Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works

Untitled work

(2010) · Cited by 14,134 · see citing works

Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

Candès, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,973 · see citing works

Humans integrate visual and haptic information in a statistically optimal fashion

Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844 · see citing works

Bayesian Learning for Neural Networks

Neal, R. M. (1996) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-0745-0 · Cited by 4,276 · see citing works

An internal model for sensorimotor integration

Wolpert, D. M., Ghahramani, Z., & Jordan, M. I. (1995) · Science · doi:10.1126/science.7569931 · Cited by 3,553 · see citing works

Universal approximation bounds for superpositions of a sigmoidal function

Barron, A. R. (1993) · IEEE Transactions on Information Theory · doi:10.1109/18.256500 · Cited by 2,861 · see citing works

Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

Leshno, M., Lin, V. Y., Pinkus, A., & Schocken, S. (1993) · Neural Networks · doi:10.1016/s0893-6080(05)80131-5 · Cited by 2,121 · see citing works

Performance-optimized hierarchical models predict neural responses in higher visual cortex

Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924 · see citing works

Context-dependent computation by recurrent dynamics in prefrontal cortex

Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013) · Nature · doi:10.1038/nature12742 · Cited by 1,807 · see citing works

Bayesian inference with probabilistic population codes

Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006) · Nature Neuroscience · doi:10.1038/nn1790 · Cited by 1,457 · see citing works

Hybrid computing using a neural network with dynamic external memory

Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · Nature · doi:10.1038/nature20101 · Cited by 1,412 · see citing works

Sparse Coding and Decorrelation in Primary Visual Cortex During Natural Vision

Vinje, W. E., & Gallant, J. L. (2000) · Science · doi:10.1126/science.287.5456.1273 · Cited by 1,288 · see citing works

The neocortical circuit: themes and variations

Harris, K. D., & Shepherd, G. M. G. (2015) · Nature Neuroscience · doi:10.1038/nn.3917 · Cited by 1,215 · see citing works

A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons

Zipser, D., & Andersen, R. A. (1988) · Nature · doi:10.1038/331679a0 · Cited by 1,172 · see citing works

Causal Inference in Multisensory Perception

Körding, K. P., Beierholm, U., Ma, W. J., Quartz, S., Tenenbaum, J. B., & Shams, L. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000943 · Cited by 1,069 · see citing works

Untangling invariant object recognition

DiCarlo, J. J., & Cox, D. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.06.010 · Cited by 948 · see citing works

Random synaptic feedback weights support error backpropagation for deep learning

Lillicrap, T., Cownden, D., Tweed, D., & Akerman, C. J. (2016) · Nature Communications · doi:10.1038/ncomms13276 · Cited by 879 · see citing works

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

Cadieu, C. F., Hong, H., Yamins, D. L. K., Pinto, N., Ardila, D., Solomon, E. A., Majaj, N. J., & DiCarlo, J. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003963 · Cited by 824 · see citing works

Probabilistic population codes for Bayesian decision making

Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.021 · Cited by 645 · see citing works

A neural network that finds a naturalistic solution for the production of muscle activity

Sussillo, D., Churchland, M. M., Kaufman, M. T., & Shenoy, K. V. (2015) · Nature Neuroscience · doi:10.1038/nn.4042 · Cited by 598 · see citing works

Responses of neurons in macaque MT to stochastic motion signals

Britten, K. H., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (1993) · Visual Neuroscience · doi:10.1017/s0952523800010269 · Cited by 594 · see citing works

Representations of Odor in the Piriform Cortex

Stettler, D. D., & Axel, R. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.005 · Cited by 583 · see citing works

Inhibition dominates sensory responses in the awake cortex

Haider, B., Häusser, M., & Carandini, M. (2012) · Nature · doi:10.1038/nature11665 · Cited by 541 · see citing works

Optimal representation of sensory information by neural populations

Jazayeri, M., & Movshon, J. A. (2006) · Nature Neuroscience · doi:10.1038/nn1691 · Cited by 530 · see citing works

Humans use internal models to estimate gravity and linear acceleration

Merfeld, D. M., Zupan, L. H., & Peterka, R. J. (1999) · Nature · doi:10.1038/19303 · Cited by 501 · see citing works

