Categories
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

Categories
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

Categories
Kreiman Lab News

Mind, Brain, and Behavior Poster Showcase

Mind, Brain, and Behavior poster showcase. William James Hall Library.

Duncan Stothers on “A Deep Learning Model for CNS Development” and Farahnaz Wick on “A computational model of hybrid visual search

Categories
Kreiman Lab News

Finding any Waldo: Zero-shot invariant visual search

Finding any Waldo: Zero-shot invariant visual search. Gabriel Kreiman @ Systems Neuroscience Club.

Categories
Kreiman Lab News

Finding any Waldo: zero-shot invariant and efficient visual search

Mengmi Zhang proposed a biologically inspired computational algorithm that can find novel objects in scenes without any prior training. Nature Communications 2018.

Zhang M, Feng J, Ma KT, Lim JH, Zhao Q, Kreiman G. (2018) Finding any Waldo: zero-shot invariant and efficient visual search. Nature Communications, 9:3730.

PDF

Resources