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, 37 (25), 6098-6112

Mnemonic Encoding and Cortical Organization in Parietal and Prefrontal Cortices

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Mnemonic Encoding and Cortical Organization in Parietal and Prefrontal Cortices

Nicolas Y Masse et al. J Neurosci.

Abstract

Persistent activity within the frontoparietal network is consistently observed during tasks that require working memory. However, the neural circuit mechanisms underlying persistent neuronal encoding within this network remain unresolved. Here, we ask how neural circuits support persistent activity by examining population recordings from posterior parietal (PPC) and prefrontal (PFC) cortices in two male monkeys that performed spatial and motion direction-based tasks that required working memory. While spatially selective persistent activity was observed in both areas, robust selective persistent activity for motion direction was only observed in PFC. Crucially, we find that this difference between mnemonic encoding in PPC and PFC is associated with the presence of functional clustering: PPC and PFC neurons up to ∼700 μm apart preferred similar spatial locations, and PFC neurons up to ∼700 μm apart preferred similar motion directions. In contrast, motion-direction tuning similarity between nearby PPC neurons was much weaker and decayed rapidly beyond ∼200 μm. We also observed a similar association between persistent activity and functional clustering in trained recurrent neural network models embedded with a columnar topology. These results suggest that functional clustering facilitates mnemonic encoding of sensory information.SIGNIFICANCE STATEMENT Working memory refers to our ability to temporarily store and manipulate information. Numerous studies have observed that, during working memory, neurons in higher cortical areas, such as the parietal and prefrontal cortices, mnemonically encode the remembered stimulus. However, several recent studies have failed to observe mnemonic encoding during working memory, raising the question as to why mnemonic encoding is observed during some, but not all, conditions. In this study, we show that mnemonic encoding occurs when a cortical area is organized such that nearby neurons preferentially respond to the same stimulus. This result provides plausible neuronal conditions that allow for mnemonic encoding, and gives us further understanding of the brain's mechanisms that support working memory.

Keywords: macaque monkey; parietal cortex; persistent activity; prefrontal cortex; topological organization; working memory.