Neural correlates of reliability-based cue weighting during multisensory integration

Fetsch, C. R., Pouget, A., DeAngelis, G. C., & Angelaki, D. E. (2012) · Nature Neuroscience · doi:10.1038/nn.2983 · Cited by 459 · see citing works

Bayesian integration of visual and auditory signals for spatial localization

Battaglia, P., Jacobs, R. A., & Aslin, R. Ν. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001391 · Cited by 426 · see citing works

Slant from texture and disparity cues: Optimal cue combination

Hillis, J. M., Watt, S. J., Landy, M. S., & Banks, M. S. (2004) · Journal of Vision · doi:10.1167/4.12.1 · Cited by 421 · see citing works

Gradient-based learning algorithms for recurrent networks and their computational complexity

Williams, R. J., & Zipser, D. (1995) · Cited by 413 · see citing works

Random convergence of olfactory inputs in the Drosophila mushroom body

Caron, S. J. C., Ruta, V., Abbott, L. F., & Axel, R. (2013) · Nature · doi:10.1038/nature12063 · Cited by 409 · see citing works

Memory without Feedback in a Neural Network

Goldman, M. S. (2009) · Neuron · doi:10.1016/j.neuron.2008.12.012 · Cited by 342 · see citing works

Orientation selectivity in the cat's striate cortex is invariant with stimulus contrast

Sclar, G., & Freeman, R. D. (1982) · Experimental Brain Research · doi:10.1007/bf00238641 · Cited by 339 · see citing works

Balanced Amplification: A New Mechanism of Selective Amplification of Neural Activity Patterns

Murphy, B. K., & Miller, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.02.005 · Cited by 338 · see citing works

Probabilistic Interpretation of Population Codes

Zemel, R. S., Dayan, P., & Pouget, A. (1998) · Neural Computation · doi:10.1162/089976698300017818 · Cited by 333 · see citing works

Probabilistic Interpretation of Population Codes

Zemel, R. S., Dayan, P., & Pouget, A. (1999) · The MIT Press eBooks · doi:10.7551/mitpress/4924.003.0011 · Cited by 308 · see citing works

Towards Biologically Plausible Deep LearningPreprint

Bengio, Y., Lee, D.-H., Bornschein, J., Mesnard, T., & Lin, Z. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.04156 · Cited by 295 · see citing works

Training excitatory-inhibitory recurrent neural networks for cognitive tasks: A simple and flexible framework

Song, H. F., Yang, G. R., & Wang, X.-J. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004792 · Cited by 281 · see citing works

Synaptic and Network Mechanisms of Sparse and Reliable Visual Cortical Activity during Nonclassical Receptive Field Stimulation

Haider, B., Krause, M. R., Duque, A., Yu, Y., Touryan, J., Mazer, J. A., & McCormick, D. A. (2010) · Neuron · doi:10.1016/j.neuron.2009.12.005 · Cited by 263 · see citing works

Decoding the activity of neuronal populations in macaque primary visual cortex

Graf, A. B. A., Kohn, A., Jazayeri, M., & Movshon, J. A. (2011) · Nature Neuroscience · doi:10.1038/nn.2733 · Cited by 247 · see citing works

Synaptic Mechanisms Underlying Sparse Coding of Active Touch

Crochet, S., Poulet, J. F. A., Kremer, Y., & Petersen, C. C. H. (2011) · Neuron · doi:10.1016/j.neuron.2011.02.022 · Cited by 241 · see citing works

Multisensory Integration in Macaque Visual Cortex Depends on Cue Reliability

Morgan, M. L., DeAngelis, G. C., & Angelaki, D. E. (2008) · Neuron · doi:10.1016/j.neuron.2008.06.024 · Cited by 208 · see citing works

Bayesian decision theory as a model of human visual perception: Testing Bayesian transfer

MALONEY, L. T., & MAMASSIAN, P. (2009) · Visual Neuroscience · doi:10.1017/s0952523808080905 · Cited by 180 · see citing works

Inferring decoding strategies from choice probabilities in the presence of correlated variability

Haefner, R. M., Gerwinn, S., Macke, J. H., & Bethge, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3309 · Cited by 161 · see citing works

Marginalization in Neural Circuits with Divisive Normalization

Beck, J. M., Latham, P. E., & Pouget, A. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1706-11.2011 · Cited by 131 · see citing works

How Can Single Sensory Neurons Predict Behavior?

