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Kreiman Lab News

Trenton Bricken presents his work at NeurIPS

SDM
Relationship between sparse distributed memory and transformer architectures

Attention approximates sparse distributed memory

Bricken T and Pehlevan C. NeurIPS 2021

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Kreiman Lab News

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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Kreiman Lab News

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.

Video presentation

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.

Video presentation

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