BNN-ANN-papers
Neural network papers
A curated collection of papers on the intersection of artificial neural networks and computational neuroscience.
Papers : Biological and Artificial Neural Networks
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Papers : Biological and Artificial Neural Networks / Artificial neural networks and computational neuroscience | |||
| sciencedirect | D. Cox, T. Dean. "Neural networks and neuroscience-inspired computer vision". (18) 921-929 (2014). ( ) | ||
| arXiv | A. Marblestone, G. Wayne, K. Kording. "Toward an integration of deep learning and neuroscience". (2016). ( ) | ||
| sciencedirect | O. Barak. "Recurrent neural networks as versatile tools of neuroscience research". (2017). ( ) | ||
| sciencedirect | D. Silva, P. Cruz, A. Gutierrez. "Are the long-short term memory and convolution neural net biological system?". (2), 100-106 (2018). ( ) | ||
| arXiv | N. Kriegeskorte, P. Douglas. "Cognitive computational neuroscience". (9), 1148-1160 (2018). ( ) | ||
| arXiv | N. Kriegeskorte, T. Golan. "Neural network models and deep learning - a primer for biologists". (2019). ( ) | ||
| arXiv | K.R. Storrs, N. Kriegeskorte. "Deep Learning for Cognitive Neuroscience". (2019). ( ) | ||
| Oxford | T.C. Kietzmann, P. McClure, N. Kriegeskorte. "Deep Neural Networks in Computational Neuroscience". . (2019). ( , )) | ||
| sciencedirect | J.S. Bowers. "Parallel Distributed Processing Theory in the Age of Deep Networks". (2019). ( ) | ||
| sciencedirect | R.M. Cichy, D. Kaiser. "Deep Neural Networks as Scientific Models". (2019). ( ) | ||
| sciencedirect | S. Musall, A.E. Urai, D. Sussillo, A.K. Churchland. "Harnessing behavioral diversity to understand neural computations for cognition". (2019). ( ) | ||
| Nat. Neurosci. | B.A. Richards, T.P. Lillicrap, et al. "A deep learning framework for neuroscience". (2019). ( ) | ||
| Neuron | U. Hasson, S.A. Nastase, A. Goldstein. "Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks". . (2020). ( ) | ||
| arXiv | A. Saxe, S. Nelli, C. Summerfield. "If deep learning is the answer, then what is the question?". (2020). ( ) | ||
| arXiv | T.P. Lillicrap, K.P. Kording. "What does it mean to understand a neural network?". (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Artificial neural networks and computational neuroscience / Analysis methods for neural networks | |||
| arXiv | D. Barrett, A. Morcos, J. Macke. "Analyzing biological and artificial neural networks: challenges with opportunities for synergy?". (2018). ( ) | ||
| arXiv | 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). ( ) | ||
| Annu Rev Neurosci | N. Kriegeskorte, J. Diedrichsen. "Peeling the Onion of Brain Representations". . (2019). ( ) | ||
| arXiv | M. Raghu, J. Gilmer, J. Yosinski, J. Sohl-Dickstein. "SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability". (2017). ( ) | ||
| arXiv | H. Wang, et al. "Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis". (2018). ( ) | ||
| arXiv | S. Kornblith, M. Norouzi, H. Lee, G. Hinton. "Similarity of Neural Network Representations Revisited". (2019). ( ) | ||
| arXiv | S. Abnar, L. Beinborn, R. Choenni, W. Zuidema. "Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains". (2019). ( ) | ||
| M.B. Ottaway, P.Y. Simard, D.H. Ballard. "Fixed point analysis for recurrent networks". (1989). ( ) | |||
| MIT Press | D. Sussillo, O. Barak. "Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks". (3), 626-649 (2013). ( , ) | ||
| M.D. Golub, D. Sussillo. "FixedPointFinder: A Tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks". (2018). ( , ) | |||
| IEEE | G.E. Katz, J.A. Reggia. "Using Directional Fibers to Locate Fixed Points of Recurrent Neural Networks". (2018). ( ) | ||
| arXiv | A.S. Morcos, D.G.T. Barrett, N.C. Rabinowitz, M. Botvinick. "On the importance of single directions for generalization". (2018). ( ) | ||
