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Papers : Biological and Artificial Neural Networks

I have collected the papers of Artificial Neural Networks which related to Neuroscience (especially Computational Neuroscience). If there are papers which is not listed, I would appreciate if you could tell me from Issue.

Artificial neural networks and computational neuroscience


  • D. Cox, T. Dean. "Neural networks and neuroscience-inspired computer vision". Curr. Biol. 24(18) 921-929 (2014). (sciencedirect)
  • A. Marblestone, G. Wayne, K. Kording. "Toward an integration of deep learning and neuroscience". (2016). (arXiv)
  • O. Barak. "Recurrent neural networks as versatile tools of neuroscience research". Curr. Opin. Neurobiol. (2017). (sciencedirect)
  • D. Silva, P. Cruz, A. Gutierrez. "Are the long-short term memory and convolution neural net biological system?". KICS. 4(2), 100-106 (2018). (sciencedirect)
  • N. Kriegeskorte, T. Golan. "Neural network models and deep learning - a primer for biologists". (2019). (arXiv)


  • T.P. Lillicrap, K.P. Kording. "What does it mean to understand a neural network?". (2019). (arXiv)

Analysis methods for neural networks

Methods for understanding of neural representation of ANN.


  • D. Barrett, A. Morcos, J. Macke. "Analyzing biological and artificial neural networks: challenges with opportunities for synergy?". (2018). (arXiv)

Neuron Feature

  • I. Rafegas, M. Vanrell, L.A. Alexandre. "Understanding trained CNNs by indexing neuron selectivity". (2017). (arXiv)
  • A. Nguyen, J. Yosinski, J. Clune. "Understanding Neural Networks via Feature Visualization: A survey". (2019). (arXiv)

Comparing the representations of neural networks with those of the Brains

Representational similarity analysis (RSA)
  • N. Kriegeskorte, J. Diedrichsen. "Peeling the Onion of Brain Representations". Annu. Rev. Neurosci. (2019). (Annu Rev Neurosci)
Canonical correlation analysis (CCA)
  • M. Raghu, J. Gilmer, J. Yosinski, J. Sohl-Dickstein. "SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability". NIPS. (2017). (arXiv)
  • H. Wang, et al. "Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis". (2018). (arXiv)
Centered kernel alignment (CKA)
  • S. Kornblith, M. Norouzi, H. Lee, G. Hinton. "Similarity of Neural Network Representations Revisited". (2019). (arXiv)
Representational stability analysis (ReStA)
  • S. Abnar, L. Beinborn, R. Choenni, W. Zuidema. "Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains". (2019). (arXiv)

Fixed point analysis for RNN

  • M.B. Ottaway, P.Y. Simard, D.H. Ballard. "Fixed point analysis for recurrent networks". NIPS. (1989). (pdf)
  • D. Sussillo, O. Barak. "Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks". Neural Comput. 25(3), 626-649 (2013). (MIT Press), (Jupyter notebook)
  • M.D. Golub, D. Sussillo. "FixedPointFinder: A Tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks". JOSS. (2018). (pdf), (GitHub)
  • G.E. Katz, J.A. Reggia. "Using Directional Fibers to Locate Fixed Points of Recurrent Neural Networks". IEEE. (2018). (IEEE)

Ablation analysis

  • A.S. Morcos, D.G.T. Barrett, N.C. Rabinowitz, M. Botvinick. "On the importance of single directions for generalization". ICLR. (2018). (arXiv)


  • M. Haesemeyer, A. Schier, F. Engert. "Convergent temperature representations in artificial and biological neural networks". (2018). (bioRxiv)

Cognitive computational neuroscience

  • N. Kriegeskorte, P. Douglas. "Cognitive computational neuroscience". Nat. Neurosci. 21(9), 1148-1160 (2018). (arXiv)
  • J.S. Bowers. "Parallel Distributed Processing Theory in the Age of Deep Networks". Trends. Cogn. Sci. (2019). (sciencedirect)
  • R.M. Cichy, D. Kaiser. "Deep Neural Networks as Scientific Models". Trends. Cogn. Sci. (2019). (sciencedirect)

Computational psychiatry

I haven't been able to completely survey papers in this field.

