ARNI

CTN: Reza Shadmehr
2025-10-03 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title and Abstract: TBD

ARNI Biological Learning Working Group
2025-10-03 14:00 - 15:00 | NY
Biological Learning first fall 2025 working group session of the year! Pingsheng Li, PhD student with Blake Richards at MILA, will be presenting Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks.

Brief discussion of how the group can all collaborate together on a project, define a benchmark for ourselves with some metrics we care about, and then the group will break into pods that each will develop methods towards solving the benchmark tasks.
Google Meets: upon request @ arni@columbia.edu

ARNI Frontier Models for Neuroscience and Behavior Working Group
2025-10-08 14:00 - 15:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title:
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
Abstract:
Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.3 million neurons from the visual cortex of 78 mice across 323 sessions, totaling more than 150 billion neural tokens recorded during natural movies, images and parametric stimuli, and behavior. We train multi-modal, multi-task transformer models (1M–300M parameters) that support three regimes flexibly at test time: neural prediction (predicting neuronal responses from sensory input and behavior), behavioral decoding (predicting behavior from neural activity), neural forecasting (predicting future activity from current neural dynamics), or any combination of the three. We find that performance scales reliably with more data, but gains from increasing model size saturate — suggesting that current brain models are limited by data rather than compute. This inverts the standard AI scaling story: in language and computer vision, massive datasets make parameter scaling the primary driver of progress, whereas in brain modeling — even in the mouse visual cortex, a relatively simple and low-resolution system — models remain data-limited despite vast recordings. These findings highlight the need for richer stimuli, tasks, and larger-scale recordings to build brain foundation models. The observation of systematic scaling raises the possibility of phase transitions in neural modeling, where larger and richer datasets might unlock qualitatively new capabilities, paralleling the emergent properties seen in large language models.
Zoom: Upon request @ arni@columbia.edu

CTN: Maryam Shanechi
2025-10-10 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Dynamical models of neural-behavioral data with application to AI-driven neurotechnology
Abstract: A major challenge in neuroAI is to model, decode, and modulate the activity of large populations of neurons that underlie our brain’s functions and dysfunctions. Toward addressing this challenge, I will present our work on novel dynamical models of neural-behavioral data and applying them to enable a new generation of brain-computer interfaces for disorders such as major depression. First, I will present a novel dynamical modeling framework that jointly describes neural-behavioral data, dissociates behaviorally relevant neural dynamics, and learns them more accurately. Then, I will show how we can also predict the effect of inputs, such as sensory stimuli or neurostimulation, to dissociate intrinsic and input-driven neural dynamics. I further present how these models can incorporate multiple spatiotemporal scales of brain activity simultaneously, from spikes to LFP to brain-wide neuroimaging. Finally, I will discuss the challenge of developing AI algorithms for neurotechnology. I will present a framework that combines neural networks with stochastic state-space models to enable accurate yet flexible inference of brain states causally, non-causally, and even with missing neural samples. The above dynamical models can enable next-generation AI-driven neurotechnologies that restore lost motor and emotional function in diverse brain disorders such as paralysis and major depression.

CTN: Ilana Witten
2025-10-17 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States

Speaker: Jascha Achterberg ARNI WG Multi-resource-cost optimization of neural network models
2025-10-21 13:00 - 15:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title:
Building the brain’s efficient system-level architecture: optimisations across space, time, and multiple regions
Abstract:
The computations a brain can perform are fundamentally constrained by physical realities: energetic resources are limited, and time is precious. To understand why the brain works the way it does, we must understand its function in the context of these constraints. Prior modeling work has successfully demonstrated how spatial energetic constraints drive structure-function co-optimization, giving rise to many of the architectural features we observe across areas of neuroscience. By incorporating such physical constraints, we can build complex systems-level models that are meaningfully constrained by physically measurable factors rather than arbitrary design choices.
In this talk, I will expand on these spatial frameworks by introducing new work on temporal processing and signal precision constraints in neural networks. I will demonstrate how different optimization strategies within individual regions can be combined in heterogeneous multi-region models, revealing how the brain trades off resource use across tasks and situations. Finally, I will show how space and time interact in surprising ways to achieve efficient computation — principles that apply not only to the brain but to any large-scale distributed computing system. Together, these advances bring us closer to understanding the general principles that enable sophisticated intelligence to emerge from physically and energetically constrained computing systems.

