Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Dellaferrera and Kreiman, ICML 2022
Tag: computational neuroscience
Categories
What is a face neuron?
Bardon A, Xiao W, Ponce CR, Livingstone MS, Kreiman G. Face neurons encode nonsemantic features. Proceedings of the National Academy of Sciences of the United States of America 119, e2118705119, doi:10.1073/pnas.2118705119 PDF
See also related work:
Face Neurons Encode More Than Faces. Harvard Brain Initiative News. The Interneuron. Jun 29, 2022
Xiao W. and Kreiman G. (2020). XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLoS Computational Biology 16(6): e1007973. PDF
Ponce C.R., Xiao W., Schade P.F., Hartmann T.S., Kreiman G., Livingstone M. (2019). Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell, 177:999-1009. PDF
Bill Lotter explains the PredNet neural network architecture
See publication:
Lotter W, Kreiman G, Cox D. (2020) A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Intelligence, 2:210-219 PDF
See also:
Tang H, Schrimpf M, Lotter W, Moerman C, Paredes A, Ortega Caro J, Hardesty W, Cox D, Kreiman G. (2018) Recurrent computations for visual pattern completion. PNAS, 115:8835-8840. PDF
Lotter W, Kreiman, G, Cox, D. (2017) Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. International Conference on Learning Representations (ICLR), Toulon, France. PDF
Lotter, W, Kreiman, G, Cox, D. (2016.) Unsupervised representation learning using predictive generative works. International Conference on Learning Representations (ICLR), Puerto Rico. PDF
Harvard, Spring 2022
Class starts on Tuesday Jan 25th, 2022, 3-5pm
Northwest Building B108
This is a seminar-style course which provides a foundational overview of key ideas in Computational Neuroscience and the study of Biological Intelligence. At the same time, the course will connect the study of brains to the blossoming and rapid development of ideas in Artificial Intelligence. Topics covered include the biophysics of computation, neural networks, machine learning, bayesian models, theory of learning, deep convolutional networks, generative adversarial networks, neural coding, control and dynamics of neural activity, applications to brain-machine interfaces, connectomics, among others. Lectures will be taught by leading Harvard experts in the field.
Beijing, Apr 20, 2021
Prof. Gabriel Kreiman’s lecture titled:
How brain computations can inspire new paths in AI
See also:
Bomatter P, Zhang M, Karev D, Madan S, Tseng C, Kreiman G (2021). When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. International Conference on Computer Vision (ICCV) PDF
Casper S, Boix X, D’Amario V, Guo L, Schrimpf M, Vinken K, Kreiman G. (2021). Frivolous Units: Wider Networks are not really that Wide. AAAI Conference on Artificial Intelligence PDF
Kreiman G and Serre T (2020). Beyond the feedforward sweep: feedback computations in the visual cortex. Ann N Y Acad Sci. 1464:222-241. PDF
Jacquot V, Ying J, Kreiman G. (2020) Can Deep Learning Recognize Subtle Human Activities? Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 14244-14253. arXiv 2003.13852
Zhang M, Tseng C, Kreiman G. (2020) Putting visual object recognition in context. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 12982-12991. arXiv:1911.07349