  • R.E. Hoffman, U. Grasemann, R. Gueorguieva, D. Quinlan, D. Lane, R. Miikkulainen. "Using computational patients to evaluate illness mechanisms in schizophrenia". Biol. Psychiatry. 69(10), 997–1005 (2011). (PMC)

Deep neural network as models of the Brain

Understanding the neural representation of the brain is difficult. Neural networks learn specific tasks (or be optimized for a specific loss function), and (sometimes) can get the same representation as the brain. Then, we can indirectly know the purpose of neural representation in the brain.


  • A.J.E. Kell, J.H. McDermott. "Deep neural network models of sensory systems: windows onto the role of task constraints". Curr. Opin. Neurobiol. (2019). (sciencedirect)

Cortical neuron

  • P. Poirazi, T. Brannon, B.W Mel. "Pyramidal Neuron as Two-Layer Neural Network". Neuron. 37(6). (2003). (Neuron)
  • B. David, S. Idan, L. Michael. "Single Cortical Neurons as Deep Artificial Neural Networks". (2019). (bioRxiv)


  • D. Zipser, R.A. Andersen. "A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons". Nat. 331, 679–684 (1988). (Nat.)
  • A. Krizhevsky, I. Sutskever, G. Hinton. "ImageNet classification with deep convolutional neural networks". NIPS (2012). (pdf)
    • (cf.) I. Goodfellow, Y. Bengio, A. Courville. "Deep Learning". MIT Press. (2016) : Chapter 9.10 "The Neuroscientific Basis for ConvolutionalNetworks"
  • D. Yamins, et al. "Performance-optimized hierarchical models predict neural responses in higher visual cortex". PNAS. 111(23) 8619-8624 (2014). (PNAS)
  • S. Khaligh-Razavi, N. Kriegeskorte. "Deep supervised, but not unsupervised, models may explain IT cortical representation". PLoS Comput. Biol. 10(11), (2014). (PLOS)
  • U. Güçlü, M.A.J. van Gerven. "Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream". J. Neurosci. 35(27), (2015). (J. Neurosci.)
  • D. Yamins, J. DiCarlo. "Eight open questions in the computational modeling of higher sensory cortex". Curr. Opin. Neurobiol. 37, 114–120 (2016). (sciencedirect)
  • K.M. Jozwik, N. Kriegeskorte, K.R. Storrs, M. Mur. "Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments". Front. Psychol. (2017). (Front. Psychol)
  • C. J. Spoerer, P. McClure, N. Kriegeskorte. "Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition". Front. Psychol. (2017). (Front. Psychol)
  • M.N.U. Laskar, L.G.S. Giraldo, O. Schwartz. "Correspondence of Deep Neural Networks and the Brain for Visual Textures". (2018). (arXiv)
  • I. Kuzovkin, et al. "Activations of Deep Convolutional Neural Network are Aligned with Gamma Band Activity of Human Visual Cortex". Commun. Biol. 1 (2018). (Commun. Biol.)
  • M. Schrimpf, et al. "Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?". (2018). (bioRxiv)
  • E. Kim, D. Hannan, G. Kenyon. "Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons". CVPR. (2018). (arXiv)
  • S. Ocko, J. Lindsey, S. Ganguli, S. Deny. "The emergence of multiple retinal cell types through efficient coding of natural movies". (2018). (bioRxiv)
  • Q. Yan, et al. "Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks". (2018). (arXiv)
  • A. Nayebi, D. Bear, J. Kubilius, K. Kar, S. Ganguli, D. Sussillo, J. DiCarlo, D. Yamins. "Task-Driven Convolutional Recurrent Models of the Visual System". (2018). (arXiv, GitHub)
  • J. Lindsey, S. Ocko, S. Ganguli, S. Deny. "A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs". (2019). (arXiv)
  • J. Ukita, T. Yoshida, K. Ohki. "Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network". Sci.Rep. (2019). (Sci.Rep.)
  • I. Fruend. "Simple, biologically informed models, but not convolutional neural networks describe target detection in naturalistic images". bioRxiv (2019). (bioRxiv)
  • T.C. Kietzmann, et al. "Recurrence required to capture the dynamic computations of the human ventral visual stream". (2019). (arXiv)
  • K. Qiao. et al. "Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices". (2019). (arXiv)
  • S.A. Cadena, et al. "Deep convolutional models improve predictions of macaque V1 responses to natural images". PLOS Comput. Biol. (2019). (PLOS, bioRxiv)
  • K. Kar, J. Kubilius, K. Schmidt, E.B. Issa, J.J. DiCarlo . "Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior". Nat. Neurosci. (2019). (Nat. Neurosci., bioRxiv)
  • A.S. Ecker. et al. "A rotation-equivariant convolutional neural network model of primary visual cortex". ICLR (2019). (OpenReview, arXiv)
  • A. Doerig. et al. "Capsule Networks but not Classic CNNs Explain Global Visual Processing". (2019). (bioRxiv)

Visual illusion

Also see the papers associated with PredNet.