CTN: Anna Schapiro
2025-10-24 11:30 - 13:00 | Zuckerman Institute- Kavli Auditorium 9th Fl, 3227 Broadway, NY
Title: Learning representations of specifics and generalities over time

Abstract: There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we rapidly learn novel information of specific and generalized types, which we test across statistical learning, inference, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems, with empirical and modeling investigations into how hippocampal replay shapes neocortical representations during sleep. Together, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.
Zoom: Available upon request @ arni@columbia.edu

ARNI Biological Learning Working Group
2025-10-24 14:00 - 15:00, 2025-11-07 16:00 - 17:00 | NY
Continuation from prior meetings about benchmarks and competition proposals.
Zoom Link: upon request @ ARNI@columbia.edu

ARNI Continual Learning Working Group
2025-10-27 15:00 - 16:00 | CEPSR 620, Schapiro 530 W. 120th St
Next Meeting Info

Date: Monday, October 27
Time: 3-4pm
Room: CEPSR 620

Zoom: Upon request @arni@columbia.edu

ARNI Distinguished Seminar Series: Leila Wehbe
2025-10-29 15:00 - 16:30 | Zuckerman Institute- Kavli Auditorium 9th Fl, 3227 Broadway, NY
Bio: Leila Wehbe is an associate professor in the Machine Learning Department and the Neuroscience Institute at Carnegie Mellon University. Her work is at the interface of cognitive neuroscience and computer science. It combines naturalistic functional imaging with machine learning both to improve our understanding of the brain and to find insight to build better artificial systems. She is the recipient of an NSF CAREER award, a Google faculty research award and an NIH CRCNS R01. Previously, she was a postdoctoral researcher at UC Berkeley and obtained her PhD from Carnegie Mellon University
Title: Model prediction error reveals separate mechanisms for integrating multi-modal information in the human cortex
Abstract: Language comprehension engages much of the human cortex, extending beyond the canonical language system. Yet in everyday life, language unfolds alongside other modalities, such as vision, that recruit these same distributed areas. Because language is often studied in isolation, we still know little about how the brain coordinates and integrates multimodal representations. In this talk, we use fMRI data from participants viewing 37 hours of TV series and movies to model the interaction of auditory and visual input. Using encoding models that predict brain activity from each stream, we introduce a framework based on prediction error that reveals how individual brain regions combine multimodal information.

CTN: Yael Niv
2025-11-05 11:30 - 12:30 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Seminar Time: 11:30am
Date: Fri 11/7/25
Seminar Location: JLG, L5-084
Host: Weijia Zhang

Title: Latent causes, prediction errors, and the organization of memory

Abstract: No two events are alike. But still, we learn, which means that we implicitly decide what events are similar enough that experience with one can inform us about what to do in another. We have suggested that this relies on parsing of incoming information into “clusters” according to inferred hidden (latent) causes. Moreover, we have suggested that unexpected information (that is, a prediction error) is key to this separation into clusters. In this talk, I will demonstrate these ideas through behavioral experiments showing evidence for clustering and illustrate the effects of prediction errors on the organization of memory. I will then tie the different findings together into a hypothesis about how information about events is organized in our brain.

Speaker: Bryan Li - ARNI Frontier Models for Neuroscience and Behavior Working Group
2025-11-05 14:00 - 15:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Bio
Bryan Li is completing his PhD in NeuroAI at the University of Edinburgh, under the supervision of Arno Onken and Nathalie Rochefort. His main PhD project focuses on building deep learning-based encoding models of the visual cortex that accurately predict neural activity in response to arbitrary visual stimuli. Recently, he joined Dario Farina’s lab at Imperial College London as an Encode Fellow, working on neuromotor interfacing and decoding.

