• (2024) Sigalas, E. and Libedinsky, C.  Mixed recurrent connectivity architecture in primate prefrontal cortex. bioRxiv (under submission)(link)
  • (2024) Tao, W. and Libedinsky, C. Evidence of Activity-Silent Working Memory in Prefrontal Cortex. bioRxiv (under submission)(link)
  • (2023) Herikstad, R. and Libedinsky, C.  Prefrontal Manifold Geometry Explains Reaction Time Variability. bioRxiv (revise and resubmit at Nature Communications) (link)


Highlighted Work:

  • (2023) Tan P.K., Tang C., Herikstad R., Pillay, A., Libedinsky, C. . Distinct lateral prefrontal regions are organized in an anterior-posterior functional gradient. Journal of Neuroscience, 43(38), pp.6564-6572. (link)
  • (2023) Sigalas, E., Vo, T.V., Leong, T.Y. and Libedinsky, C., 2023. Discovering Low-Dimensional Causal Pathways between Multiple Interacting Neuronal Populations. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 45, No. 45). (link)
  • (2023) Libedinsky, C. . Comparing representations and computations in single neurons versus neural networks. Trends in Cognitive Sciences. Vol. 27, Issue 6, p517-527 (link)  

Brief Summary

Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function.

  • (2022) Kumar, M.G., Tan, C., Libedinsky, C., Yen, S.C. and Tan, A.Y.Y. A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation. Cerebral Cortex 32(18), pp.3917-3936 (link)
  • (2021) Kumar, M.G., Tan, C., Libedinsky, C., Yen, S.C. and Tan, A.Y.Y. One-shot learning of paired associations by a reservoir computing model with Hebbian plasticity. arXiv:2106.03580. (under submission)
  • (2020) Cheng T., Herikstad R., Parthasarathy, A., Libedinsky, C., Yen, S.C. . Independent Activity Subspaces for Working Memory and Motor Preparation in the Lateral Prefrontal Cortex. eLife, 9, p.e58154. (link Equal Contribution, corresponding senior authors

    Brief Summary

    The lateral prefrontal cortex is involved in the integration of multiple types of information, including working memory and motor preparation. However, it is not known how downstream regions can extract one type of information without interference from the others present in the network. Here we show that the lateral prefrontal cortex contains two independent low-dimensional subspaces: one that encodes working memory information, and another that encodes motor preparation information. These subspaces capture all the information about the target in the delay periods, and the information in both subspaces is reduced in error trials. A single population of neurons with mixed selectivity forms both subspaces, but the information is kept largely independent from each other. A bump attractor model with divisive normalization replicates the properties of the neural data. These results have implications for the neural mechanisms of cognitive flexibility and capacity limitations.

  • (2019) Parthasarathy, A., Cheng T., Herikstad R., Loong F.C., Yen, S.C. , Libedinsky, C. . Time-Invariant Working Memory Representations in the Presence of Code-Morphing in the Lateral Prefrontal Cortex. Nature Communications, 10, 4995 (full text)
    Equal Contribution, corresponding senior authors

    Brief Summary

    Maintenance of working memory is thought to involve the activity of prefrontal neuronal populations with strong recurrent connections. However, it was recently shown that distractors evoke a morphing of the prefrontal population code, even when memories are maintained throughout the delay. How can a morphing code maintain time-invariant memory information? We hypothesized that dynamic prefrontal activity contains time-invariant memory information within a subspace of neural activity. Using an optimization algorithm, we found a low-dimensional subspace that contains time-invariant memory information. This information was reduced in trials where the animals made errors in the task and was also found in periods of the trial not used to find the subspace. A bump attractor model replicated these properties and provided predictions that were confirmed in the neural data. Our results suggest that the high-dimensional responses of prefrontal cortex contain subspaces where different types of information can be simultaneously encoded with minimal interference.

