Kreiman Lab News

Giorgia Dellaferrera gives a talk at ICML 2022

Dellaferrera at ICML
Giorgia Dellaferrera giving a talk at ICML

Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Dellaferrera and Kreiman, ICML 2022

Link to paper

Kreiman Lab News

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

Kreiman Lab News

PredNet: A deep neural network architecture for predictive coding

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

GitHub Page

Kreiman Lab News

Neuro 140/240: Biological and Artificial Intelligence

Harvard, Spring 2022

Class starts on Tuesday Jan 25th, 2022, 3-5pm

Northwest Building B108

Link to class website

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.

Kreiman Lab News

At the interface of brain and machine

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