Title (https://www.biorxiv.org/content/10.1101/2025.09.16.676524v2)
Movie-trained transformer reveals novel response properties to dynamic stimuli in mouse visual cortex

Abstract
Understanding how the brain encodes complex, dynamic visual stimuli remains a
fundamental challenge in neuroscience. Here, we introduce ViV1T, a transformer-based model trained on natural movies to predict neuronal responses in mouse primary visual cortex (V1). ViV1T outperformed state-of-the-art models in predicting responses to both natural and artificial dynamic stimuli, while requiring fewer parameters and reducing runtime. Despite being trained exclusively on natural movies, ViV1T accurately captured core V1 properties, including orientation and direction selectivity as well as contextual modulation, despite lacking explicit feedback mechanisms. ViV1T also revealed novel functional features. The model predicted a wider range of contextual responses when using natural and model-generated surround stimuli compared to traditional gratings, with novel model-generated dynamic stimuli eliciting maximal V1 responses. ViV1T also predicted that dynamic surrounds elicited stronger contextual modulation than static surrounds. Finally, the model identified a subpopulation of neurons that exhibit contrast-dependent surround modulation, switching their response to surround stimuli from inhibition to excitation when contrast decreases. These predictions were validated through semi-closed-loop in vivo recordings. Overall, ViV1T establishes a powerful, data-driven framework for understanding how brain sensory areas process dynamic visual information across space and time.

Zoom link: Upon request @ arni@columbia.edu

ARNI Continual Learning Working Group
2025-11-12 16:00 - 17:00 | CEPSR 620, Schapiro 530 W. 120th St
Next Meeting Info

Date: Wednesday, Nov 12
Time: 4pm-5pm
Room: CEPSR 620
Zoom: Upon request @ arni@columbia.edu

Carl Vondick Hosts Talk with Aaron Hertzmann (Adobe)
2025-11-13 14:30 - 15:30 | CSB 453, Mudd Building, 500 W 120th Street
Aaron Hertzmann
Why Do Pictures Work? Explanations From Real-World Vision
Speaker: Aaron Hertzmann (Adobe)
Host: Carl Vondrick
Date: Thursday, November 13, 2025
Time: 2:30 PM
Location: CSB 453

Abstract: I outline possible answers to the long-standing question of why pictures work: why can people look at a painting or photograph, and see a depicted subject, rather than just marks on a page or lights on a display? Observers with no prior experience with pictures can understand some kinds of pictures, indicating that picture understanding is not solely a product of experience or culture. I argues that picture perception can be explained as a product of several properties of real-world vision. First, the fact that humans can understand certain real-world phenomena—refraction, reflection, cast shadows—as simultaneously surface phenomena but also images of an underlying cause explains why we can see pictures as depictions and not just markings. Second, the fact that viewers can understand real-world scenes with unfamiliar combinations of objects explains our ability to understand many different styles of depiction. For example, we can understand black-and-white photos of people because, in real-world vision, we could recognize a familiar person who had been painted gray. Third, our robustness to visual defects and other difficult viewing conditions explains our ability to understand styles of pictorial textures, like paint strokes. Extensions of these basic ideas can explain depiction in many different visual styles, including photographic tone reproduction, line drawings, silhouettes, cartoons, painterly styles, and more. The proposed models of picture understanding could significantly inform future analysis of perceptual mechanisms, picture aesthetics, and the nature of different styles of depiction.

Zoom Link: Upon request @arni@columbia.edu

ARNI Annual Retreat 2025
2025-11-17 08:00 - 2025-11-18 13:00 | NY
To celebrate the many accomplishments as we wrap up year two and continue our momentum into year three.
We anticipate engaging discussions in the working groups and panels as we explore future directions for ARNI.
We also want to highlight the participation of Bing Brunton, Jim DiCarlo, and Thomas Reardon from our External Advisory Board.
By registration only!

CTN: Karel Svoboda
2025-11-21 11:30 - 12:30 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Seminar Time: 11:30am
Date: 11/21/25
Seminar Location: JLG, L5-084
Host: Ji Xia

Title: Illuminating synaptic learning
Abstract: How do synapses in the middle of the brain know how to adjust their weight to advance a behavioral goal? This is referred to as the synaptic ‘credit assignment problem’. A large variety of synaptic learning rules have been proposed, mainly in the context of artificial neural networks. The most powerful learning rules (e.g. back-propagation of error) are thought to be biologically implausible, whereas the widely studied biological learning rules (Hebbian) are insufficient for goal-directed learning. I will describe ongoing work, both experimental and theoretical, focused on understanding learning at the level of circuits and synapses in the motor cortex.

