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CBMM: The brain’s operating system. Research Update 1

We hope that you will be able to join next week’s research meeting with presentations by Trenton Bricken and Will Xiao, Kreiman Lab.

CBMM Research Meeting

Title: Module 2 Research presentation

Speaker/s: Trenton Bricken and Will Xiao, Kreiman Lab

Date/Time: October 19, 2021, 4:00 pm to 5:30 pm ET

RSVP for post meeting social

Will Xiao
Will Xiao

Will Xiao‘s presentation:

Title: What you see is what IT gets: Responses in primate visual cortex during natural viewing

Abstract: How does the brain support our ability to see? Studies of primate vision have typically focused on controlled viewing conditions exemplified by the rapid serial visual presentation (RSVP) task, where the subject must hold fixation while images are flashed briefly in randomized order. In contrast, during natural viewing, eyes move frequently, guided by subject-initiated saccades, resulting in a sequence of related sensory input. Thus, natural viewing departs from traditional assumptions of independent and unpredictable visual inputs, leaving it an open question how visual neurons respond in real life.

We recorded responses of interior temporal (IT) cortex neurons in macaque monkeys freely viewing natural images. We first examined responses of face-selective neurons and found that face neurons responded according to whether individual fixations were near a face, meticulously distinguishing single fixations. Second, we considered repeated fixations on very close-by locations, termed ‘return fixations.’ Responses were more similar during return fixations, and again distinguished individual fixations. Third, computation models could partially explain neuronal responses from an image crop centered on each fixation.

 These results shed light on how the IT cortex does (and does not) contribute to our daily visual percept: a stable world despite frequent saccades.

Trenton Bricken
Trenton Bricken

Trenton Bricken‘s presentation:

Title: Attention Approximates Sparse Distributed Memory

Abstract: While Attention has come to be an important mechanism in deep learning, it emerged out of a heuristic process of trial and error, providing limited intuition for why it works so well. Here, we show that Transformer Attention closely approximates Sparse Distributed Memory (SDM), a biologically plausible associative memory model, under certain data conditions. We confirm that these conditions are satisfied in pre-trained GPT2 Transformer models. We discuss the implications of the Attention-SDM map and provide new computational and biological interpretations of Attention.

The Fall 2021 CBMM Research Meetings will be hosted in a hybrid format. Please see the information included below regarding attending the event either in-person or remotely via Zoom connection

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Kreiman Lab News

Congratulations to Shashi Kant Gupta on his thesis!

Shashi Gupta
Shashi Gupta

An integrated computational model of visual search combining eccentricity, bottom-up and top-down cues. India Institute of Technology Kanpur (2021).

Read his thesis here

Read his NeurIPS 2021 paper related to his thesis work. Visual search asymmetry: DeepNets and Humans share similar inherent biases. Gupta et al, NeurIPS 2021

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Kreiman Lab News

NIPS 2015

Neural Information Processing Systems Review

NIPS 2015

Crowded conference room for a presentation

See also:

Singer JM, Madsen JR, Anderson WS, Kreiman G. (2015). Sensitivity to Timing and Order in Human Visual CortexJournal of Neurophysiology, 113:1656-1669.

Singer J, Kreiman G. (2014). Short Temporal Asynchrony Disrupts Visual Object Recognition. Journal of Vision, 12,14

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Kreiman Lab News

NIPS Symposium 2015: Brains, Minds and Machines

NIPS Symposium: Brains, Minds and Machines. Montreal, Canada.

Gabriel Kreiman: The roles of recurrent and feedback computations in cortex.

Gabriel Kreiman: Predicting episodic memory formation.

Views and Reviews on Symposium by Gabriel Kreiman

Symposium Program

Lecture by Prof. Kreiman at NIPS 2015