  • (2019) Libedinsky, C. D.., & Fernandez, P. F. . Graded Memory: A Cognitive Category to Replace Spatial Sustained Attention and Working Memory. The Yale Journal of Biology and Medicine92(1), 121. (full text)
  • (2017) Parthasarathy, A., Herikstad, R., Bong, J. H., Medina, F. S., Libedinsky, C. , & Yen, S. C. . Mixed selectivity morphs population codes in prefrontal cortex. Nature Neuroscience, 1-10. (full text + suppl.) Equal Contribution, corresponding senior authors. Highlighted in Nature Reviews Neuroscience (full text)

    Brief Summary

    Monkeys were trained to perform a delay saccade task with interfering distractors. This task required the maintenance of spatial information despite distracting stimuli. We recorded from neurons in the LPFC and the FEF and found that the code used by LPFC, but not FEF, morphed to a different code after the distractor was presented. The morphing was assessed using cross-temporal decoding and state-space analysis: the code employed by LPFC to encode memory information during the first delay period (before the distractor) could not be used to predict the location of the memory during the second delay (after the distractor). We found that neurons with mixed selectivity were necessary and sufficient for the code-morphing to occur. These neurons encoded both the target location and the task epoch (delay period before or after the distractor) in a non-linear manner (i.e. there was an interaction between both types of information). These results are hard to reconcile with the predominant view of a stable code during memory maintenance and raise the question of how downstream areas may be able to interpret this information in a stable manner. Furthermore, we highlight functional differences between the FEF and LPFC in memory maintenance and distractor suppression.

  • (2015) Massar, S.A.*, Libedinsky, C.*, Weiyan, C., Huettel, S. A., & Chee, M. W. Separate and overlapping brain areas encode subjective value during delay and effort discounting. Neuroimage120, 104-113 (full text)
    *Equal Contribution, co-first author
  • (2013) Libedinsky, C., Massar, S. A., Ling, A., Chee, W., Huettel, S. A., & Chee, M. W. Sleep deprivation alters effort discounting but not delay discounting of monetary rewards. Sleep36(6), 899-904 (full text). Editorial Commentary in Sleep (full text)
  • (2013) Lee, Y., Chong, M. F., Liu, J. C., Libedinsky, C., Gooley, J. J., Chen, S., … & Chee, M. W. Dietary disinhibition modulates neural valuation of food in the fed and fasted states. The American journal of clinical nutrition97(5), 919-925 (full text)
  • (2011) Libedinsky, C., Smith, D. V., Teng, C. S., Namburi, P., Chen, V. W., Huettel, S. A., & Chee, M. W. Sleep deprivation alters valuation signals in the ventromedial prefrontal cortex. Frontiers in behavioral neuroscience(full text)
  • (2011) Libedinsky C, Livingstone M. Role of prefrontal cortex in conscious visual perception. Journal of Neuroscience 31(1):64-9 (full textsuppl)
  • (2009) Libedinsky C, Savage T, Livingstone M. Perceptual and physiological evidence for a role for early visual areas in motion-induced blindness. Journal of Vision 9(1):14.1-10 (full text)