Speaker: Alan Stocker ARNI WG Multi-resource-cost optimization of neural network models
2025-12-10 11:00 - 12:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Alan Stocker
Professor of Psychology at UPenn
Title: Economics of temporal evidence integration
Abstract: The temporal integration of sensory information is an important aspect of many human decision tasks. I will present results of ongoing research in my laboratory aimed at understanding the dynamic processes underlying evidence integration. In particular, I will discuss a novel resource-rational model that treats both the representation as well as the integration and maintenance of sensory evidence as actively controlled, performance-effort trade-off mechanisms. Validated against data from various behavioral experiments, the model not only provides a normative explanation for observed non-linear dynamics in evidence integration but also a parsimonious explanation for individual tendencies for recency or primacy behavior. As the work is ongoing and unpublished, I am looking forward to an engaged discussion with the audience.
Zoom Link: Upon request @ arni@columbia.edu

ARNI Continual Learning Working Group
2025-12-11 15:00 - 16:00 | CEPSR 620, Schapiro 530 W. 120th St
Next Meeting Info

Date: Thursday, Dec 11
Time: 3pm-4pm
Room: CEPSR 620
Zoom: Upon request @ arni@columbia.edu

CTN: Mehrdad Jazayeri
2025-12-12 11:30 - 13:00 | Zuckerman Institute- Kavli Auditorium 9th Fl, 3227 Broadway, NY
Title: Adaptive problem solving in the primate frontal cortex

Abstract: Humans excel at solving problems adaptively. When missing the bus to an appointment, for instance, we might wait for the next one, call a taxi, cancel, or reschedule, depending on the situation. This ability to assess context and choose a suitable strategy is central to intelligence, yet its neural and computational foundations remain poorly understood. To address this gap, we trained monkeys on a challenging decision-making task that could be solved using multiple strategies, providing a controlled setting to study strategic flexibility. Behaviorally, the animals performed accurately and generalized to new conditions, but their choices were inconsistent with any single policy, suggesting the use of internally generated strategies. Large-scale electrophysiological recordings from the dorsomedial frontal cortex revealed that population activity unfolded along distinct neural trajectories corresponding to different strategies. The structure of these trajectories—set by the organization of initial neural states and their subsequent evolution—showed that animals assessed the problem and engaged distinct, rationally structured computational algorithms. A latent behavioral model grounded in these neural dynamics predicted the animals’ choices more accurately than any fixed-strategy model, providing a direct link between cortical population activity and adaptive decision-making. Together, these findings reveal a neurophysiological mechanism for strategic decision-making and offer a mechanistic understanding of the neural basis of adaptive problem solving.

CTN: Roozbeh Kiani
2025-12-19 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Seminar Time: 11:30am
Date: 12/19/25
Seminar Location: JLG, L5-084
Host: Tahereh Toosi

Title: Flexible decision-making: policies and rules

Abstract: Flexible behavior requires flexible decision-making. We adapt seamlessly to changing environments—adjusting biases, altering decision rules, and inferring hidden task contexts—often without explicit cues. In this talk, I will outline a framework that formalizes different levels of this flexibility and show how these adjustments are implemented in neural codes across the frontoparietal cortex. I will highlight three forms of decision flexibility: (1) Bias adjustments, driven by asymmetric rewards, shift neural activity along the decision variable axis; (2) Rule changes, such as varying sensory weights in a multi-feature discrimination task, produce rotational changes in the population geometry, supporting rapid changes in decision policy; and (3) Hierarchical inference, where animals infer hidden contexts to adapt to task structure, is reflected in the emergence of latent variables represented in distributed subspaces.

CTN: Nao Uchida
2026-01-16 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: A normative perspective on diversity of dopamine neurons
Abstract: TBD

Speaker: Xaq Pitkow ARNI WG Multi-resource-cost optimization of neural network models
2026-01-20 15:00 - 16:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: Frugal Inference for Control

Abstract: A key challenge in advancing artificial intelligence is achieving the right balance between utility maximization and resource use by both external movement and internal computation. While this trade-off has been studied in fully observable settings, our understanding of resource efficiency in partially observable environments remains limited. Motivated by this challenge, we develop a version of the POMDP framework where the information gained through inference is treated as a resource that must be optimized alongside task performance and motion effort. By solving this problem in environments described by linear-Gaussian dynamics, we uncover fundamental principles of resource efficiency. Our study reveals a phase transition in the inference, switching from a Bayes-optimal approach to one that strategically leaves some uncertainty unresolved. This frugal behavior gives rise to a structured family of equally effective strategies, facilitating adaptation to later objectives and constraints overlooked during the original optimization. We illustrate the applicability of our framework and the generality of the principles we derived using two nonlinear tasks. Overall, this work provides a foundation for a new type of rational computation that both brains and machines could use for effective but resource-efficient control under uncertainty.