Figures

Figure 1.
Figure 1.
The spatial and motion tasks. A, The spatial task was a delayed memory saccade task, in which the monkeys had to maintain fixation on a central point for 500 ms, followed by a visual target presentation in one of eight locations for 307 ms, and finally by a delay period of 1013 ms. The central fixation point was then extinguished, and the monkey had 500 ms to saccade to the location of the remembered visual target to receive a reward. B, The motion task was either a DMS or a DMC task. The monkeys had to maintain fixation on a central point for 500 ms, followed by a 667 ms sample motion stimulus, followed by a 1013 ms delay, and then a 667 ms test motion stimulus. Monkeys released a manual lever to indicate whether the test stimulus was the same direction (DMS) or same category (DMC) as the previously presented sample. If the first test stimulus was a nonmatch, a second brief (267 ms) delay was presented followed by a test stimulus which matched the sample.
Figure 2.
Figure 2.
Example PFC and PPC neurons showing spatial and motion direction selectivity. A, Mean neural response of an example PFC neuron to the 8 spatial locations during the spatial task (left), and to the 6 motion directions during the motion task (right). Neural responses to the different spatial and motion directions are indicated by the different color traces. For the spatial task, the three vertical dashed lines, from left to right, indicate the visual target onset, the visual target offset, and the end of the delay period. For the motion task, the three vertical dashed lines, from left to right, indicate the sample stimulus onset, the sample offset, and the end of the delay period. B, Sample as A, except an example PPC neuron is shown.
Figure 3.
Figure 3.
PFC and PPC population level spatial and motion direction selectivity. A, Decoding accuracy during the spatial task for the PPC (green curve) and PFC (magenta) neuronal populations was calculated by decoding the spatial location from the population spike rates using SVM linear classifiers. Top, Horizontal green bar represents times at which the mean PPC decoding accuracy was significantly greater than chance (p < 0.01, bootstrap). Horizontal magenta bar represents times at which the mean PFC decoding accuracy was significantly greater than chance (p < 0.01, bootstrap). Bottom, Horizontal green bar represents times at which the mean PPC decoding accuracy was significantly (p < 0.01, bootstrap) greater than the PFC decoding accuracy. Horizontal magenta bar represents times at which the mean PFC decoding accuracy was significantly (p < 0.01, bootstrap) greater than the PPC decoding accuracy. Error bars indicate SEM. B, Same as A, except for the motion task. C, Similar to A, except that spatial selectivity was calculated using the normalized PEV: the percentage of variance in each neuron's spike rate that is explained by the spatial or motion direction, normalized to eliminate positive bias arising from a finite number of samples. D, Same as C, except for the motion task.
Figure 4.
Figure 4.
Tuning similarity and persistent activity in PPC and PFC. A, Spatial tuning similarity for the PPC (green curve) and the PFC (magenta) populations was calculated as the weighted dot-product between the preferred spatial location or motion directions of nearby neurons recorded during different sessions. As with Figure 2, vertical dashed lines indicate, from left to right, the stimulus presentation, the stimulus offset, and the end of the delay period. Error bars indicate SEM. B, Same as A, except for the motion task. C, The time course of the correlation between the spatial tuning similarity and the spatial stimulus selectivity (i.e., normalized PEV). D, Same as C, except showing the correlation between the motion direction tuning similarity with motion direction selectivity.
Figure 5.
Figure 5.
Tuning similarity and persistent activity in PPC and PFC for individual monkeys. A, Similar to Figure 3A, the decoding accuracy of PPC (green curve) and PFC (magenta curve) is shown for Monkey Q during the spatial (left) and motion (right) tasks. B, Similar to Figure 4A, B, the mean tuning similarity is shown for Monkey Q. C, Similar to Figure 4C, D, the time course of the correlation between the spatial tuning similarity and the spatial stimulus selectivity is shown for Monkey Q. D, Scatter plot showing the individual clusters of nearby neurons that are used to compute the correlation in C for Monkey Q. The tuning similarity (x-axis) and the logarithm of the stimulus selectivity (y-axis) were averaged over the last 500 ms of the delay epoch for PPC for the spatial (left) and motion (right) tasks. E, Same as D, except for the motion task. F–J, Same as A–E, except for Monkey W.
Figure 6.
Figure 6.
Tuning similarity as a function of distance. Tuning similarity was calculated in the same manner as done for Figure 4A, B, except that similarity was calculated between all pairs of neurons spaced between 62 and 188 μm apart (green curve), between 188 and 469 μm apart (magenta curve), and between 469 and 750 μm apart (cyan curve). A, The mean tuning similarity for the three distance ranges for PPC during the spatial task. Top, Horizontal bars represent times at which the tuning similarity was significantly greater than chance (p < 0.01, bootstrap). B, Same as A, except for PFC during the spatial task. C, Same as A, except for PPC during the motion task. D, Same as A, except for PFC during the motion task.
Figure 7.
Figure 7.
Recording locations, depths, and distances between neuron pairs. A, Heat map showing the number of neurons included in our analysis at each recording location on the array placed over PPC on Monkey Q. The array consisted of 32 electrodes, with 5 rows of 6 electrodes, with a final row of 2 electrodes at the anterior edge. B, Same as A, except for the array placed over PFC on Monkey Q. C, Same as A, except for the array placed over PPC on Monkey W. D, Same as A, except for the array placed over PFC on Monkey W. E, Histograms showing the distributions of estimated cortical depths for all PPC (green curve) and PFC (magenta curve) neurons included in our analysis. The cortical depth was estimated by finding the shallowest depth of all neurons recorded from each channel, and setting that depth equal to zero. F, Histograms showing the distributions of distances between neuron pairs for PPC (green curve) and PFC (magenta curve). As per our analysis, only neuron pairs separated by distances within a range of 62.5–750 μm are included.
Figure 8.
Figure 8.
Direction encoding and connectivity in trained recurrent neural network models. A, Architecture of the neural network trained to perform the DMS task. Input consisted of 72 excitatory motion direction-selective neurons whose preferred directions uniformly spanned 360 degrees. Each input neuron had a 50% connection probability with each neuron in the downstream recurrent network. The recurrent network consisted of 6 columns of 24 excitatory and 6 inhibitory neurons (180 neurons total). Connection probability within a column was 50%, and the connection probability between neurons from different columns was 5%. These recurrent connected neurons projected onto two downstream output neurons that were trained to selectively respond to a matching test stimulus or to a nonmatching test stimulus. The connection probability between each neuron in the recurrent network and each downstream neuron was 50%. B, The mean response of the match (green curve) and nonmatch (magenta curve) output neurons for match trials. Mean responses were averaged across all 20 trained networks. Error bars indicate 1 SD. C, Same as B, except for nonmatch trials. D, The normalized PEV averaged across all 3600 neurons in the recurrent network (20 networks each containing 180 recurrently connected neurons). Error bars indicate SEM. E, The mean motion direction tuning similarity across all pairs of neurons within the same column (blue curve) and all pairs of neurons from different columns (red curve). F, Scatter plot comparing the mean motion direction selectivity for each column measured using the normalized PEV (x-axis) versus the mean tuning similarity (y-axis) for all pairs of neurons within the same column. Both values were calculated from the last time point in the delay epoch. Each dot represents one of the 120 columns (20 networks each containing 6 columns). G, The time course of the correlation between the mean motion direction selectivity (normalized PEV) for each column versus the mean tuning similarity of neurons within the same column.
Figure 9.
Figure 9.
LFP spatial and motion direction selectivity. A, The mean LFP response, filtered with a 10 ms boxcar, to the eight target locations during the spatial task from an example electrode in PFC. Vertical dashed line indicates the time of visual target onset. B, The mean LFP response to the six motion directions during the motion task from the same example electrode. Vertical dashed line indicates the time of sample onset. C, LFP decoding accuracy during the spatial task for PPC (blue curve) and PFC (red), and during the motion task for PPC (green) and PFC (black) was calculated by decoding the spatial location from the LFP from all electrodes during each session using SVM linear classifiers. Top, Horizontal bars represent times at which the decoding accuracy was significantly (p < 0.01, bootstrap) greater than chance. Vertical dashed line indicates the time of visual target (spatial task) or sample (motion task) onset. Error bars indicate the SEM.

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