  • E.J. Ward. "Exploring Perceptual Illusions in Deep Neural Networks". (2019). (bioRxiv)
  • E.D. Sun, R. Dekel."ImageNet-trained deep neural network exhibits illusion-like response to the Scintillating Grid". (2019). (arXiv)

Recursive Cortical Network (RCN; non NN model)

  • D. George, et al. "A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs". Science (2017). (Science, GitHub)

Weight shared ResNet as RNN for object recognition

  • Q. Liao, T. Poggio. "Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex". (arXiv)

Generating visual super stimuli

  • C.R. Ponce, et al. "Evolving super stimuli for real neurons using deep generative networks". Cell. 177, 999–1009 (2019). (bioRxiv, Cell)
  • P. Bashivan, K. Kar, J.J DiCarlo. "Neural Population Control via Deep Image Synthesis". Science. (2019). (bioRxiv, Science, GitHub1, GitHub2)
  • A.P. Batista. K.P. Kording. "A Deep Dive to Illuminate V4 Neurons". Trends. Cogn. Sci. (2019). (Trends. Cogn. Sci.)

Visual number sense

  • K. Nasr, P. Viswanathan, A. Nieder. "Number detectors spontaneously emerge in a deep neural network designed for visual object recognition". Sci. Adv. (2019). (Sci. Adv.)

Auditory cortex

  • U. Güçlü, J. Thielen, M. Hanke, M. van Gerven. "Brains on Beats". NIPS (2016) (arXiv)
  • A.J.E. Kell,D.L.K. Yamins,E.N. Shook, S.V. Norman-Haignere, J.H.McDermott. "A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy". Neuron 98(3), (2018) (sciencedirect)
  • T. Koumura, H. Terashima, S. Furukawa. "Cascaded Tuning to Amplitude Modulation for Natural Sound Recognition". J. Neurosci. 39(28), 5517-5533 (2019). (J. Neurosci., bioRxiv, GitHub)

Motor cortex

  • D. Sussillo, M. Churchland, M. Kaufman, K. Shenoy. "A neural network that finds a naturalistic solution for the production of muscle activity". Nat. Neurosci. 18(7), 1025–1033 (2015). (PubMed)
  • J.A. Michaels, et al. "A neural network model of flexible grasp movement generation". (2019). (bioRxiv)

Spatial coding (Place cells, Grid cells)

  • C. Cueva, X. Wei. "Emergence of grid-like representations by training recurrent neural networks to perform spatial localization". ICLR. (2018). (arXiv)
  • A. Banino, et al. "Vector-based navigation using grid-like representations in artificial agents". Nature. 557(7705), 429–433 (2018). (pdf, GitHub)
  • J.C.R. Whittington. et al. "Generalisation of structural knowledge in the hippocampal-entorhinal system". NIPS. (2018). (arXiv)

Rodent barrel cortex

  • C. Zhuang, J. Kubilius, M. Hartmann, D. Yamins. "Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System". NIPS. (2017). (arXiv)

Cognitive task

  • H.F. Song, G.R. Yang, X.J. Wang. "Reward-based training of recurrent neural networks for cognitive and value-based tasks". eLife. 6 (2017). (eLife)
  • G.R. Yang, M.R. Joglekar, H.F. Song, W.T. Newsome, X.J. Wang. "Task representations in neural networks trained to perform many cognitive tasks". Nat. Neurosci. (2019). (Nat. Neurosci.) (GitHub)

Time perception

  • N.F. Hardy, V. Goudar, J.L. Romero-Sosa, D.V. Buonomano. "A model of temporal scaling correctly predicts that motor timing improves with speed". Nat. Commun. 9 (2018). (Nat. Commun.)
  • J. Wang, D. Narain, E.A. Hosseini, M. Jazayeri. "Flexible timing by temporal scaling of cortical responses". Nat. Neurosci. 21 102–110(2018). (Nat. Neurosci.)
  • W. Roseboom, Z. Fountas, K. Nikiforou, D. Bhowmik, M. Shanahan, A. K. Seth. "Activity in perceptual classification networks as a basis for human subjective time perception". Nat. Commun. 10 (2019). (Nat. Commun.)