Zoom Link: Upon request @ arni@columbia.edu

Continual Learning Working Group
2026-01-22 15:00 - 15:30 | CEPSR 620, Schapiro 530 W. 120th St
To kick off the Continual Learning Working Group activities for this year, the Continual Learning working group will meet on Thursday, Jan 22 at 3pm on Zoom and in CEPSR 620.

The meeting will be brief, and we’ll discuss our agenda and scheduling for the coming semester, including our goals for the benchmark project.
Join via Zoom: Upon request @arni@columbia.edu

CTN: Scott Linderman
2026-01-23 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: When and How to Parallelize Seemingly Sequential Models

Abstract: Transformers have become the de facto model for sequential data in large part because they are well adapted to modern hardware: At training time, the loss can be evaluated in parallel over the sequence length on GPUs and TPUs. By contrast, evaluating nonlinear recurrent neural networks (RNNs) appears to be an inherently sequential problem. However, recent advances like DEER (arXiv:2309.12252) and DeepPCR (arXiv:2309.16318) have shown that evaluating a nonlinear recursion can be recast as solving a parallelizable optimization problem, and sometimes this approach can yield dramatic speed-ups in wall-clock time. However, the factors that govern the difficulty of these optimization problems remain unclear, limiting the larger adoption of the technique. I will present a recent line of work from my lab that further develops these methods in both theory and practice. We establish a precise relationship between the dynamics of a nonlinear system and the conditioning of its corresponding optimization formulation. We show that the predictability of a system, defined as the degree to which small perturbations in state influence future behavior, impacts the number of optimization steps required for evaluation. In predictable systems, the state trajectory can be computed in O(log2T) time, where T is the sequence length, a major improvement over the conventional sequential approach. In contrast, chaotic or unpredictable systems exhibit poor conditioning, with the consequence that parallel evaluation converges too slowly to be useful. We validate our claims through extensive experiments, with a particular emphasis on parallelizing nonlinear RNNs and Markov chain Monte Carlo (MCMC) algorithms for Bayesian statistics. I will provide practical guidance on when nonlinear dynamical systems can be efficiently parallelized, and highlighting predictability as a key design principle for parallelizable models.

Language and Vision Working Group
2026-01-23 12:00 - 13:00 | NY
Initial Meeting!
About:
The ARNI Language & Vision Working Group aims to bring together researchers across neuroscience, cognitive science, computer science, and AI to collaboratively advance our understanding of how humans and machines construct multimodal experiences. Its goal is to create a space for discussing ongoing language- and vision-focused projects, identifying natural points of overlap, and transforming them into larger, interdisciplinary initiatives. Grounded in the idea that language and vision form a dynamic, symbiotic system rather than isolated modules, the group seeks to explore how this integration is represented in the brain and in the machine. Strengthening collaboration between these domains is essential for building the next generation of AI systems that learn from continual, multimodal input, reflect human cognitive principles, and ultimately support real-world human needs.
More questions: Contact Anna Krason (akrason@gc.cuny.edu)
Zoom: upon request @ arni@columbia.edu

Speaker: Thuy Nguyen – ARNI Frontier Models for Neuroscience and Behavior Working Group
2026-02-02 15:00 - 16:00 | MILA, A14
Title:A multimodal sleep foundation model for disease prediction
Abstract: Sleep is a fundamental biological process with broad implications for physical and mental health, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)—the gold standard for sleep analysis—captures rich physiological signals but is underutilized due to challenges in standardization, generalizability and multimodal integration. To address these challenges, we developed SleepFM, a multimodal sleep foundation model trained with a new contrastive learning approach that accommodates multiple PSG configurations. Trained on a curated dataset of over 585,000 hours of PSG recordings from approximately 65,000 participants across several cohorts, SleepFM produces latent sleep representations that capture the physiological and temporal structure of sleep and enable accurate prediction of future disease risk. From one night of sleep, SleepFM accurately predicts 130 conditions with a C-Index of at least 0.75 (Bonferroni-corrected P < 0.01), including all-cause mortality (C-Index, 0.84), dementia (0.85), myocardial infarction (0.81), heart failure (0.80), chronic kidney disease (0.79), stroke (0.78) and atrial fibrillation (0.78). Moreover, the model demonstrates strong transfer learning performance on a dataset from the Sleep Heart Health Study—a dataset that was excluded from pretraining—and performs competitively with specialized sleep-staging models such as U-Sleep and YASA on common sleep analysis tasks, achieving mean F1 scores of 0.70–0.78 for sleep staging and accuracies of 0.69 and 0.87 for classifying sleep apnea severity and presence. This work shows that foundation models can learn the language of sleep from multimodal sleep recordings, enabling scalable, label-efficient analysis and disease prediction.

