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CBMM Module 2. Research Update

February 16, 2021. 4-5:30pm ET. Speakers: Mengmi Zhang, Jie Zheng, and Will Xiao

Mengmi Zhang. The combination of eccentricity, bottom-up, and top-down cues explains conjunction and asymmetric visual search
Visual search requires complex interactions between visual processing, eye movements, object recognition, memory, and decision making. Elegant psychophysics experiments have described the task characteristics and stimulus properties that facilitate or slow down visual search behavior. En route towards a quantitative framework that accounts for the mechanisms orchestrating visual search, here we propose an image-computable biologically-inspired computational model that takes a target and a search image as inputs and produces a sequence of eye movements. We consider nine foundational experiments that demonstrate two intriguing principles of visual search: (i) asymmetric search costs; (ii) the increase in search costs associated with feature conjunctions. The proposed computational model has three main components, an eccentricity-dependent visual feature processor learnt through natural image statistics, bottom-up saliency, and target-dependent top-down cues. Without any prior exposure to visual search stimuli or any task-specific training, the model demonstrates the essential properties of search asymmetries and slower reaction time in feature conjunction tasks. The proposed model unifies previous theoretical frameworks into an image-computable architecture that can be directly and quantitatively compared against psychophysics experiments and can also provide a mechanistic basis that can be evaluated in terms of the underlying neuronal circuits.

Will Xiao. Adversarial images for the Primate Brain
Deep artificial neural networks have been proposed as a model of primate vision. However, these networks are vulnerable to adversarial attacks, whereby introducing minimal noise can fool networks into misclassifying images. Primate vision is thought to be robust to such adversarial images. We evaluated this assumption by designing adversarial images to fool primate vision. To do so, we first trained a model to predict responses of face-selective neurons in macaque inferior temporal cortex. Next, we modified images, such as human faces, to match their model-predicted neuronal responses to a target category, such as monkey faces, with a small budget for pixel value change. These adversarial images elicited neuronal responses similar to the target category. Remarkably, the same images fooled monkeys and humans at the behavioral level. These results call for closer inspection of the adversarial sensitivity of primate vision, and show that a model of visual neuron activity can be used to specifically direct primate behavior.

Jie Zheng. Neurons detect cognitive boundaries to structure episodic memories in humans
While experience is continuous, memories are organized as discrete events. Cognitive boundaries are thought to segment experience and structure memory, but how this process is implemented remains unclear. We recorded the activity of single neurons in the human medial temporal lobe during the formation and retrieval of memories with complex narratives. Neurons responded to abstract cognitive boundaries between different episodes. Boundary-induced neural state changes during encoding predicted subsequent recognition accuracy but impaired event order memory, mirroring a fundamental behavioral tradeoff between content and time memory. Furthermore, the neural state following boundaries was reinstated during both successful retrieval and false memories. These findings reveal a neuronal substrate for detecting cognitive boundaries that transform experience into mnemonic episodes and structure mental time travel during retrieval.

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Kreiman Lab News

Now on MIT’s Open Courseware! – Brains, Minds and Machines Summer Course 2018

Brains, Minds and Machines Summer Course now on MIT’s Open Courseware

See also the CBMM Learning Hub

See also the courses taught by Professor Kreiman

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Kreiman Lab News

McGovern Institute for Brain Research at MIT: Highlight on CBMM

McGovern Institute for Brain Research at MIT – BrainScan: Spring 2018, Issue 44. Engineering Intelligence

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Kreiman Lab News

The New Center for Brains, Minds and Machines

CBMM

Introduction to the new Center for Brains, Minds and Machines

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Kreiman Lab News

$25 million NSF grant to team including 6 CBS faculty

Center for Brain Science – $25 million NSF grant to team including 6 CBS faculty.