Short-term memory task

  • K. Rajan, C.D.Harvey, D.W.Tank. "Recurrent Network Models of Sequence Generation and Memory". Neuron. 90(1), 128-142 (2016). (sciencedirect)
  • A.E. Orhan, W.J. Ma. " A diverse range of factors affect the nature of neural representations underlying short-term memory". Nat. Neurosci. (2019). (Nat. Neurosci.), (bioRxiv), (GitHub)
  • N.Y. Masse. et al. "Circuit mechanisms for the maintenance and manipulation of information in working memory". Nat. Neurosci. (2019). (Nat. Neurosci.), (bioRxiv)


  • J. Chiang, et al. "Neural and computational mechanisms of analogical reasoning". (2019). (bioRxiv)
  • S. Na, Y.J. Choe, D. Lee, G. Kim. "Discovery of Natural Language Concepts in Individual Units of CNNs". ICLR. (2019). (OpenReview), (arXiv)

Language learning

  • B.M. Lake, T. Linzen, M. Baroni. "Human few-shot learning of compositional instructions". (2019). (arXiv)
  • A. Alamia, V. Gauducheau, D. Paisios, R. VanRullen. "Which Neural Network Architecture matches Human Behavior in Artificial Grammar Learning?". (2019). (arXiv)

Neural network architecture based on neuroscience


  • D. Hassabis, D. Kumaran, C. Summerfield, M. Botvinick. "Neuroscience-Inspired Artificial Intelligence". Neuron. 95(2), 245-258 (2017). (sciencedirect)

PredNet (Predictive coding network)

  • W. Lotter, G. Kreiman, D. Cox. "Deep predictive coding networks for video prediction and unsupervised learning". ICLR. (2017). (arXiv, GitHub)
  • W. Lotter, G. Kreiman, D. Cox. "A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception". (2018). (arXiv)
  • E. Watanabe, A. Kitaoka, K. Sakamoto, M. Yasugi, K. Tanaka. "Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction". Front. Psychol. (2018). (Front. Psychol.)
  • M. Fonseca. "Unsupervised predictive coding models may explain visual brain representation". (2019). (arXiv, GitHub)


  • R. Costa, Y. Assael, B. Shillingford, N. Freitas, T. Vogels. "Cortical microcircuits as gated-recurrent neural networks". NIPS. (2017). (arXiv)

Activation functions

  • G.S. Bhumbra. "Deep learning improved by biological activation functions". (2018). (arXiv)


  • L. Gonzalo, S. Giraldo, O. Schwartz. "Integrating Flexible Normalization into Mid-Level Representations of Deep Convolutional Neural Networks". (2018). (arXiv)
  • M.F. Günthner, et al. "Learning Divisive Normalization in Primary Visual Cortex". (2019). (bioRxiv)

Reinforcement Learning

I haven't been able to completely survey papers in this field.

  • N. Haber, D. Mrowca, L. Fei-Fei, D. Yamins. "Learning to Play with Intrinsically-Motivated Self-Aware Agents". NIPS. (2018). (arXiv)
  • J. X. Wang, et al. "Prefrontal cortex as a meta-reinforcement learning system". Nat. Neurosci. (2018). (Nat. Neurosci.), (bioRxiv), (blog)
  • M. Botvinick. et al. "Reinforcement Learning, Fast and Slow". Trends. Cogn. Sci. (2019). (Trends. Cogn. Sci.)
  • E.O. Neftci, B.B. Averbeck. "Reinforcement learning in artificial and biological systems". Nat. Mach. Intell. (2019). (Nat. Mach. Intell.)

