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

Virtual Brains, Minds and Machines Summer Course

Woods Hole, Massachussetts

Brains, Minds and Machines

Directors: Gabriel Kreiman, Boris Katz, Tomaso Poggio

August 10, 2020 through August 21, 2020
Please follow this link to the course web site

Kreiman Lab News

The brain’s operating system – CBMM Module 2

The brain’s operating system. Discussion of CBMM Module 2 @ Harvard led by Gabriel Kreiman, Will Xiao, Jerry Wang, Xavier Boix, Jerry Wang

Kreiman Lab News

Brains, Minds and Machines Summer Course – 2018

Brains, Minds and Machines Summer Course. Woodshole, MA 08/09/2018 — 08/30/2018

Directors: Gabriel Kreiman, Children’s Hospital, Harvard Medical School; Boris Katz, Massachusetts Institute of Technology; and Tomaso Poggio, Massachusetts Institute of Technology
Location: Woods Hole, MA.
Course Dates: Aug. 9th – Aug. 30th, 2018
Application deadline: April 9, 2018
ScheduleBMM Summer Course 2018 Schedule

Course Description

The basis of intelligence – how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines – is arguably the greatest problem in science and technology. To solve it, we will need to understand how human intelligence emerges from computations in neural circuits, with rigor sufficient to reproduce similar intelligent behavior in machines. Success in this endeavor ultimately will enable us to understand ourselves better, to produce smarter machines, and perhaps even to make ourselves smarter. Today’s AI technologies, such as Watson and Siri, are impressive, but their domain specificity and reliance on vast numbers of labeled examples are obvious limitations; few view this as brain-like or human intelligence. The synergistic combination of cognitive science, neurobiology, engineering, mathematics, and computer science holds the promise to build much more robust and sophisticated algorithms implemented in intelligent machines. The goal of this course is to help produce a community of leaders that is equally knowledgeable in neuroscience, cognitive science, and computer science and will lead the development of true biologically inspired AI.

The class discussions will cover a range of topics, including:

  • Neuroscience: neurons and models
  • Computational vision
  • Biological vision
  • Machine learning
  • Bayesian inference
  • Planning and motor control
  • Memory
  • Social cognition
  • Inverse problems & well-posedness
  • Audition and speech processing
  • Natural language processing

These discussions will be complemented in the first week by MathCamps and NeuroCamps, to refresh the necessary background. Throughout the course, students will participate in workshops and tutorials to gain hands-on experience with these topics.

Core presentations will be given jointly by neuroscientists, cognitive scientists, and computer scientists. Lectures will be followed by afternoons of computational labs, with additional evening research seminars. To reinforce the theme of collaboration between (computer science + math) and (neuroscience + cognitive science), exercises and projects often will be performed in teams that combine students with both backgrounds.

The course will culminate with student presentations of their projects. These projects provide the opportunity for students to work closely with the resident faculty, to develop ideas that grow out of the lectures and seminars, and to connect these ideas with problems from the students’ own research at their home institutions.

This course aims to cross-educate computer engineers and neuroscientists; it is appropriate for graduate students, postdocs, and faculty in computer science or neuroscience. Students are expected to have a strong background in one discipline (such as neurobiology, physics, engineering, and mathematics). Our goal is to develop the science and the technology of intelligence and to help train a new generation of scientists that will leverage the progress in neuroscience, cognitive science, and computer science. The course is limited to 35 students.

Link to course

Kreiman Lab News

MSRP Summer Poster Session

August 11th, 2017. MSRP Summer Poster Session. MIT.

Image of Candace Ross