Dr. Richard Lange
2024-07-25 15:00 - 17:00 UTC | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: “What Bayes can and cannot tell us about the neuroscience of vision”
Nikolaus Kriegeskorte’s Group is hosting Dr.Richard Lange, Assistant Professor in the Department of Computer Science at Rochester Institute of Technology. He will be giving a talk at Zuckerman Institute.

Continual Learning Working Group Talk
2024-07-30 15:00 - 17:00 UTC | CEPSR 620, Schapiro 530 W. 120th St
Title: Continual learning, machine self-reference, and the problem of problem-awareness
Abstract: Continual learning (CL) without forgetting has been a long-standing problem in machine learning with neural networks. Here I will bring a new perspective by looking at learning algorithms (LAs) as memory mechanisms with their own decision making problem. I will present a natural solution to CL under this view: instead of handcrafting such LAs, we metalearn continual in-context LAs using self-referential weight matrices. Experiments confirm that this method effectively achieves CL without forgetting, outperforming handcrafted algorithms on classic benchmarks. While this is a promising result on its own, in this talk, I will go beyond this limited scope of CL. I will serve this CL setting as an example to introduce a broader perspective of “problem awareness” in machine learning. I will argue that in many prior CL methods, systems fail in CL because they do not know what it means to continually learn without forgetting. I will show that the same argument can explain the previous failures of neural networks on other classic challenges—historically pointed out by cognitive scientists in comparison to human intelligence—, such as systematic generalization and few-shot learning. I will highlight how similar metalearning methods provide a promising solution to these challenges too.
Bio: is a post-postdoc at Harvard University, Center for Brain Science.
Previously, he was a postdoc and lecturer at the Swiss AI Lab IDSIA, University of Lugano (Switzerland) from 2020 to 2023, where he taught a popular course on practical deep learning. He received his PhD in Computer Science from RWTH Aachen University (Germany) in 2020, and undergraduate and Master’s degrees in Applied Mathematics from École Centrale Paris and ENS Cachan (France). He was also a research intern at Google in NYC and Mountain View, in 2017 and 2018. He is broadly interested in the computational principles of learning, memory, perception, self-reference, and decision making, as ingredients for building and understanding general-purpose intelligence. The scope of his research interests has expanded from language modeling (PhD) to general sequence and program learning (postdoc), and currently to neuroscience and cognitive science (post-postdoc).

CTN Claudia Clopath
2024-08-16 11:30 - 13:00 UTC | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Feedback-based motor control can guide plasticity and drive rapid learning
Abstract: Animals use afferent feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that counteracts its effects. Primary motor cortex (M1) is intimately involved in both processes, integrating inputs from various sensorimotor brain regions to update the motor output. Here, we investigate whether feedback-based motor control and motor adaptation may share a common implementation in M1 circuits. We trained a recurrent neural network to control its own output through an error feedback signal, which allowed it to recover rapidly from external perturbations. Implementing a biologically plausible plasticity rule based on this same feedback signal also enabled the network to learn to counteract persistent perturbations through a trial-by-trial process, in a manner that reproduced several key aspects of human adaptation. Moreover, the resultant network activity changes were also present in neural population recordings from monkey M1. Online movement correction and longer-term motor adaptation may thus share a common implementation in neural circuits.

CTN: Sebastian Seung
2024-09-06 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Insights into vision from interpreting a neuronal wiring diagram
Host: Marcus Triplett
Abstract: In 2023, the FlyWire Consortium released the neuronal wiring diagram of an adult fly brain. This contains as a corollary the first complete wiring diagram of a visual system, which has been used to identify all 200+ cell types that are intrinsic to the Drosophila optic lobe. About half of these cell types were previously unknown, and less than 20% have ever been recorded by a physiologist. I will argue that plausible functions for many cell types can be guessed by interpreting the wiring diagram.