  • (2024) McMillan, K., So, R.Q., Libedinsky, C., Ang, K.K. and Premchand, B.,. “Spike-Weighted Spiking Neural Network with Spiking Long Short-Term Memory: A Biomimetic Approach to Decoding Brain Signals”. Algorithms, 17(4), p.156.
  • (2023) Stampfl, Anton PJ, Zhongdong Liu, Jun Hu, Kei Sawada, H. Takano, Yoshiki Kohmura, Tetsuya Ishikawa et al.  “SYNAPSE: An international roadmap to large brain imaging.” Physics Reports 999 : 1-60.
  • (2022) So R.Q., Libedinsky C.  Towards a Wireless Implantable Brain-Machine Interface for Locomotion Control. In: Thakor N.V. (eds) Handbook of Neuroengineering. Springer, Singapore.
  • (2021) Ng, H.W., Premchand, B., Toe, K.K., Libedinsky, C. and So, R.Q., November. Intention Estimation Based Adaptive Unscented Kalman Filter for Online Neural Decoding. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 5808-5811). IEEE.
  • (2021) Blasiak, A., Ng, K.A., Wong, D.S., Tsai, C.W., Rusly, A., Gammad, G.G.L., Voges, K., Libedinsky, C., Yen, S.C., Thakor, N.V. and Lahiri, A. STEER: 3D Printed Guide for Nerve Regrowth Control and Neural Interface in Non-Human Primate Model. IEEE Transactions on Biomedical Engineering.
  • (2020) Shaikh, S., So, R., Sibindi, T., Libedinsky, C. and Basu, A., October. Towards Autonomous Intra-cortical Brain Machine Interfaces: Applying Bandit Algorithms for Online Reinforcement Learning. In 2020 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.
  • (2020) Premchand, B., Toe, K.K., Wang, C., Shaikh, S., Libedinsky, C., Ang, K.K. and So, R.Q. Decoding movement direction from cortical microelectrode recordings using an LSTM-based neural network. 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3007-3010).
  • (2020) Teh D.B.L., Bansal A., Chai C., Toh T.B., Tucker R.A.J., Gammad G.G.L., Yeo Y., Lei Z., Zheng X., Yang F., Ho J.S., Bolem N., Wu B.C., Gnanasammandhan M.K., Hooi L., Dawe J.S., Libedinsky C., Ong W-Y., Halliwell B., Chow E.K-H., Lim K-L., Zhang Y., Kennedy B.K. Photodynamic Therapy: A Flexi‐PEGDA Upconversion Implant for Wireless Brain Photodynamic Therapy. Advanced Materials, 32(29), 2070219.
  • (2019) Shaikh, S., So, R., Sibindi, T., Libedinsky, C. and Basu, A. Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(2), pp.380-389.
  • (2019) Shaikh, S., So, R., Sibindi, T., Libedinsky, C. and Basu, A. Towards intelligent intracortical bmi (i2bmi): Low-power neuromorphic decoders that outperform kalman filters. IEEE Transactions on Biomedical Circuits and Systems, 13(6), pp.1615-1624.
  • (2019) Zhang, X., Libedinsky, C., So, R., Principe, J.C. and Wang, Y. Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(9), pp.1684-1694.
  • (2019) Wu S., Tan K.J., Govindarajan L.N., Stewart J.C., Gu L., Ho J.W.H., Katarya M., Wong B.H., Tan E-K, Li D, Claridge-Chang A., Libedinsky C.D., Cheng L., Aw S.S. Automated leg tracking reveals distinct conserved gait and tremor signatures in Drosophila models of Parkinson’s Disease and Spinocerebellar ataxia 3. PLoS biology, 17(6), p.e3000346.
  • (2019) Premchand, B., Toe, K.K., Wang, C.C., Libedinsky, C., Ang, K.K. and So, R.Q. Rapid Detection of Inactive Channels during Multi-unit Intracranial Recordings. IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp. 1-4).
  • (2019) Shaikh, S., So, R., Libedinsky, C., & Basu, A. Experimental Comparison of Hardware-Amenable Spike Detection Algorithms for iBMIs. 9th International IEEE/EMBS Conference on Neural Engineering (NER) 2019 Mar 20 (pp. 754-757).
  • (2019) Shaikh, S., So, R., Sibindi, T., Libedinsky, C., & Basu, A. Real-time Closed Loop Neural Decoding on a Neuromorphic Chip. 9th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 670-673). IEEE.
