The Spring 2022 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

Please note, MIT is requiring that all attendees, including MIT COVIDpass users, sign-in to the event prior to entering the auditorium.

Abstract: We can computationally approximate a visual object's identity from the distributed neural activity patterns across a series of hierarchically connected brain areas (e.g., V4, IT) in the primate ventral stream. However, testing whether these circuits indeed play a causal role requires targeted neural perturbation strategies that enable discrimination amongst competing models. Here we probed the role of macaque V4 and its primary feedforward target, the inferior temporal (IT) cortex, during object recognition. We combined DREADDs-based chemogenetic inhibition of V4 neurons with large-scale electrophysiology in V4 and IT while simultaneously measuring the monkeys' image-by-image object recognition behavior. Our results provide causal evidence linking the ventral stream hierarchy with core object recognition behavior. Also, in addition, to providing a “yes” vs. “no” answer to the involvement of a brain area in a behavior, we demonstrate how our approach allows us to use direct causal perturbation data to discriminate amongst competing mechanistic brain models.​

Link to attend talk remotely via Zoom:

Zoom link: https://mit.zoom.us/j/97558618808?pwd=b2RTVkZLUmpIL2Y3Szg2TG9RT1BoZz09

Guidance for attending in-person:

MIT attendees:
MIT attendees will need to be registered via the MIT COVIDpass system to have access to MIT Building 46.
Please visit URL https://covidpass.mit.edu/ for more information regarding MIT COVIDpass.

Non-MIT attendees:

MIT is currently welcoming visitors to attend talks in person. All visitors to the MIT campus are required to follow MIT COVID19 protocols, see URL https://now.mit.edu/policies/campus-access-and-visitors/.  Specifically, visitors are required to wear a face-covering/mask while indoors and use the new MIT TIM Ticket system for accessing MIT buildings. Per MIT’s event policy, use of the Tim Tickets system is required for all indoor events; for information about this and other current MIT policies, visit MIT Now.

Link to this event's MIT TIM TICKET: https://tim-tickets.atlas-apps.mit.edu/CmoDwauHJkuqMSBo8

To access MIT Bldg. 46 with a TIM Ticket, please enter the building via the McGovern/Main Street entrance - 524 Main Street (on GPS). This entrance is equipped with a QR reader that can read the TIM Ticket. A map of the location of, and an image of, this entrance is available at URL: https://mcgovern.mit.edu/contact-us/

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Information collected by the TIM Ticket:

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Host: Prof. Gabriel Kreiman (Children's, Harvard)

Speaker: Mengmi Zhang
Title: The combination of eccentricity, bottom-up, and top-down cues explain conjunction and asymmetric visual search
Abstract: 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. To compare the model against human behavior, we consider nine foundational experiments that demonstrate two intriguing principles of visual search: (i) asymmetric search costs when looking for a certain object A among distractors B versus the reverse situation of locating B among distractors A; (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. Furthermore, the model can generalize to real-world search tasks in complex natural environments. 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.

Speaker: Jie Zheng
Title: Neurons detect cognitive boundaries to structure episodic memories in humans
Abstract: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.

Speaker: Will Xiao
Title: Adversarial images for the Primate Brain
Abstract: 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.

Zoom link: https://mit.zoom.us/j/92930528137?pwd=Z2ZiQnl3RjFGYzAvdXpZbkppaG5Mdz09

Abstract:  Large-scale vision benchmarks have driven---and often even defined---progress in machine learning. However, these benchmarks are merely proxies for the real-world tasks we actually care about. How well do our benchmarks capture such tasks?

In this talk, I will discuss the alignment between our benchmark-driven ML paradigm and the real-world uses cases that motivate it. First, we will explore examples of biases in the ImageNet dataset, and how state-of-the-art models exploit them. We will then demonstrate how these biases arise as a result of design choices in the data collection and curation processes.

Throughout, we illustrate how one can leverage relatively standard tools (e.g., crowdsourcing, image processing) to quantify the biases that we observe.

Based on joint works with Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Jacob Steinhardt, Dimitris Tsipras and Kai Xiao.

Speaker bio.: Prof. Mądry is the Director of the MIT Center for Deployable Machine Learning, the Faculty Lead of the CSAIL-MSR Trustworthy and Robust AI Collaboration, a Professor of Computer Science in the MIT EECS Department, and member of both CSAIL and the Theory of Computation group.

