Kreiman Lab News

Statistical Learning Theory and Applications

MIT Class 9.520 / 6.860 (Fall 2021)

Class 19 and Class 20: Real neural networks and the ventral stream

By Thomas Serre, Gabriel Kreiman and Tomaso Poggio

Slides Nov 16, 2021

Kreiman Lab News

Contextual reasoning in man and machines

Flash presentation by Prof. Gabriel Kreiman in the SciFoo conference 2021

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) arXiv 2104.02215. PDF

Zhang M, Tseng C, Kreiman G. (2020) Putting visual object recognition in context. CVPR. PDF

Kreiman Lab News

CBMM Panel Discussion: Should models of cortex be falsifiable?

Title: Should models of cortex be falsifiable?

Presenters: Prof. Tomaso Poggio (MIT)
Prof. Gabriel Kreiman (Harvard Medical School, BCH)
Prof. Thomas Serre (Brown U.)
Discussants: Prof. Leyla Isik (JHU), Martin Schrimpf (MIT), Michael Lee (MIT), Prof. Susan Epstein (Hunter CUNY), and Jenelle Feather (MIT)
Moderator: Prof. Josh McDermott (MIT)

Date: December 1, 2020 3:00 pm- 5:00 pm

Abstract:  Deep Learning architectures designed by engineers and optimized with stochastic gradient descent on large image databases have become de facto models of the cortex. A prominent example is vision. What sorts of insights are derived from these models? Do the performance metrics reveal the inner workings of cortical circuits or are they a dangerous mirage? What are the critical tests that models of cortex should pass?We plan to discuss the promises and pitfalls of deep learning models contrasting them with earlier models (VisNet, HMAX,…) which were developed from the ground up following neuroscience data to account for critical properties of scale + position invariance and selectivity of primate vision.

Kreiman Lab News

How brain computations can inspire new paths in AI

Lecture presented at the BMM Summer School 2021 by Gabriel Kreiman

Kreiman Lab News

Congratulations Matthias Tsai!

Matthias Tsai
Matthias Tsai

Matthias Tsai successfully defended his Master’s Thesis [09/10/2018]

Matthias Tsai. Neural circuits of visual pattern completion. Ecole Polythechnique Federale de Laussanne (EPFL) (2018).

Click here for a copy of his thesis work

See also the following studies:

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

Tang H, Kreiman G. (2017). Recognition of occluded objects. In Computational and Cognitive Neuroscience of Vision. (ed Zhao, Q). Singapore: Springer-Verlag | PDF

Tang H, Buia C, Madhavan R, Madsen J, Anderson W, Crone N, Kreiman G. (2014). Spatiotemporal dynamics underlying object completion in human ventral visual cortex. Neuron, 83:736-748 | PDF