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Task-specific versus generalized mnemonic representations in parietal and prefrontal cortices

Nature Neuroscience volume 19, pages 143–149 (2016)Cite this article

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Abstract

Our ability to learn a wide range of behavioral tasks is essential for responding appropriately to sensory stimuli according to behavioral demands, but the underlying neural mechanism has been rarely examined by neurophysiological recordings in the same subjects across learning. To understand how learning new behavioral tasks affects neuronal representations, we recorded from posterior parietal cortex (PPC) before and after training on a visual motion categorization task. We found that categorization training influenced cognitive encoding in PPC, with a marked enhancement of memory-related delay-period encoding during the categorization task that was absent during a motion discrimination task before categorization training. In contrast, the prefrontal cortex (PFC) exhibited strong delay-period encoding during both discrimination and categorization tasks. This reveals a dissociation between PFC's and PPC's roles in working memory, with general engagement of PFC across multiple tasks, in contrast with more task-specific mnemonic encoding in PPC.

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Figure 1: DMS and DMC tasks.
Figure 2: Behavioral performance.
Figure 3: Example LIP neurons before and after categorization training.
Figure 4: Population-level direction and category classification in LIP.
Figure 5: Time course of direction and category classification in LIP during DMS and DMC tasks.
Figure 6: Example PPC and PFC neurons during the DMS task.
Figure 7: Time course of direction classification in PPC and PFC.

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Acknowledgements

The authors thank N. Santhanam, R. Kharkar and M. Rose for assistance with animal training, J. Hodnefield for expert behavioral and experimental assistance, S. Thomas for expert technical assistance, C. Gray for technical and surgical assistance, and the staff of The University of Chicago Animal Resources Center for expert veterinary assistance. We also thank J. Assad, S. Bensmaia, N. Brunel, J. Chaisangmongkon, T. Engel, G. Ibos and K. Mohan for valuable input and/or comments on an earlier version of this manuscript. This work was supported by a grants from the US National Institutes of Health (R01MH092927 to X.-J.W. and D.J.F. and R01EY019041 to D.J.F.), and NRSA MH097428 (A.S.). Additional support was provided by a McKnight Scholar award, the Alfred P. Sloan Foundation, NSF CAREER award 0955640, The Brain Research Foundation (D.J.F.) and R01-MH062349 (X.-J.W.).

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Authors and Affiliations

  1. Department of Neurobiology, The University of Chicago, Chicago, Illinois, USA

    Arup Sarma, Nicolas Y Masse & David J Freedman

  2. Center for Neural Science, New York University, New York, New York, USA

    Xiao-Jing Wang

  3. NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai, Shanghai, China

    Xiao-Jing Wang

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Contributions

D.J.F., A.S., N.Y.M. and X.-J.W. designed the experiments. A.S. and N.Y.M. trained the animals, performed all of the neurophysiological recordings, performed all of the data analysis and assisted with writing the manuscript. D.J.F. supervised the experimental work, assisted with animal training, neurophysiological recordings, data analysis and wrote the manuscript. X.-J.W. assisted in data analysis and edited the manuscript.

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Correspondence to David J Freedman.

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Integrated supplementary information

Supplementary Figure 1 Pre-recording behavioral performance.

Performance is shown for the last seven days of training prior to neuronal recordings for the DMS and DMC tasks. (a) The monkeys’ average DMS performance (proportion reported as non-match) is shown as a function of sample-test difference. The gray line is a sigmoid curve fit to the behavioral data. Dashed black line indicates chance performance. Error bars indicate SEM for sample-test differences that were presented on more than one session. The circular points show behavioral performance for sample-test differences tested in only one session. The mean number of presentations per sample-test difference was 127 per session. (b) The monkeys’ average DMC performance is shown as a function of sample direction. Colors correspond to the two categories and the green dashed lines correspond to the category boundary. Dashed black line indicates chance performance. Error bars indicate SEM.

Supplementary Figure 2 DMS training behavioral performance.

The monkeys’ DMS performance leading up to DMS recordings is shown as a function of training session for Monkey D (a) and Monkey H (b). The level of behavioral performance is depicted by plotting the sample-test difference at which the monkey would perform at 75% accuracy, as estimated by a sigmoid curve (1+1/(1+e-(x-a)/b)) fit to the average performance for the sample-test differences tested in each session. The first 21 (Monkey D) and 20 (Monkey H) motion-DMS training sessions were excluded because performance on 180° sample-test differences was lower than the performance threshold (as the monkey was not yet reliably differentiating between 180° non-matches).