CTN: Stephanie Palmer
2024-09-13 11:30 - 13:00 UTC | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: How behavioral and evolutionary constraints sculpt early visual processing
Abstract: Biological systems must selectively encode partial information about the environment, as dictated by the capacity constraints at work in all living organisms. For example, we cannot see every feature of the light field that reaches our eyes; temporal resolution is limited by transmission noise and delays, and spatial resolution is limited by the finite number of photoreceptors and output cells in the retina. Classical efficient coding theory describes how sensory systems can maximize information transmission given such capacity constraints, but it treats all input features equally. Not all inputs are, however, of equal value to the organism. Our work quantifies whether and how the brain selectively encodes stimulus features, specifically predictive features, that are most useful for fast and effective movements. We have shown that efficient predictive computation starts at the earliest stages of the visual system, in the retina. We borrow techniques from statistical physics and information theory to assess how we get terrific, predictive vision from these imperfect (lagged and noisy) component parts. In broader terms, we aim to build a more complete theory of efficient encoding in the brain, and along the way have found some intriguing connections between formal notions of coarse graining in biology and physics.

Continual Learning Working Group: Kick Off
2024-09-13 15:30 - 17:00 (America/New_York) | CEPSR 620, Schapiro 530 W. 120th St
Speaker: Mengye Ren

Title: Lifelong and Human-like Learning in Foundation Models

Abstract: Real-world agents, including humans, learn from online, lifelong experiences. However, today’s foundation models primarily acquire knowledge through offline, iid learning, while relying on in-context learning for most online adaptation. It is crucial to equip foundation models with lifelong and human-like learning abilities to enable more flexible use of AI in real-world applications. In this talk, I will discuss recent works exploring interesting phenomena in foundation models when learning in online, structured environments. Notably, foundation models exhibit anticipatory and semantically-aware memorization and forgetting behaviors. Furthermore, I will introduce a new method that combines pretraining and meta-learning for learning and consolidating new concepts in large language models. This approach has the potential to lead to future foundation models with incremental consolidation and abstraction capabilities.

ARNI NSF Site Visit
2024-09-16 08:00 - 16:30 (America/New_York) | Innovation Hub, Tang Family Hall - 2276 12TH AVENUE – FLOOR 02
NSF Site Visit – The NSF team will evaluate the progress and achievements of ARNI’s projects to date and provide recommendations to steer future directions and funding for the project.
If you are interested in learning more about ARNI over-all, join this Zoom link from 9am to 12pm or 2pm to 4:30pm.

CTN: Eva Dyer
2024-09-20 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Large-scale pretraining on neural data allows for transfer across individuals, tasks and species
Abstract: As neuroscience datasets grow in size and complexity, integrating diverse data sources to achieve a comprehensive understanding of brain function presents both an opportunity and a challenge. In this talk, I will introduce our approach to developing a multi-source foundation model for neuroscience, utilizing large-scale pretraining on neural data from various tasks, brain regions, and species. These models are designed to enable seamless transfer learning across individuals, tasks, and species, thereby enhancing data efficiency and advancing the capabilities of neural decoding technologies. By integrating diverse datasets, our aim is to uncover the common neural functions that underlie a wide range of tasks and brain regions, providing a deeper understanding of brain function and informing future brain-machine interface applications.
Zoom:
https://columbiauniversity.zoom.us/j/97505761667?pwd=[REDACTED]
passcode: ctn

Continual Learning Working Group: Haozhe Shan
2024-09-20 15:30 - 17:00 (America/New_York) | CEPSR 620, Schapiro 530 W. 120th St
Speaker: Haozhe Shan

Title: A theory of continual learning in deep neural networks: task relations, network architecture and learning procedure

Abstract: Imagine listening to this talk and afterwards forgetting everything else you’ve ever learned. This absurd scenario would be commonplace if the brain could not perform continual learning (CL) – acquiring new skills and knowledge without dramatically forgetting old ones. Ubiquitous and essential in our daily life, CL has proven a daunting computational challenge for neural networks (NN) in machine learning. When is CL especially easy or difficult for neural systems, and why?