Papers : Biological and Artificial Neural Networks / Artificial neural networks and computational neuroscience / Computational psychiatry | |||
| PMC | R.E. Hoffman, U. Grasemann, R. Gueorguieva, D. Quinlan, D. Lane, R. Miikkulainen. "Using computational patients to evaluate illness mechanisms in schizophrenia". (10), 997–1005 (2011). ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Survey | |||
| sciencedirect | A.J.E. Kell, J.H. McDermott. "Deep neural network models of sensory systems: windows onto the role of task constraints". (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Cortical neuron | |||
| Neuron | P. Poirazi, T. Brannon, B.W Mel. "Pyramidal Neuron as Two-Layer Neural Network". . (6). (2003). ( ) | ||
| bioRxiv | B. David, S. Idan, L. Michael. "Single Cortical Neurons as Deep Artificial Neural Networks". (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Vision | |||
| Nature. | D. Zipser, R.A. Andersen. "A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons". , 679–684 (1988). ( ) | ||
| A. Krizhevsky, I. Sutskever, G. Hinton. "ImageNet classification with deep convolutional neural networks". (2012). ( ) | |||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Vision / pdf | |||
| Deep Learning | (cf.) I. Goodfellow, Y. Bengio, A. Courville. " ". MIT Press. (2016) : Chapter 9.10 "The Neuroscientific Basis for ConvolutionalNetworks" | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Vision | |||
| PNAS | D. Yamins, et al. "Performance-optimized hierarchical models predict neural responses in higher visual cortex". (23) 8619-8624 (2014). ( ) | ||
| PLOS | S. Khaligh-Razavi, N. Kriegeskorte. "Deep supervised, but not unsupervised, models may explain IT cortical representation". . (11), (2014). ( ) | ||
| J. Neurosci. | U. Güçlü, M.A.J. van Gerven. "Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream". (27), (2015). ( ) | ||
| sciencedirect | D. Yamins, J. DiCarlo. "Eight open questions in the computational modeling of higher sensory cortex". , 114–120 (2016). ( ) | ||
| Front. Psychol | 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". . (2017). ( ) | ||
| arXiv | M.N.U. Laskar, L.G.S. Giraldo, O. Schwartz. "Correspondence of Deep Neural Networks and the Brain for Visual Textures". (2018). ( ) | ||
| Commun. Biol. | I. Kuzovkin, et al. "Activations of Deep Convolutional Neural Network are Aligned with Gamma Band Activity of Human Visual Cortex". (2018). ( ) | ||
| bioRxiv | M. Schrimpf, et al. "Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?". (2018). ( ) | ||
| arXiv | E. Kim, D. Hannan, G. Kenyon. "Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons". (2018). ( ) | ||
| bioRxiv | S. Ocko, J. Lindsey, S. Ganguli, S. Deny. "The emergence of multiple retinal cell types through efficient coding of natural movies". (2018). ( ) | ||
| arXiv | Q. Yan, et al. "Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks". (2018). ( ) | ||
| Sci.Rep. | H. Wen, J. Shi, W. Chen, Z. Liu. "Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization". (2018). ( ) | ||
| arXiv | 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). ( ) | ||
| bioRxiv | I. Fruend. "Simple, biologically informed models, but not convolutional neural networks describe target detection in naturalistic images". (2019). ( ) | ||
| bioRxiv | A. Doerig, et al. "Capsule Networks but not Classic CNNs Explain Global Visual Processing". (2019). ( ) | ||
| bioRxiv | A.S. Benjamin, et al. "Hue tuning curves in V4 change with visual context". (2019). ( ) | ||
| bioRxiv | S. Baek, M. Song, J. Jang, et al. "Spontaneous generation of face recognition in untrained deep neural networks". (2019). ( ) | ||
| Front. Psychol | C. J. Spoerer, P. McClure, N. Kriegeskorte. "Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition". (2017). ( ) | ||
| 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 | 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). ( ) | ||