Pitkow, X., Liu, S., Angelaki, D. E., DeAngelis, G. C., & Pouget, A. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.033 · Cited by 129 · see citing works

Unifying account of visual motion and position perception

Kwon, O., Tadin, D., & Knill, D. C. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1500361112 · Cited by 121 · see citing works

Behavior and neural basis of near-optimal visual search

Ma, W. J., Navalpakkam, V., Beck, J. M., van den Berg, R., & Pouget, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2814 · Cited by 112 · see citing works

A Fast and Simple Population Code for Orientation in Primate V1

Berens, P., Ecker, A. S., Cotton, R. J., Ma, W. J., Bethge, M., & Tolias, A. S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1335-12.2012 · Cited by 107 · see citing works

Equivalence Proofs for Multi-Layer Perceptron Classifiers and the Bayesian Discriminant Function

Hampshire, J. B., & Pearlmutter, B. A. (1991) · Elsevier eBooks · doi:10.1016/b978-1-4832-1448-1.50023-8 · Cited by 104 · see citing works

Interactions between Speed and Contrast Tuning in the Middle Temporal Area: Implications for the Neural Code for Speed

Krekelberg, B., Wezel, R. J. A. V., & Albright, T. D. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.1983-06.2006 · Cited by 100 · see citing works

Random feedback weights support learning in deep neural networksPreprint

Lillicrap, T., Cownden, D., Tweed, D., & Akerman, C. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.0247 · Cited by 89 · see citing works

Solving the Problem of Negative Synaptic Weights in Cortical Models

Parisien, C., Anderson, C. H., & Eliasmith, C. (2008) · Neural Computation · doi:10.1162/neco.2008.07-06-295 · Cited by 83 · see citing works

Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization

Qamar, A. T., Cotton, R. J., George, R. G., Beck, J. M., Prezhdo, E., Laudano, A., Tolias, A. S., & Ma, W. J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1219756110 · Cited by 64 · see citing works

Learning Multisensory Integration and Coordinate Transformation via Density Estimation

Makin, J. G., Fellows, M. R., & Sabes, P. N. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003035 · Cited by 53 · see citing works

Towards a Neural Implementation of Causal Inference in Cue Combination

Ma, W. J., & Rahmati, M. (2013) · Multisensory Research · doi:10.1163/22134808-00002407 · Cited by 36 · see citing works

Linear Responses to Stochastic Motion Signals in Area MST

Heuer, H. W., & Britten, K. H. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00083.2007 · Cited by 22 · see citing works

Compressive neural representation of sparse, high-dimensional probabilitiesPreprint

Pitkow, Z. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.1800 · Cited by 5 · see citing works

Cognitive computational neuroscience

Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535 · see citing works

Comparing Bayesian and non-Bayesian accounts of human confidence reports

Adler, W. T., & Ma, W. J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006572 · Cited by 118 · see citing works

A neural basis of probabilistic computation in visual cortex

Walker, E. Y., Cotton, R. J., Ma, W. J., & Tolias, A. S. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0554-5 · Cited by 134 · see citing works

A diverse range of factors affect the nature of neural representations underlying short-term memory

Orhan, A. E., & Ma, W. J. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0314-y · Cited by 136 · see citing works

Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference

Echeveste, R., Aitchison, L., Hennequin, G., & Lengyel, M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0671-1 · Cited by 126 · see citing works

Toward an integration of deep learning and neuroscience

Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works

Bayesian Decision Models: A Primer

Ma, W. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.037 · Cited by 105 · see citing works

Computational Foundations of Natural Intelligence

van Gerven, M. A. J. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00112 · Cited by 72 · see citing works

A theory of learning to infer.

