ARNI

CTN: Christine Constantinople
2025-09-05 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Neural circuit mechanisms of value-based decision-making
Abstract:
The value of the environment determines animals’ motivational states and sets expectations for error-based learning. But how are values computed? We developed a novel temporal wagering task with latent structure, and used high-throughput behavioral training to obtain well-powered behavioral datasets from hundreds of rats that learned the structure of the task. We found that rats use distinct value computations for sequential decisions within single trials. Moreover, these sequential decisions are supported by different brain regions, suggesting that distinct neural circuits support specific types of value computations. I will discuss our ongoing efforts to delineate how distributed circuits in the orbitofrontal cortex and striatum coordinate complex value-based decisions.

ARNI Continual Learning Working Group Project
2025-09-08 15:00 - 16:00 | CSB 480, Mudd Building, 500 W 120th Street
Zoom link: Upon request @arni@columbia.edu

CTN: Naomi Leonard
2025-09-12 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title:
Fast and Flexible Group Decision-Making
Abstract:
A wide range of animals live and move in groups. Many animals do better in groups than alone when, for example, foraging for food, migrating, and avoiding predators. A key to group success is social interaction. Less well understood is how a group, with no centralized control, is capable of the fast and flexible decision-making required to carry out its tasks in an environment with uncertainty, variability, and rapid change. I will introduce an approach to modeling group decision-making dynamics that draws on biophysical models from computational neuroscience. Analysis of our model provides new insights into fast and flexible decision-making: how indecision can be broken as fast as it becomes costly, how sensitivity to stimulus can be tuned as context and environment change, how social heterogeneity can enhance stability and flexibility, and how excitability (spiking) provides further agility and frugality. I will discuss the significance of these results for the study and design of collective intelligence in nature and technology.

ARNI Continual Learning Working Group Project
2025-09-15 15:00 - 16:00 | CSB 480, Mudd Building, 500 W 120th Street
Monday (9/15) the Continual Learning Group will have a presentation from group member Yunfan Zhang. Yunfan will be sharing his ongoing work on developing a continual learning benchmark based on deriving up-to-date facts from news over time.

Date: Monday, September 15
Time: 3-4pm
Room: CSB 480
Zoom: upon request @ arni@columbia.edu

CTN: Dani Bassett
2025-09-19 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title and Abstract: TBD
Zoom:
Meeting ID: 993 3345 6502
Passcode: [REDACTED] request @ arni@columbia.edu

ARNI Continual Learning Working Group Project
2025-09-22 15:00 - 16:00 | CSB 480, Mudd Building, 500 W 120th Street
Zoom Link: Upon request @ arni@columbia.edu

CTN: Ann Kennedy
2025-09-26 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Neural computations underlying the regulation of motivated behavior
Abstract: As we interact with the world around us, we experience a constant stream of sensory inputs, and must generate a constant stream of behavioral actions. What makes brains more than simple input-output machines is their capacity to integrate sensory inputs with an animal’s own internal motivational state to produce behavior that is flexible and adaptive. In this talk, I will present three recent stories from the lab exploring the dynamics and modulation of motivational states. First, working with neural recordings from a hypothalamic nucleus involved in regulation of aggression, I show how we relate the dynamical properties of neural populations to escalation of an aggressive motivational state. Next, using methods from control theory and reinforcement learning, I show that different sites of modulation within a neural circuit produce different resulting effects on behavior and neural activity. Finally, I will show how theoretical models can reveal unexpected effects of neuromodulation on the dynamic regimes of recurrent neural networks, illuminating the ways in which the brain might use small molecules to reshape its activity and thus modify behavior.

ARNI Continual Learning Working Group Project
2025-09-29 15:00 - 16:00 | CEPSR 620, Schapiro 530 W. 120th St
Next Monday, September 29, we will continue our fall semester program with a group workshop on the topic of neural memory models. First, we will have a presentation from group member Max Bennett about our ongoing work on generalized neural memory systems that perform flexible updates based on learning instructions specified in natural language. After the presentation, we will spend some time in open discussion of memory models, and hopefully discuss potential (interdisciplinary) projects for the group.

Next Meeting Info

Date: Monday, September 29
Time: 3-4pm
Room: CEPSR 620
Zoom: upon request @ arni@columbia.edu

CTN: Reza Shadmehr
2025-10-03 11:30 - 13:00 | 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
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
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
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