CTN: Herbert Zheng Wu
2026-02-06 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Herbert Zheng Wu
Title: Neural Basis of Leader–Follower Dynamics in Cooperative Behavior

Abstract: Cooperation allows social species to achieve outcomes that individuals cannot accomplish alone. Even in simple groups, cooperative behavior often depends on complementary social roles such as leaders and followers, yet the neural computations supporting these dynamic relationships are not well understood. Our lab investigates how the brain represents social partners, coordinates shared goals, and flexibly allocates control across individuals. Using a new mouse paradigm that captures naturalistic leader–follower behavior during joint foraging, we combine large-scale neural recording, circuit perturbation, and computational modeling to dissect the mechanisms that enable cooperative decision-making. We find that activity in the medial prefrontal cortex reflects both the individual’s role and the evolving social context, integrating self- and partner-related information to guide coordinated action. To probe latent strategies of the animals, we developed a multi-agent inverse reinforcement learning framework that infers the individual goals governing joint behavior, which closely mirror and are decodable from prefrontal activity. Together, these studies aim to reveal general principles by which distributed brain networks support higher-order social cognition and collective behavior.

ARNI Biological Learning Working Group
2026-02-06 16:00 - 17:00 | NY
1) Charlotte onboarded vision and audio MNIST dataloaders. She’s focusing on predictive coding for sequential tasks (e.g., audio data/moving MNIST).
2) Todd built a multi-modal predictive coding baselines showing that unsupervised representation learning is possible here.
3) Eivinas proposed a backprop based autoencoder and Hebbian based autoencoder (maybe Exponentiated Gradients?).
4) Nihal offered to onboard 3D MNIST and workin Hebbian learning rules.
There was also discussion of software engineering conventions (e.g., Github practices, configuration tooling, etc.).
Virtual Link: request @ arni@columbia.edu

CTN: SueYeon Chung
2026-02-13 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
SueYeon Chung
Title: Computing with Neural Manifolds: A Multi-Scale Framework for Understanding Biological and Artificial Neural Networks
Abstract: Recent breakthroughs in experimental neuroscience and machine learning have opened new frontiers in understanding the computational principles governing neural circuits and artificial neural networks (ANNs). Both biological and artificial systems exhibit an astonishing degree of orchestrated information processing capabilities across multiple scales – from the microscopic responses of individual neurons to the emergent macroscopic phenomena of cognition and task functions. At the mesoscopic scale, the structures of neuron population activities manifest themselves as neural representations. Neural computation can be viewed as a series of transformations of these representations through various processing stages of the brain. The primary focus of my lab’s research is to develop theories of neural representations that describe the principles of neural coding and, importantly, capture the complex structure of real data from both biological and artificial systems.
In this talk, I will present three related approaches that leverage techniques from statistical physics, machine learning, and geometry to study the multi-scale nature of neural computation. First, I will introduce new theories based on statistical physics and convex geometry that connect complex geometric structures that arise from neural responses (i.e., neural manifolds) to the efficiency of neural representations in implementing a task. Second, I will employ these theories to analyze how these representations evolve across scales, shaped by the properties of single neurons, learning dynamics, and the transformations across distinct brain regions. Finally, I will show how these insights extend efficient coding principles beyond early sensory stages, linking representational geometry to efficient task implementations. This framework not only help interpret and compare models of brain data but also offers a principled approach to designing ANN models for higher-level vision. This perspective opens new opportunities for using neuroscience-inspired principles to guide the development of intelligent systems.