Dasgupta, I., Schulz, E., Tenenbaum, J. B., & Gershman, S. J. (2020) · Psychological Review · doi:10.1037/rev0000178 · Cited by 70 · see citing works

A confirmation bias in perceptual decision-making due to hierarchical approximate inference

Lange, R. D., Chattoraj, A., Beck, J. M., Yates, J. L., & Haefner, R. M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009517 · Cited by 60 · see citing works

Meta-learning of Sequential Strategies

Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv · doi:10.48550/arxiv.1905.03030 · Cited by 47 · see citing works

Post-injury pain and behaviour: a control theory perspective

Seymour, B., Crook, R. J., & Chen, Z. S. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00699-5 · Cited by 41 · see citing works

A neural network walks into a lab: towards using deep nets as models for human behaviorPreprint

Ma, W. J., & Peters, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02181 · Cited by 39 · see citing works

Sea surface temperature inversion model for infrared remote sensing images based on deep neural network

Ai, B., Wen, Z., Jiang, Y., Gao, S., & Lv, G. (2019) · Infrared Physics & Technology · doi:10.1016/j.infrared.2019.04.022 · Cited by 36 · see citing works

Bayesian encoding and decoding as distinct perspectives on neural coding

Lange, R. D., Shivkumar, S., Chattoraj, A., & Haefner, R. M. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01458-6 · Cited by 38 · see citing works

Bayesian modeling of the mind: From norms to neurons

Rescorla, M. (2020) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1540 · Cited by 30 · see citing works

Training recurrent networks to generate hypotheses about how the brain solves hard navigation problemsPreprint

Kanitscheider, I., & Fiete, I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.09059 · Cited by 30 · see citing works

A recurrent neural network model of prefrontal brain activity during a working memory task

Piwek, E. P., Stokes, M. G., & Summerfield, C. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011555 · Cited by 28 · see citing works

Multisensory Bayesian Inference Depends on Synapse Maturation during Training: Theoretical Analysis and Neural Modeling Implementation

Ursino, M., Cuppini, C., & Magosso, E. (2017) · Neural Computation · doi:10.1162/neco_a_00935 · Cited by 28 · see citing works

Rational thoughts in neural codes

Wu, Z., Kwon, M., Daptardar, S., Schrater, P., & Pitkow, X. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912336117 · Cited by 27 · see citing works

Uncertainty is maintained and used in working memory

Yoo, A. H., Acerbi, L., & Ma, W. J. (2021) · Journal of Vision · doi:10.1167/jov.21.8.13 · Cited by 25 · see citing works

Divisively Normalized Integration of Multisensory Error Information Develops Motor Memories Specific to Vision and Proprioception

Hayashi, T., Kato, Y., & Nozaki, D. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.1745-19.2019 · Cited by 34 · see citing works

Natural General Intelligence

Summerfield, C. (2022) · doi:10.1093/oso/9780192843883.001.0001 · Cited by 23 · see citing works

A recurrent neural network framework for flexible and adaptive decision making based on sequence learning

Zhang, Z., Cheng, H., & Yang, T. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008342 · Cited by 21 · see citing works

One Step Back, Two Steps Forward: Interference and Learning in Recurrent Neural Networks

Beer, C., & Barak, O. (2019) · Neural Computation · doi:10.1162/neco_a_01222 · Cited by 21 · see citing works

Decision prioritization and causal reasoning in decision hierarchies

Zylberberg, A. D. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009688 · Cited by 19 · see citing works

Efficient coding theory of dynamic attentional modulation

Młynarski, W. F., & Tkačik, G. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001889 · Cited by 19 · see citing works

Investigating the representation of uncertainty in neuronal circuits

Dehaene, G., Coen-Cagli, R., & Pouget, A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008138 · Cited by 16 · see citing works

Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inferencePreprint

Echeveste, R., Aitchison, L., Hennequin, G., & Lengyel, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/696088 · Cited by 17 · see citing works

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

Balasubramani, P. P., Moreno‐Bote, R., & Hayden, B. Y. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00022 · Cited by 14 · see citing works

The role of sensory uncertainty in simple contour integration

Zhou, Y., Acerbi, L., & Ma, W. J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006308 · Cited by 13 · see citing works

Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments

Foucault, C., & Meyniel, F. (2021) · eLife · doi:10.7554/elife.71801 · Cited by 14 · see citing works

Towards a cross-level understanding of Bayesian inference in the brain

Lin, C., & Garrido, M. I. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104649 · Cited by 15 · see citing works

A generative spiking neural-network model of goal-directed behaviour and one-step planning

Basanisi, R., Brovelli, A., Cartoni, E., & Baldassarre, G. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007579 · Cited by 12 · see citing works

Short-term memory by transient oscillatory dynamics in recurrent neural networks

Ichikawa, K., & Kaneko, K. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033193 · Cited by 8 · see citing works

Effective approximation of high-dimensional space using neural networks

Zheng, J. D., Wang, J., Chen, Y., Chen, S., Chen, J., Zhong, W., & Wu, W. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04038-2 · Cited by 9 · see citing works

Emergent human-like covert attention in feedforward convolutional neural networks

Srivastava, S., Wang, W. Y., & Eckstein, M. P. (2024) · Current Biology · doi:10.1016/j.cub.2023.12.058 · Cited by 8 · see citing works

Model Sharing in the Human Medial Temporal Lobe

Glitz, L., Juechems, K., Summerfield, C., & Garrett, N. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1978-21.2022 · Cited by 6 · see citing works

Integration of allocentric and egocentric visual information in a convolutional/multilayer perceptron network model of goal-directed gaze shifts

Khoozani, P. A., Bharmauria, V., Schütz, A., Wildes, R. P., & Crawford, J. D. (2022) · Cerebral Cortex Communications · doi:10.1093/texcom/tgac026 · Cited by 5 · see citing works

Bayesian inference is facilitated by modular neural networks with different time scales

Ichikawa, K., & Kaneko, K. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011897 · Cited by 6 · see citing works

Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliabilityPreprint

Aitchison, L., Hennequin, G., & Lengyel, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.08952 · Cited by 7 · see citing works

Population codes of prior knowledge learned through environmental regularities

Quax, S. C., Bosch, S., Peelen, M. V., & van Gerven, M. A. J. (2021) · Scientific Reports · doi:10.1038/s41598-020-79366-z · Cited by 5 · see citing works

Solving the causal inference problem

Noppeney, U. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.09.004 · Cited by 6 · see citing works

The role of sensory uncertainty in simple contour integrationPreprint

Zhou, Y., Acerbi, L., & Ma, W. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/350082 · Cited by 3 · see citing works

One step back, two steps forward: interference and learning in recurrent neural networksPreprint

Beer, C., & Barak, O. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09603 · Cited by 4 · see citing works

Neural networks trained with high-dimensional functions approximation data in high-dimensional space

Zheng, J. D., Wang, J., Chen, Y., Chen, S., Chen, J., Zhong, W., & Wu, W. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-211417 · Cited by 3 · see citing works

Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference

Echeveste, R., Ferrante, E., Milone, D. H., & Samengo, I. (2021) · Network Neuroscience · doi:10.1162/netn_a_00219 · Cited by 3 · see citing works

Modeling Cognitive Processes with Neural Reinforcement LearningPreprint

Bosch, S., Seeliger, K., & van Gerven, M. A. J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/084111 · Cited by 3 · see citing works

Training stochastic stabilized supralinear networks by dynamics-neutral growthPreprint

Soo, W. W. M., & Lengyel, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.19.512820 · Cited by 3 · see citing works

Dynamical Mechanism of Sampling-Based Probabilistic Inference Under Probabilistic Population Codes

Ichikawa, K., & Kataoka, A. (2022) · Neural Computation · doi:10.1162/neco_a_01477 · Cited by 2 · see citing works

The value of action

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.003.0006 · Cited by 1 · see citing works

Navigating uncertainty: Safety solutions for autonomous vehicle integration into mixed-mode mobility

Cooke, J. R. E., Lingam, S. N., Alavian, P., Collast, C., de Croon, G., Franssen, M., Hagenaars, J. J., Kober, J., Morsink, P., Liu, K., et al. (2026) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2026.102141 · Cited by 1 · see citing works