Towards answering these questions, we developed a statistical mechanics theory of CL dynamics in deep NNs. The theory exactly describes how the network’s input-output mapping evolves as it learns a sequence of tasks, as a function of the training data, NN architecture, and the strength of a penalty applied to between-task weight changes. We first analyzed how task relations affect CL performance, finding that they can be efficiently described by two metrics: similarity between inputs from two tasks in the NN’s feature space (“input overlap”) and consistency of input-output rules of different tasks (“rule congruency”). Higher input overlap leads to faster forgetting while lower congruency leads to stronger asymptotic forgetting – predictions which we validated with both synthetic tasks and popular benchmark datasets. Surprisingly, we found that increasing the network depth reshapes geometry of the network’s feature space to decrease input overlap between tasks and slow forgetting. The reduced cross-task overlap in deeper networks also leads to less anterograde interference during CL but at the same time hinders their ability to accumulate knowledge across tasks. Finally, our theory can well match CL dynamics in NNs trained with stochastic gradient descent (SGD). Using noisier, faster learning during CL is equivalent to weakening the weight-change penalty. Link to preprint: https://arxiv.org/abs/2407.10315.
Bio: Haozhe Shan joined Columbia University as an ARNI Postdoctoral Fellow in August 2024. He recently received a Ph.D. in Neuroscience from Harvard, advised by Haim Sompolinsky. His research applies quantitative tools from physics, statistics and other fields to discover computational principles behind neural systems, both biological and artificial. A recent research interest is the ability of neural systems to continually learn and perform multiple tasks in a flexible manner.
Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

Multi-resource-cost Optimization for Neural Networks Models Working Group (NNMS): Tom Griffiths
2024-09-25 14:00 - 16:30 (America/New_York) | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: Bounded optimality: A cognitive perspective on neural computation with resource limitations

Multi-resource-cost Optimization for Neural Networks Models Working Group (NNMS): Simon Laughlin
2024-10-03 13:00 - 15:00 (America/New_York) | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: Neuronal energy consumption: basic measures and trade-offs, and their effects on efficiency
Zoom: https://columbiauniversity.zoom.us/j/98299154214?pwd=[REDACTED]

Continual Learning Working Group: Amogh Inamdar
2024-10-04 14:00 - 16:00 UTC | CSB 488
Title: Taskonomy: Disentangling Task Transfer Learning
Abstract: TBD
Link: http://taskonomy.stanford.edu/taskonomy_CVPR2018.pdf

CTN: Brenden Lake
2024-10-11 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: Meta-learning for more powerful behavioral modeling
Abstract: Two modeling paradigms have historically been in tension: Bayesian models provide an elegant way to incorporate prior knowledge, but they make simplifying and constraining assumptions; on the other hand, neural networks provide great modeling flexibility, but they make it difficult to incorporate prior knowledge. Here I describe how to get the best of both approaches through Behaviorally-Informed Meta-Learning (BIML). BIML allows for modeling behavior with flexible Transformers, even with only minimal data, by distilling Bayesian priors into neural networks and then further fine-tuning the networks on behavioral data. I’ll show some initial successes using BIML to model human concept learning, resulting in superior fits by capturing behavioral heuristics and biases that violate simple Bayesian assumptions. At the end, I would love to discuss how to overcome the challenges of interpreting this new class of models.
Zoom: https://columbiauniversity.zoom.us/j/93740145362?pwd=[REDACTED]
meeting ID: 937 4014 5362
passcode: ctn

Continual Learning Working Group: Lindsay Smith
2024-10-11 13:30 - 15:00 UTC | CEPSR 620, Schapiro 530 W. 120th St
Title: A Practitioner’s Guide to Continual Multimodal Pretraining
Reading: https://arxiv.org/pdf/2408.1447
Zoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

