U(1,2)). In order to ensure that decoding performance in Delay 1 and Delay 2 were the same, we imposed a positive mean for the motor preparation activity, so that the incorporation of motor preparation in Delay 2 would elevate the population mean, and divisive normalization would reduce the mean activity of both working memory and motor preparation information. Otherwise, if the motor preparation activity had zero mean, there would be a significant increase of decoding performance in Delay 2. In the input weight matrix, the input activity for working memory corresponded to the 8 working memory eigenvectors, and the input activity for motor preparation corresponded to the 8 motor preparation eigenvectors. For each target location, there was a one-to-one mapping of working memory activity and motor preparation activity. The distractor inputs had the same input loading as did the target inputs, but with a lower magnitude (0.2 compared to target). At the beginning of each trial, the population started with baseline activity equal to the stable baseline eigenvector, then the input for working memory was transiently active in the target presentation period, and the input for motor preparation was transiently active in the distractor presentation period. When the input activity had the same direction as a stable eigenvector, the resultant population activity would stay stable across time because it was still a linear combination of stable eigenvectors. As all the input activity corresponded to stable eigenvectors, all target information, distractor information, and motor preparation information were maintained stably across time.

Due to our experimental design, the working memory location and the motor preparation locations were identical in each trial, and thus correlated. We can imagine another case where there are two types of information that do not have a one-to-one mapping (for example, in a task that requires memorizing locations of items - one out of two possible locations, and their colors - one out of three possible colors, which are uncorrelated). When each target location is associated with only one stimulus color (similar to our working memory and motor preparation locations), the incorporation of stimulus color information in Delay 2 would add only one out of three possible shifts (representing the three possible stimulus colors) to the clusters representing target location (Figure 2—figure supplement 7a). However, when target location and stimulus color are uncorrelated (each stimulus color is equally likely to appear in each target location), the incorporation of stimulus color information could add any of the three possible shifts to the clusters representing target location activity, leading to much more diffuse clusters (Figure 2—figure supplement 7b). In this latter case, we propose a more general formulation to estimate the information subspaces for target location and stimulus color. First, we group trials by target location and obtain the trial-averaged and time-averaged activity in Delay 1 (${\displaystyle \overline{G_{\text{1}}}}$). Next, we group trials by stimulus color and obtain the trial-averaged and time-averaged activity in Delay 2 (${\displaystyle \overline{G_{\text{2}}}}$). Finally, we estimate the subspaces by:

where ${\displaystyle \overline{L}}$ and ${\displaystyle \overline{C}}$ define the subspaces for target location and stimulus color, while a and b are scalars representing the mixing coefficients. ${\displaystyle E\left[\overline{C_L}\right]}$ represents the expectation of stimulus color activity associated with particular target locations, and ${\displaystyle E\left[\overline{L_C}\right]}$ represents the expectation of target location activity associated with particular stimulus colors. At one extreme, the correlation between target location and stimulus color could be 0 (completely random pairing), in which case the expectation value will reduce to 0 if averaging across the other variable does not result in a net translation (which also means there will be no code morphing). In this case, there is no need to minimize mutual information, as the ${\displaystyle \overline{L}}$ and ${\displaystyle \overline{C}}$ vectors will remain unchanged. On the other hand, if there is a net translation, code morphing will be present, and there will be a need to minimize the mutual information to recover the angles between the subspaces. At the other extreme, the correlation could be 1 (one-to-one mapping), in which case the expectation value will reduce to ${\displaystyle \overline{C}}$ and ${\displaystyle \overline{L}}$. We can verify that the decorrelation method used for working memory and motor preparation elements in this work was a special case of this formulation (${\displaystyle \overline{G_{\text{1}}}}$ and ${\displaystyle \overline{G_{\text{2}}}}$ become ${\displaystyle \overline{D_{\text{1}}}}$ and ${\displaystyle \overline{D_{\text{2}}}}$ as memory and preparation have the same grouping, and the expectations reduce to ${\displaystyle \overline{P}}$ and ${\displaystyle \overline{M}}$ as there is one-to-one mapping). We would perform the same optimization on $(a,b)$ that will give the least mutual information between ${\displaystyle \overline{L}}$ and ${\displaystyle \overline{C}}$.

The code package and data needed to perform the analyses used in the paper is available at https://github.com/chengtang827/MemoryPreparationSubspace (Tang, 2020; copy archived at https://github.com/elifesciences-publications/MemoryPreparationSubspace).

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

This work was supported by startup grants from the Ministry of Education Tier 1 Academic Research Fund and SINAPSE to CL, a grant from the NUS-NUHS Memory Networks Program to S-CY, a grant from the Ministry of Education Tier 2 Academic Research Fund to CL and S-CY (MOE2016-T2-2-117), and a grant from the Ministry of Education Tier 3 Academic Research Fund to CL and S-CY (MOE2017-T3-1-002).

Animal experimentation: All animal procedures were approved by, and conducted in compliance with the standards of the Agri-Food and Veterinary Authority of Singapore and the Singapore Health Services Institutional Animal Care and Use Committee (SingHealth IACUC #2012/SHS/757). The procedures also conformed to the recommendations described in Guidelines for the Care and Use of Mammals in Neuroscience and Behavioral Research (National Academies Press, 2003).

1. Received: April 22, 2020
2. Accepted: August 21, 2020
3. Version of Record published: September 9, 2020 (version 1)

© 2020, Tang et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.