  • (2018) Yang, H., Ang, K.K., Libedinsky, C. and So, R.Q. Robust Local Field Potential-based Neural Decoding by Actively Selecting Discriminative Channels. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1992-1995).
  • (2018) Ong, L.J., Liu, S.C., Wong, M.D.S., Sibindi, T., Gammad, G.G.L., Tsai, C.W., Rusly, A., Ng, K.A., Libedinsky, C., Nag, S. and Yen, S.C. A Fully Wireless Implantable Multi-Channel Muscle Stimulator with Closed-Loop Feedback Control. In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 1-4). IEEE.
  • (2018) Yang, H., Ang, K. K., Libedinsky, C., & So, R. Q. Robust Local Field Potential-based Neural Decoding by Actively Selecting Discriminative Channels. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1992-1995). IEEE.
  • (2017) Tay, N., Tan, Y., Chng, K., Libedinsky, C., Gluckman, P., & Buschdorf, J. Effect of human milk formula with bovine colostrum supplementation on bone mineral density in infant cynomolgus macaques. Journal of Developmental Origins of Health and Disease, 1-10. doi:10.1017/S2040174417000812 (full text)
  • (2017) Koh, T. H., Libedinsky, C., Guan, C., Ang, K. K., & So, R. Q. Stop state classification in intracortical brain-machine-interface. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1926-1929). IEEE.
  • (2017) Lee, G.C., Libedinsky, C., Guan, C. and So, R. Use of wavelet transform coefficients for spike detection for a Robust Intracortical Brain Machine Interface. Neural Engineering (NER), 2017 8th International IEEE/EMBS Conference on (pp. 540-543). IEEE. (full text)
  • (2017) Yang, H., Libedinsky, C., Guan, C., Ang, K.K. and So, R.Q. Boosting performance in brain-machine interface by classifier-level fusion based on accumulative training models from multi-day data. In Engineering in Medicine and Biology Society (EMBC), 39th Annual International Conference of the IEEE (pp. 1922-1925). IEEE. (full text)
  • (2016) Camilo Libedinsky*, Rosa So* , Zhiming Xu, Toe K. Kyar, Duncun Ho, Clement Lim, Louiza Chan, Yuanwei Chua, Lei Yao, Jia Hao Cheong, Jung Hyup Lee, Kulkarni Vinayak Vishal, Yongxin Guo, Zhi Ning Chen, Lay K. Lim, Peng Li, Lei Liu, Xiaodan Zou, Kai K. Ang, Yuan Gao, Wai Hoe Ng, Boon Siew Han, Keefe Chng, Cuntai Guan, Minkyu Je, Shih-Cheng Yen.  Independent Mobility Achieved through a Wireless Brain-Machine Interface. PLOS One 11(11): e0165773. doi:10.1371/journal. pone.0165773 (full text)
    *Equal Contribution, co-first author
  • (2016) So, R., Libedinsky, C., Ang, K.K., Lim, W.C.C., Toe, K.K. and Guan, C. Adaptive decoding using local field potentials in a brain-machine interface. Engineering in Medicine and Biology Society (EMBC), IEEE 38th Annual International Conference of the (pp. 5721-5724). IEEE. (full text)
  • (2015) Sheshadri, S., Kortelainen, J., Rigosa, J., Cutrone, A., Bossi, S., Libedinsky, C., Lahiri, A., Chan, L., Chng, K., Thakor, N.V. and Delgado-Martínez, I. Classification of phases of hand grasp task by the extraction of miniature compound nerve action potentials (mCNAPs). In Neural Engineering (NER), 7th International IEEE/EMBS Conference on (pp. 593-596). IEEE. (full text)
  • (2014) Sheshadri, S., Kortelainen, J., Nag, S., Ng, K.A., Bazley, F.A., Michoud, F., Patil, A., Orellana, J., Libedinsky, C., Lahiri, A. and Chan, L. Correlation between muscular and nerve signals responsible for hand grasping in non-human primates. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE (pp. 2314-2317). IEEE. (full text)
  • (2003) Major, D. E., Rodman, H. R., Libedinsky, C., & Karten, H. J. Pattern of retinal projections in the California ground squirrel (Spermophilus beecheyi): anterograde tracing study using cholera toxin. Journal of Comparative Neurology463(3), 317-340 (full text)