CTN: Benjamin Grewe
2024-10-16 10:30 - 12:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Target Learning rather than Backpropagation Explains Learning in the Mammalian Neocortex
Abstract: Modern computational neuroscience presents two competing hypotheses for hierarchical learning in the neocortex: (1) deep learning-inspired approximations of the backpropagation algorithm, where neurons adjust synapses to minimize error, and (2) target learning algorithms, where neurons reduce the feedback required to achieve a desired activity. In this talk, I will explore this fundamental question by examining the relationship between synaptic plasticity and the somatic activity of pyramidal neurons. Using a combination of single-neuron modeling, in vitro experiments, and deep learning theory, we predict distinct neuronal dynamics for each hypothesis. We then test these predictions using in vivo data from the mouse visual cortex. Our results reveal that cortical learning aligns more closely with target learning, underscoring a significant discrepancy between conventional deep learning approaches and the mechanisms underlying cortical hierarchical learning. This work provides new insights into the neural processes that drive learning in the brain and challenges current models inspired by deep learning.

Continual Learning Working Group: Yasaman Mahdaviyeh
2024-10-18 13:30 - 15:00 UTC | CEPSR 620, Schapiro 530 W. 120th St
Title: Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction
Reading: https://openreview.net/pdf?id=TpD2aG1h0D
Zoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

ARNI Annual Retreat
2024-10-21 09:00 - 2024-10-22 13:00 (America/New_York) | Faculty House, 64 Morningside Dr
General Agenda
October 21st, Day 1 from 8:45am to 5pm

Breakfast and Lunch Provided
Opening
3 Keynote Speakers from ARNI Faculty
Research Brainstorming and Discussions
Project/Student Poster Session
Education and Broader Impact Discussions

October 22nd, Day 2 from 9am to 1pm

Breakfast and Lunch Provided
1 Keynote Speaker
Brainstorming and Discussion on Collaborations & Knowledge Transfer
AI Industry Panel (with industry partners)
Closing

CTN: Tatiana Engel
2024-10-25 11:30 - 13:00 UTC | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Unifying neural population dynamics, manifold geometry, and circuit structure.

Abstract: Single neurons show complex, heterogeneous responses during cognitive tasks, often forming low-dimensional manifolds in the population state space. Consequently, it is widely accepted that neural computations arise from low-dimensional population dynamics while attributing functional properties to individual neurons is impossible. I will present recent work from our lab that bridges single-neuron heterogeneity to manifold geometry and population dynamics. First, we developed a flexible modeling approach for simultaneously inferring single-trial population dynamics and tuning functions of individual neurons to the latent population state. Applied to spike data recorded during decision-making, our model revealed that all neurons encode the same dynamic decision variable, and heterogeneous firing rates result from diverse tuning of single neurons to this decision variable. Second, using a firing-rate recurrent network model, we mathematically prove that responses of single neurons cluster into functional types when population dynamics are confined to a low-dimensional linear subspace, with the number of distinct response types equal to the linear dimension of the neural manifold. We confirm these predictions in recurrent neural networks trained on cognitive tasks and brain-wide neural recordings from mice during a decision-making behavior. Our findings show that low-dimensional population dynamics can be understood in terms of functional cell types, and random mixed selectivity emerges only in the limit of high-dimensional dynamics.

CTN: Naureen Ghani
2024-10-31 11:00 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Mice wiggle a wheel to boost the salience of low visual contrast stimuli

Abstract: From the Welsh tidy mouse to the New York City pizza rat, movement belies rodent intelligence. We show that head-fixed mice develop an active sensing strategy while performing a visual perceptual decision-making task (The International Brain Laboratory, 2021). Akin to humans shaking a computer mouse to find the cursor on a screen, we demonstrate that mice wiggle the wheel that controls the movement of a visual stimulus to boost low contrast salience. Moreover, mice wiggle the wheel at a temporal frequency (11.9 ± 2.9 Hz) optimal for their visual systems (Umino et al, 2018). With the “old method of watching and wondering about behavior,” we reveal that mice exploit that it is easier to see something moving than something stationary by wiggling (Tinbergen, 1973).