Supplementary Figure 3 Single-neuron measures of direction and category classification.

The average time-course of direction and category selectivity for individual neurons measured by the rDSI (a) and rCTI (b) during DMS (pink curve) and DMC (green curve) tasks. The three dashed, vertical lines represent the start of the sample epoch, the end of the sample epoch, and the end of the delay epoch, respectively. Gray and black bars represent times at which classification accuracy was significantly different between the populations recorded during the two tasks (Wilcoxon rank-sum test, gray, P<0.05, black, P<0.01). Only cells that were direction selective during the sample or delay periods were used from each population. (c-f) Distributions of neurons’ rDSI (c,d) and rCTI (e,f) values during the sample (c,e) and delay (d,f) periods for direction selective cells during the indicated task epoch. Thick dashed lines indicate mean values across the population of direction selective cells during the DMS (pink) or DMC (green) tasks (rDSI, Sample: DMS µ=0.16, DMC µ=0.13; rCTI, Sample: DMS µ=-0.0015, DMC µ=0.0093; rDSI, Delay: DMS µ=0.044, DMC µ=0.038; rCTI, Delay: DMS µ=3.9x10-4, DMC µ=0.049; ***P<=0.001).

Supplementary Figure 4 Temporal stability of direction encoding in LIP.

Similar to Figure 5c, the stability of direction encoding in LIP neurons was determined by training the classifier at one time point (y axis) and testing at a second time point (x axis). Classification accuracy is indicated by the color at each x-y coordinate. (a) Direction classification accuracy during the DMS task was above chance during the sample epoch, and near chance during the delay epoch. Classification values were approximately equal for most training and test times during the sample, indicating that direction encoding was mostly stable during this epoch. (b) Direction classification accuracy during the DMC task was also above chance during the sample epoch, and near chance during the delay epoch. Similar to (a), direction encoding was mostly stable during the sample epoch.

Supplementary Figure 5 Mid-training DMC behavioral performance.

(a) The monkeys’ average DMC performance is shown as a function of sample direction. Colors correspond to the two categories and the green dashed lines correspond to the category boundary. The dashed black line indicates chance performance. Error bars indicate standard error of mean (SEM). (b–e) The monkeys’ average DMC performance during mid-training DMC (cyan) and fully-trained DMC (green) recordings is shown as a function of sample-test difference for match trials (b,c) and nonmatch trials (d,e) where sample directions were 22.5° (b,d) or 67.5° (c,e) from the boundary. Dashed black line indicates chance performance. Error bars indicate SEM.

Supplementary Figure 6 Example LIP neurons and population-level direction and category classification during the mid-training DMC task.

(a,b) Average activity evoked by 8 sample directions for two LIP neurons from the mid-training DMC task. Different traces indicate different sample directions and are colored according to their direction and category membership. The three dashed, vertical lines represent the start of the sample epoch, the end of the sample epoch, and the end of the delay epoch, respectively. Dark blue and dark red traces indicate sample directions near the middle of categories 1 and 2, respectively, while light blue and light red traces indicate sample directions near the category boundary. Both cells shown were direction and/or category selective during the sample period (one-way ANOVA, P<0.01). Neither cell was direction selective during the delay period. (c,d) The time course of direction and category selectivity in LIP during the mid-training DMC task was determined by computing classification accuracy as a function of time relative to sample onset using the (c) sample direction classifier and (d) sample category classifier. Error bars indicate SEM. The light and dark colored bars at the top of the figures indicate times at which classification accuracy was significantly above chance (light, P<0.05, dark, P<0.01, bootstrap).

Supplementary Figure 7 Monkeys Q and W DMS behavioral performance.

The monkeys’ average DMS performance is shown as a function of sample direction. The chance level of performance in the DMS task is 50%. Error bars indicate SEM.

Supplementary Figure 8 Temporal stability of direction encoding in PPC.

Similar to Figure 5c, the stability of direction encoding in PPC was determined by training the classifier at one time point (y axis) and testing at a second time point (x axis). Classification accuracy is indicated by color at each x-y coordinate. Direction classification accuracy in PPC was above chance during the sample period and near chance during the delay period. Classification accuracy was above chance only near the diagonal, indicating mostly dynamic direction encoding.

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Sarma, A., Masse, N., Wang, XJ. et al. Task-specific versus generalized mnemonic representations in parietal and prefrontal cortices. Nat Neurosci 19, 143–149 (2016). https://doi.org/10.1038/nn.4168

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