| Nat. Neurosci. | 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". (2019). ( , ) | ||
| PNAS | T.C. Kietzmann, C.J. Spoerer, L.K.A. Sörensen, R.M. Cichy, O.Hauk, N. Kriegeskorte, "Recurrence is required to capture the representational dynamics of the human visual system". (2019). ( ) | ||
| PLOS | S.A. Cadena, et al. "Deep convolutional models improve predictions of macaque V1 responses to natural images". (2019). ( , ) | ||
| OpenReview | A.S. Ecker, et al. "A rotation-equivariant convolutional neural network model of primary visual cortex". (2019). ( , ) | ||
| bioRxiv | E.J. Ward. "Exploring Perceptual Illusions in Deep Neural Networks". (2019). ( ) | ||
| arXiv | E.D. Sun, R. Dekel."ImageNet-trained deep neural network exhibits illusion-like response to the Scintillating Grid". (2019). ( ) | ||
| Science | D. George, et al. "A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs". (2017). ( , ) | ||
| arXiv | Q. Liao, T. Poggio. "Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex". (2016). ( ) | ||
| Sci.Rep. | J. Ukita, T. Yoshida, K. Ohki. "Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network". (2019). ( ) | ||
| bioRxiv | C.R. Ponce, et al. "Evolving super stimuli for real neurons using deep generative networks". . , 999–1009 (2019). ( , ) | ||
| bioRxiv | P. Bashivan, K. Kar, J.J DiCarlo. "Neural Population Control via Deep Image Synthesis". (2019). ( , , , ) | ||
| Trends. Cogn. Sci. | A.P. Batista. K.P. Kording. "A Deep Dive to Illuminate V4 Neurons". (2019). ( ) | ||
| Sci. Adv. | K. Nasr, P. Viswanathan, A. Nieder. "Number detectors spontaneously emerge in a deep neural network designed for visual object recognition". (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Auditory cortex | |||
| arXiv | U. Güçlü, J. Thielen, M. Hanke, M. van Gerven. "Brains on Beats". (2016) ( ) | ||
| sciencedirect | 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". (3), (2018) ( ) | ||
| J. Neurosci. | T. Koumura, H. Terashima, S. Furukawa. "Cascaded Tuning to Amplitude Modulation for Natural Sound Recognition". (28), 5517-5533 (2019). ( , , ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Motor cortex | |||
| PubMed | D. Sussillo, M. Churchland, M. Kaufman, K. Shenoy. "A neural network that finds a naturalistic solution for the production of muscle activity". (7), 1025–1033 (2015). ( ) | ||
| bioRxiv | J.A. Michaels, et al. "A neural network model of flexible grasp movement generation". (2019). ( ) | ||
| Nat.Commun. | J. Merel, M. Botvinick, G. Wayne. "Hierarchical motor control in mammals and machines". (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Spatial coding (Place cells, Grid cells, Head direction cells) | |||
| arXiv | C. Cueva, X. Wei. "Emergence of grid-like representations by training recurrent neural networks to perform spatial localization". (2018). ( ) | ||
| A. Banino, et al. "Vector-based navigation using grid-like representations in artificial agents". (7705), 429–433 (2018). ( , ) | |||
| arXiv | J.C.R. Whittington. et al. "Generalisation of structural knowledge in the hippocampal-entorhinal system". (2018). ( ) | ||
| arXiv | C.J. Cueva, P.Y. Wang, M. Chin, X. Wei. "Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks". (2020). ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Rodent barrel cortex | |||
| arXiv | C. Zhuang, J. Kubilius, M. Hartmann, D. Yamins. "Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System". (2017). ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Convergent Temperature Representations | |||
| bioRxiv | M. Haesemeyer, A. Schier, F. Engert. "Convergent temperature representations in artificial and biological neural networks". . (2019). ( ), ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Cognitive task | |||
| eLife | H.F. Song, G.R. Yang, X.J. Wang. "Reward-based training of recurrent neural networks for cognitive and value-based tasks". . (2017). ( ) | ||