The Role of the Reward Recognition Network in Understanding Motivation

Wasserman, T. H., & Wasserman, L. D. (2020) · Springer eBooks · doi:10.1007/978-3-030-58724-6_4 · Cited by 1 · see citing works

A Feedforward Network with a Few Million Neurons Learns from Images to Covertly Attend to Contextual Cues like Human and Bayesian Ideal ObserversPreprint

Srivastava, S., Wang, W. Y., & Eckstein, M. P. (2022) · Research Square · doi:10.21203/rs.3.rs-1805213/v1 · Cited by 1 · see citing works

Multisensory integration across reference frames with additive feed-forward networksPreprint

Farahmandi, A., Khoozani, P. A., & Blohm, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.25.625309 · Cited by 1 · see citing works

Research on Sparsity of Output Synapses in Echo State Networks

Mu, X., Li, L., & He, X. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/1984524

Machine Learning for Optimal Economic Design

Dütting, P., Feng, Z., Golowich, N., Narasimhan, H., Parkes, D. C., & Ravindranath, S. S. (2019) · Studies in economic design · doi:10.1007/978-3-030-18050-8_70

Attentional dynamics of efficient codesPreprint

Młynarski, W. F., & Tkačik, G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.29.437459

A bridge between physiological and perceptual views of autism by means of sampling-based Bayesian inferencePreprint

Echeveste, R., Ferrante, E., Milone, D. H., & Samengo, I. (2021) · arXiv (Cornell University) · doi:10.12751/nncn.bc2021.p050

Dynamical Mechanism of Sampling-based Stochastic Inference under Probabilistic Population CodesPreprint

Ichikawa, K., & Kataoka, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05591

Model sharing in the human medial temporal lobePreprint

Glitz, L., Juechems, K., Summerfield, C., & Garrett, N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.23.449588

A Feedforward Convolutional Neural Network Learns from Images to Covertly Attend to Contextual Cues Like Human and Bayesian Ideal ObserversPreprint

Srivastava, S., Wang, W. Y., & Eckstein, M. P. (2021) · doi:10.31234/osf.io/tymfn

A picture of the mind

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.003.0008

The language of thought

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.003.0003

The control of memory

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.003.0007

The nature of intelligence

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.003.0002

The structure of knowledge

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.003.0004

Copyright Page

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.002.0003

Preface

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.002.0004

Abbreviations

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.002.0006

The problem of abstraction

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.003.0005

Turing’s question

Summerfield, C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192843883.003.0001

Die Rolle des Belohnungserkennungsnetzwerks beim Verständnis von Motivation

Wasserman, T. H., & Wasserman, L. D. (2024) · Springer eBooks · doi:10.1007/978-3-031-48004-1_4

Attention and the Role of the Reward Recognition Network

Wasserman, T. H., & Wasserman, L. D. (2024) · Springer eBooks · doi:10.1007/978-3-031-78732-4_8

Computational models and neural mechanisms of causal inference in multisensory integration

Cheng, Y., Yuan, X., Jiang, Y., & Cao, Y. (2025) · Scientia Sinica Vitae · doi:10.1360/ssv-2024-0160

Learning and inference with correlated neural variability

Qi, Y., Zhu, Z., Wei, Y., Cao, L., Wang, Z., Zhang, J., Lu, W., & Feng, J. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf284

Predictive learning induces Bayesian cognitive maps in the hippocampusPreprint

Kim, Y., & Kang, Y. H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.03.729991

The Inevitability of Probability: Probabilistic Inference in Generic Neural Networks Trained with Non-Probabilistic FeedbackPreprint

Orhan, A. E., & Ma, W. J. (2016) · arXiv (Cornell University) · Cited by 3 · see citing works

Mechanisms of probabilistic inference in neural networks

Ichikawa, K. (2020)

Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliabilityPreprint

Aitchison, L., Hennequin, G., & Lengyel, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.08952 · Cited by 7 · see citing works

Inference by Reparameterization in Neural Population CodesPreprint

Raju, R. V., & Pitkow, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06544 · Cited by 20 · see citing works

Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models

Beck, J. M., Pouget, A., & Heller, K. (2012) · Archive ouverte UNIGE (University of Geneva) · Cited by 34 · see citing works