CTN: Jacob Macke
2024-11-01 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Building mechanistic models of neural computations with simulation-based machine learning

Abstract: Experimental techniques now make it possible to measure the structure and function of neural circuits at an unprecedented scale and resolution. How can we leverage this wealth of data to understand how neural circuits perform computations underlying behaviour? A mechanistic understanding will require models that align with experimental measurements and biophysical mechanisms, while also being capable of performing behaviorally relevant computations. Building such models has remained a central challenge.
I will present our work on addressing his challenge: We have developed machine learning methods and differentiable simulators that make it possible to algorithmically identify models that link biophysical mechanisms, neural data, and behaviour. I will show how these approaches—in combination with modern connectomic measurements—make it possible to build large-scale mechanistic models of the fruit fly visual system, and how such a model can make experimentally testable predictions for each neuron in the system.

ARNI Seminar Series Kick Off: Speaker Jim DiCarlo
2024-11-01 15:00 - 17:00 (America/New_York) | Zuckerman Institute – L7-119, 3227 Broadway, New York, NY, 10027, United States
Title: Do contemporary, machine-executable models (aka digital twins) of the primate ventral visual system unlock the ability to non-invasively, beneficially modulate high level brain states?
Abstract:
In this talk, I will first briefly review the story of how neuroscience, cognitive science and computer science (“AI”) converged to create specific, image-computable, deep neural network models intended to appropriately abstract, emulate and explain the mechanisms of primate core visual object identification and categorization behaviors. Based on a large body of primate neurophysiological and behavioral data, some of these network models are now the most accurate emulators of the primate ventral visual stream — they well-approximate both its internal neural mechanisms and how those mechanisms support the ability of humans and other primates to rapidly and accurately infer object identity, position, pose, etc. from the set of pixels (image) received during typical natural viewing.
Because these leading neuroscientific emulator models — aka “digital twins” — are fully observable and machine-executable, they offer predictive and potential application power that our field’s prior conceptual models did not. I will describe two recent examples from our team. First, the current leading digital twins predict that the brain’s high level visual neurons (inferior temporal cortex, IT) should be highly susceptible to “adversarial attacks” in which an agent (the adversary) aims to strongly disrupt the normal neural response (here, neural firing rate) to any given natural image via small magnitude, targeted changes to that image. We verified this surprising prediction in monkey IT neurons. Second, we show how we can turn this result around and extend it: instead of making adversarial “attacks”, we propose using digital twin models to support non-invasive, beneficial brain modulation. Specifically, we show that we can use a digital twin to design spatial patterns of light energy that, when applied to the organism’s retina in the context of ongoing natural visual processing, results in precise modulation (i.e. rate bias) of the pattern of a population of IT neurons (where any intended modulation pattern is chosen ahead of time by the scientist). Because the IT visual neural populations are known to directly modulate downstream neural circuits involved in mood and anxiety, we speculate that this could provide a new, non-invasive application avenue of potential future human clinical benefit.
Zoom Link: https://columbiauniversity.zoom.us/j/97757217278?pwd=[REDACTED]

Continual Learning Working Group: Brainstorming
2024-11-06 12:30 - 13:00 (America/New_York) | CEPSR 620, Schapiro 530 W. 120th St
Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

CTN: Tanya Sharpee
2024-11-08 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Seminar Time: 11:30am
Date: 11/8/2024
Location: JLG, L5-084
Host: Krishan Kumar

Title: Building mechanistic models of neural computations with simulation-based machine learnin

Continual Learning Working Group: Nikita Rajaneesh
2024-11-13 12:00 - 13:00 (America/New_York) | CEPSR 6LW4, Computer Science Department 500 West 120 Street
Title: Wandering Within a World
A discussion on Wandering Within a World: Online Contextualized Few-Shot Learning, this 2021 paper by our very own Rich Zemel leverages contextual information in a continually changing environment to improve model performance in realistic settings.
Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

CTN: Catherine Hartley
2024-11-15 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: TBD
Abstract: TBD

