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CBMM: The brain’s operating system. Research Update 2

We hope that you will be able to join next week’s research meeting with presentations by Mengmi Zhang and Jie Zheng, Kreiman Lab.

CBMM Research Meeting

Title: Module 2 Research presentation

Date/Time: October 26, 2021, 4:00 pm to 5:30 pm ET

Jie Zheng
Jie Zheng

Jie Zheng‘s presentation (in person):

Title: Neurons that structure memories of ordered experience in human

 Abstract: The process of constructing temporal associations among related events is essential to episodic memory. However, what neural mechanism helps accomplish this function remains unclear. To address this question, we recorded single unit activity in humans while subjects performed a temporal order memory task. During encoding, subjects watched a series of clips (i.e., each clip consisted of 4 events) and were later instructed to retrieve the ordinal information of event sequences. We found that hippocampal neurons in humans could index specific orders of events with increased neuronal firings (i.e., rate order cells) or clustered spike timing relative to theta phases (i.e., phase order cells), which are transferrable across different encoding experiences (e.g., different clips). Rate order cells also increased their firing rates when subjects correctly retrieved the temporal information of their preferred ordered events. Phase order cells demonstrated stronger phase precessions at event transitions during encoding for clips whose ordinal information was subsequently correct retrieved. These results not only highlight the critical role of the hippocampus in structuring memories of continuous event sequences but also suggest a potential neural code representing temporal associations among events.

Mengmi Zhang
Mengmi Zhang

Mengmi Zhang‘s [virtual] presentation:

Title: Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases

Abstract: Visual search is a ubiquitous and often challenging daily task, exemplified by looking for the car keys at home or a friend in a crowd. An intriguing property of some classical search tasks is an asymmetry such that finding a target A among distractors B can be easier than finding B among A. To elucidate the mechanisms responsible for asymmetry in visual search, we propose a computational model that takes a target and a search image as inputs and produces a sequence of eye movements until the target is found. The model integrates eccentricity-dependent visual recognition with target-dependent top-down cues. We compared the model against human behavior in six paradigmatic search tasks that show asymmetry in humans. Without prior exposure to the stimuli or task-specific training, the model provides a plausible mechanism for search asymmetry. We hypothesized that the polarity of search asymmetry arises from experience with the natural environment. We tested this hypothesis by training the model on an augmented version of ImageNet where the biases of natural images were either removed or reversed. The polarity of search asymmetry disappeared or was altered depending on the training protocol. This study highlights how classical perceptual properties can emerge in neural network models, without the need for task-specific training, but rather as a consequence of the statistical properties of the developmental diet fed to the model. Our work will be presented in the upcoming Neurips conference, 2021.

See also: Gupta SK, Zhang M, Wu CC, Wolfe JM, Kreiman G (2021). Visual search asymmetry: deep nets and humans share similar inherent biases. NeurIPS PDF

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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.

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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

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Brains, Minds and Machines Summer Course 2019

Aug 8 – Aug 29, 2019. Woods Hole, MA

BMM Summer Course 2019

Brains, Minds and Machines
Directors: Gabriel Kreiman, Children’s Hospital, Harvard Medical School; Boris Katz, Massachusetts Institute of Technology; and Tomaso Poggio, Massachusetts Institute of Technology
Location: Marine Biological Laboratory, in Woods Hole, MA.
Course Dates: August 8 – August 29, 2019
Application: http://www.mbl.edu/education/courses/brains-minds-and-machines/
Application deadline: April 8, 2019
Schedule:  PDF (8/13/2019)

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.

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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