Learning and development

Biologically plausible learning algorithms


  • J. Whittington, R. Bogacz. "Theories of Error Back-Propagation in the Brain". Trends. Cogn. Sci. (2019). (sciencedirect)
  • T.P. Lillicrap, A.Santoro. "Backpropagation through time and the brain". Curr. Opin. Neurobiol. (2019). (sciencedirect)

Equilibrium Propagation

  • Y. Bengio, D. Lee, J. Bornschein, T. Mesnard, Z. Lin. "Towards Biologically Plausible Deep Learning". (2015). (arXiv)
  • B. Scellier, Y. Bengio. "Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation". Front. Comput. Neurosci. 11(24), (2017). (arXiv)
  • J. Sacramento, R. P. Costa, Y. Bengio, W. Senn. "Dendritic cortical microcircuits approximate the backpropagation algorithm". NIPS. (2018). (arXiv)

Feedback alignment

  • T. Lillicrap, D. Cownden, D. Tweed, C. Akerman. "Random synaptic feedback weights support error backpropagation for deep learning". Nat. Commun. 7 (2016). (Nat. Commun.)
  • A. Nøkland. "Direct Feedback Alignment Provides Learning in Deep Neural Networks". (2016). (arXiv)
  • M. Akrout, C. Wilson, P.C. Humphreys, T.Lillicrap, D. Tweed. "Deep Learning without Weight Transport". (2019). (arXiv)
  • B.J. Lansdell, P. Prakash, K.P. Kording. "Learning to solve the credit assignment problem". (2019). (arXiv)

Local error signal

  • H. Mostafa, V. Ramesh, G.Cauwenberghs. "Deep Supervised Learning Using Local Errors". Front. Neurosci. (2018). (Front. Neurosci.).
  • A. Nøkland, L.H. Eidnes. "Training Neural Networks with Local Error Signals". (2019). (arXiv) (GitHub)


  • M. Jaderberg, et al. "Decoupled Neural Interfaces using Synthetic Gradients" (2016). (arXiv)
  • N. Ke, A. Goyal, O. Bilaniuk, J. Binas, M. Mozer, C. Pal, Y. Bengio. "Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding". NIPS. (2018). (arXiv)
  • S. Bartunov, A. Santoro, B. Richards, L. Marris, G. Hinton, T. Lillicrap. "Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures". NIPS. (2018). (arXiv)
  • R. Feldesh. "The Distributed Engram". (2019). (bioRxiv)
  • Y. Amit. "Deep Learning With Asymmetric Connections and Hebbian Updates". Front. Comput. Neurosci. (2019). (Front. Comput. Neurosci.). (GitHub)


  • F. Crick. "The recent excitement about neural networks". Nature. 337, 129–132 (1989). (Nat.)

Development of neural networks and brains

  • J. Shen, M. D. Petkova, F. Liu, C. Tang. "Toward deciphering developmental patterning with deep neural network". (2018). (bioRxiv)
  • A.M. Saxe, J.L. McClelland, S. Ganguli. "A mathematical theory of semantic development in deep neural networks". PNAS. (2019). (arXiv). (PNAS)
  • D.V. Raman, A.P. Rotondo, T. O’Leary. "Fundamental bounds on learning performance in neural circuits". PNAS. (2019). (PNAS)

Few shot Learning

  • A. Cortese, B.D. Martino, M. Kawato. "The neural and cognitive architecture for learning from a small sample". Curr. Opin. Neurobiol. 55, 133–141 (2019). (sciencedirect)

A Critique of Pure Learning

  • A. Zador. "A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains". (2019). (bioRxiv)

Brain Decoding & Brain-machine interface

  • E. Matsuo, I. Kobayashi, S. Nishimoto, S. Nishida, H. Asoh. "Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity". ACL SRW. (2016). (ACL Anthology)
  • Y. Güçlütürk, U. Güçlü, K. Seeliger, S.E.Bosch, R.J. van Lier, M.A.J. van Gerven. "Reconstructing perceived faces from brain activations with deep adversarial neural decoding". NIPS (2017). (NIPS)
  • R. Rao. "Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces". (2018). (arXiv)
  • G. Shen, T. Horikawa, K. Majima, Y. Kamitani. "Deep image reconstruction from human brain activity". PLOS (2019). (PLOS)


  • M.S. Goldman. "Memory without Feedback in a Neural Network". Neuron (2009). (sciencedirect)
  • R. Yuste. "From the neuron doctrine to neural networks". Nat. Rev. Neurosci. 16, 487–497 (2015). (Nat. Rev. Neurosci.)
  • S. Saxena, J.P. Cunningham. "Towards the neural population doctrine". Curr. Opin. Neurobiol. (2019). (sciencedirect)
  • D.J. Heeger. "Theory of cortical function". PNAS. 114(8), (2017). (PNAS)
  • C.C. Chow, Y. Karimipanah. "Before and beyond the Wilson-Cowan equations". (2019). (arXiv)
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