CTN: Seminar Speaker Alessandro Ingrosso
2024-11-20 10:30 - 11:30 (America/New_York) | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title:
Statistical mechanics of transfer learning in the proportional limit

Abstract:
Transfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related (source) task, and it crucially depends on the ability of a network to learn useful features. I will present a recent work that leverages analytical progress in the proportional regime of deep learning theory (i.e. the limit where the size of the training set P and the size of the hidden layers N are taken to infinity keeping their ratio P/N finite) to develop a novel statistical mechanics formalism for TL in Bayesian neural networks. I’ll show how such single-instance Franz-Parisi formalism can yield an effective theory for TL in one-hidden-layer fully-connected neural networks. Unlike the (lazy-training) infinite-width limit, where TL is ineffective, in the proportional limit TL occurs due to a renormalized source-target kernel that quantifies their relatedness and determines whether TL is beneficial for generalization.

Lecture in AI: Danqi Chen
2024-12-06 11:00 - 12:00 (America/New_York) | Davis Auditorium, 530 W 120th St, New York, NY 10027, New York, NY, 10027
Title: Training Language Models in Academic: Research Questions and Opportunities
Abstract: Large language models have emerged as transformative tools in artificial intelligence, demonstrating unprecedented capabilities in understanding and generating human language. While these models have achieved remarkable performance across a wide range of benchmarks and enabled groundbreaking applications, their development has been predominantly concentrated within large technology companies due to substantial computational and proprietary data requirements. In this talk, I will present a vision for how academic research can play a critical role in advancing the open language model ecosystem, particularly by developing smaller yet highly capable models and advancing our fundamental understanding of training practices. Drawing from our research group’s recent projects, I will examine key research questions and challenges in both pre-training and post-training stages. Our work spans developing small language models (Sheared LLaMA; 1-3B parameters), the state-of-the-art <10B model on Chatbot Arena (gemma-2-SimPO), and long-context models supporting up to 512K tokens (ProLong). These examples illustrate how academic research can push the boundaries of model efficiency, capability, and scalability. I will conclude by exploring future directions and highlighting opportunities to shape the development of more accessible and powerful language models.

CTN Seminar: Andrew Leifer
2024-12-06 11:00 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States

Continual Learning Working Group: Lea Duncker
2024-12-06 14:00 - 15:00 (America/New_York) | CEPSR 620, Schapiro 530 W. 120th St
Title: Task-dependent low-dimensional population dynamics for robustness and learning
Abstract: Biological systems face dynamic environments that require flexibly deploying learned skills and continual learning of new tasks. It is not well understood how these systems balance the tension between flexibility for learning and robustness for memory of previous behaviors. Neural activity underlying single, highly controlled experimental tasks has repeatedly been observed to exhibit low-dimensional structure. However, it is unclear how this organization arises and is maintained throughout learning, and how it might differ when networks are exposed to multiple tasks. In this talk, I will present work on a continual learning rule designed to minimize interference between sequentially learned tasks in recurrent networks. The learning rule preserves network dynamics within activity-defined low-dimensional subspaces used for previously learned tasks. It encourages recurrent dynamics associated with interfering tasks to explore orthogonal subspaces. Employing a set of tasks used in neuroscience, I will show that this approach can successfully eliminate catastrophic interference, while allowing for reuse of similar low-dimensional dynamics across similar tasks. This possibility for shared computation allows for faster learning during sequential training. Finally, I will highlight limitations of this approach in fully exploiting task-similarity for optimal re-use of previously learned solutions, and outline new work we are starting in my group now to address this.
Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

CTN Lab: Ashok Litwin-Kumar
2024-12-09 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Searching for symmetries in connectome data

Abstract: I will talk about work with Haozhe Shan on identifying structure in connectome data that suggests a cell type encodes one or a handful of variables, like heading direction or retinotopy. We are framing the problem as learning a graph embedding, but I will also mention other things we have considered which, at least for me, were educational. The project is at an early stage, so we would welcome suggestions and ideas.