His research spans algorithmic graph theory, optimization and machine learning. In particular, I have a strong interest in building on the existing machine learning techniques to forge a decision-making toolkit that is reliable and well-understood enough to be safely and responsibly deployed in the real world.

Research lab website: http://madry-lab.ml/

This research meeting will be hosted remotely via Zoom.

Zoom link: https://mit.zoom.us/j/97149851234?pwd=czZGbWFhYU41MXlLUzh4ZVZVUmt1Zz09
Passcode: 644798

Duncan Stothers-

Title: Turing's Child Machine: A Deep Learning Model of Neural Development

Abstract:

Turing recognized development’s connection to intelligence when he proposed engineering a ‘child machine’ that becomes intelligent through a developmental process, instead of top-down hand-designing intelligence into an ‘adult machine’. We now know from neurobiology that the most important developmental process is the ‘critical period’ where the architecture (equivalently connectome or topology) expands in a random way and then prunes itself down based on activity. The computational role of this process is unknown, but we know it is connected to intelligence because deprivation during this period has permanent negative effects later in life. Further, the fact the connectome changes during this period through ‘architecture learning’ in addition to the synaptic weights changing through ‘synaptic weight learning’, set it apart from deep learning AI research where the architecture is hand designed and stays fixed during learning and only ‘synaptic weight learning’ takes place. To understand development’s connection to biological and artificial intelligence we model the critical period by adding random expansion and activity based pruning steps to deep neural network training. Results suggest the critical period is as an unsupervised architecture search process that finds exponentially small architectures that generalize well. Resultant architectures from this process also show similarities to hand-designed ones. 

Will Xiao-

Title: Uncovering preferred stimuli of visual neurons using generative neural networks

Abstract:

What information do neurons represent? This is a central question in neuroscience. Ever since Hubel and Wiesel discovered that neurons in primary visual cortex (V1) respond preferentially to bars of certain orientations, investigators have searched for preferred stimuli to reveal information encoded by neurons, leading to the discovery of cortical neurons that respond to specific motion directions (Hubel, 1959), color (Michael, 1978), binocular disparity (Barlow et al., 1967), curvature (Pasupathy & Connor, 1999), complex shapes such as hands or faces (Desimone et al., 1984; Gross et al., 1972), and even variations across faces (Chang & Tsao, 2017).

However, the classic approach for defining preferred stimuli depends on using a set of hand-picked stimuli, limiting possible answers to stimulus properties chosen by the investigator. Instead, we wanted to develop a method that is as general and free of investigator bias as possible. To that end, we used a generative deep neural network (Dosovitskiy & Brox, 2016) as a vast and diverse hypothesis space. A genetic algorithm guided by neuronal preferences searched this space for stimuli.

We evolved images to maximize firing rates of neurons in macaque inferior temporal cortex and V1. Evolved images often evoked higher firing rates than the best of thousands of natural images. Furthermore, evolved images revealed neuronal selective properties that were sometimes consistent with existing theories but sometimes also unexpected.

This generative evolutionary approach complements classical methods for defining neuronal selectivities, serving as an independent test and a hypothesis-generating tool. Moreover, the approach has the potential for uncovering internal representations in any modality that can be captured by generative neural networks.

Nimrod Shaham-

Title: Continual learning and replay in a sparse forgetful Hopfield model

Abstract:

The brain has a remarkable ability to deal with an endless, continuous stream of information, while storing new memories and learning to perform new tasks. This is done without losing previously learned knowledge, which can be stored to timescales of the order of the animal’s life. In contrast, current artificial neural network models suffer from limited capacity (associative memory network models) and acute loss of performance in previously learned tasks after learning new ones (deep neural networks). Overcoming this limitation, known as catastrophic interference, is one of the main challenges in machine learning and theoretical neuroscience.

Here, we study a recurrent neural network that continually learns and stores sparse patterns of activity, while forgetting old ones (a palimpsestic model). Time dependent forgetting is incorporated as a decay of old memories’ contributions to the weight matrix. We calculate the required forgetting rate in order to avoid catastrophic interference, and find the optimal decay rate that gives maximal number of retrievable memories. Then, we introduce replay to the system, in the form of reappearance of previously stored patterns, and calculate the enhancement of time for which a memory is retrievable due to different patterns of replays. Our model reveals in a tractable and illuminating way how a recurrent neural network can learn continuously and store selected information for lifelong timescales.