| Nat. Neurosci. | 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". (2019). ( ) ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Time perception | |||
| Nat. Commun. | 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". (2018). ( ) | ||
| Nat. Neurosci. | J. Wang, D. Narain, E.A. Hosseini, M. Jazayeri. "Flexible timing by temporal scaling of cortical responses". 102–110(2018). ( ) | ||
| Nat. Commun. | 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". (2019). ( ) | ||
| arXiv | B. Deverett, et al. "Interval timing in deep reinforcement learning agents". . (2019). ( ) | ||
| arXiv | Z. Bi, C. Zhou. "Time representation in neural network models trained to perform interval timing tasks". (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Short-term memory task | |||
| sciencedirect | K. Rajan, C.D.Harvey, D.W.Tank. "Recurrent Network Models of Sequence Generation and Memory". (1), 128-142 (2016). ( ) | ||
| Nat. Neurosci. | A.E. Orhan, W.J. Ma. " A diverse range of factors affect the nature of neural representations underlying short-term memory". (2019). ( ), ( ), ( ) | ||
| Nat. Neurosci. | N.Y. Masse. et al. "Circuit mechanisms for the maintenance and manipulation of information in working memory". (2019). ( ), ( ) | ||
Papers : Biological and Artificial Neural Networks / Deep neural network as models of the Brain / Language | |||
| bioRxiv | J. Chiang, et al. "Neural and computational mechanisms of analogical reasoning". (2019). ( ) | ||
| OpenReview | S. Na, Y.J. Choe, D. Lee, G. Kim. "Discovery of Natural Language Concepts in Individual Units of CNNs". (2019). ( ), ( ) | ||
| arXiv | 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). ( ) | ||
Papers : Biological and Artificial Neural Networks / Neural network architecture based on neuroscience / Survey | |||
| sciencedirect | D. Hassabis, D. Kumaran, C. Summerfield, M. Botvinick. "Neuroscience-Inspired Artificial Intelligence". (2), 245-258 (2017). ( ) | ||
Papers : Biological and Artificial Neural Networks / Neural network architecture based on neuroscience / PredNet (Deep predictive coding network) | |||
| arXiv | W. Lotter, G. Kreiman, D. Cox. "Deep predictive coding networks for video prediction and unsupervised learning". (2017). ( , ) | ||
| Front. Psychol. | E. Watanabe, A. Kitaoka, K. Sakamoto, M. Yasugi, K. Tanaka. "Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction". (2018). ( ) | ||
| arXiv | M. Fonseca. "Unsupervised predictive coding models may explain visual brain representation". (2019). ( , ) | ||
| arXiv | W. Lotter, G. Kreiman, D. Cox. "A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception". . (2020). ( , ) | ||
| arXiv | R. Costa, Y. Assael, B. Shillingford, N. Freitas, T. Vogels. "Cortical microcircuits as gated-recurrent neural networks". (2017). ( ) | ||
| arXiv | 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). ( ) | ||
| bioRxiv | M.F. Günthner, et al. "Learning Divisive Normalization in Primary Visual Cortex". (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Reinforcement Learning | |||
| arXiv | N. Haber, D. Mrowca, L. Fei-Fei, D. Yamins. "Learning to Play with Intrinsically-Motivated Self-Aware Agents". (2018). ( ) | ||
| Nat. Neurosci. | J. X. Wang, et al. "Prefrontal cortex as a meta-reinforcement learning system". (2018). ( ), ( ), ( ) | ||
| Trends. Cogn. Sci. | M. Botvinick. et al. "Reinforcement Learning, Fast and Slow". (2019). ( ) | ||
| Nat. Mach. Intell. | E.O. Neftci, B.B. Averbeck. "Reinforcement learning in artificial and biological systems". (2019). ( ) | ||
| Nature | W. Dabney, Z. Kurth-Nelson, N. Uchida, C.K. Starkweather, D. Hassabis, R. Munos, & M. Botvinick. "A distributional code for value in dopamine-based reinforcement learning". . (2020). ( ). ( ) | ||
Papers : Biological and Artificial Neural Networks / Learning and development / Biologically plausible learning algorithms | |||
| sciencedirect | J. Whittington, R. Bogacz. "Theories of Error Back-Propagation in the Brain". (2019). ( ) | ||
| sciencedirect | T.P. Lillicrap, A.Santoro. "Backpropagation through time and the brain". (2019). ( ) | ||
| Nat. Rev. Neurosci. | T.P. Lillicrap, A. Santoro, L. Marris, et al. "Backpropagation and the brain". . (2020). ( ) | ||
| arXiv | 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". (24), (2017). ( ) | ||
| arXiv | J. Sacramento, R. P. Costa, Y. Bengio, W. Senn. "Dendritic cortical microcircuits approximate the backpropagation algorithm". (2018). ( ) | ||
| Nat. Commun. | T. Lillicrap, D. Cownden, D. Tweed, C. Akerman. "Random synaptic feedback weights support error backpropagation for deep learning". (2016). ( ) | ||
| arXiv | 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). ( ) | ||
| Front. Neurosci. | H. Mostafa, V. Ramesh, G.Cauwenberghs. "Deep Supervised Learning Using Local Errors". (2018). ( ) | ||
| arXiv | A. Nøkland, L.H. Eidnes. "Training Neural Networks with Local Error Signals". (2019). ( ) ( ) | ||
| arXiv | 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". (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". (2018). ( ) | ||
| bioRxiv | R. Feldesh. "The Distributed Engram". (2019). ( ) | ||
| Front. Comput. Neurosci. | Y. Amit. "Deep Learning With Asymmetric Connections and Hebbian Updates". (2019). ( ). ( ) | ||
| arXiv | T. Mesnard, G. Vignoud, J. Sacramento, W. Senn, Y. Bengio "Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks". (2019). ( ) | ||
| Nat. | F. Crick. "The recent excitement about neural networks". . , 129–132 (1989). ( ) | ||
Papers : Biological and Artificial Neural Networks / Learning and development / Learning dynamics of neural networks and brains | |||
| bioRxiv | J. Shen, M. D. Petkova, F. Liu, C. Tang. "Toward deciphering developmental patterning with deep neural network". (2018). ( ) | ||
| arXiv | A.M. Saxe, J.L. McClelland, S. Ganguli. "A mathematical theory of semantic development in deep neural networks". . (2019). ( ). ( ) | ||
| PNAS | D.V. Raman, A.P. Rotondo, T. O’Leary. "Fundamental bounds on learning performance in neural circuits". . (2019). ( ) | ||
| Nat.Commun. | R. C. Wilson, A. Shenhav, M. Straccia, J.D. Cohen. "The Eighty Five Percent Rule for optimal learning". (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Learning and development / Few shot Learning | |||
| sciencedirect | A. Cortese, B.D. Martino, M. Kawato. "The neural and cognitive architecture for learning from a small sample". , 133–141 (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Learning and development / A Critique of Pure Learning | |||
| bioRxiv | A. Zador. "A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains". (2019). ( ). ( ) | ||
Papers : Biological and Artificial Neural Networks / Brain Decoding & Brain-machine interface | |||
| ACL Anthology | E. Matsuo, I. Kobayashi, S. Nishimoto, S. Nishida, H. Asoh. "Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity". (2016). ( ) | ||
| NIPS | 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". (2017). ( ) | ||
| arXiv | R. Rao. "Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces". (2018). ( ) | ||
| PLOS | G. Shen, T. Horikawa, K. Majima, Y. Kamitani. "Deep image reconstruction from human brain activity". (2019). ( ) | ||
Papers : Biological and Artificial Neural Networks / Others | |||
| sciencedirect | M.S. Goldman. "Memory without Feedback in a Neural Network". (2009). ( ) | ||
| Nat. Rev. Neurosci. | R. Yuste. "From the neuron doctrine to neural networks". 16, 487–497 (2015). ( ) | ||
| sciencedirect | S. Saxena, J.P. Cunningham. "Towards the neural population doctrine". (2019). ( ) | ||
| PNAS | D.J. Heeger. "Theory of cortical function". . (8), (2017). ( ) | ||
| arXiv | C.C. Chow, Y. Karimipanah. "Before and beyond the Wilson-Cowan equations". (2019). ( ) | ||
jnv/lists