ARNI

ARNI Continual Learning Working Group
2025-10-27 15:00 - 16:00 (America/New_York) | 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 Biological Learning Working Group
2025-10-24 14:00 - 15:00
Continuation from prior meetings about benchmarks and competition proposals.
Zoom Link: upon request @ ARNI@columbia.edu

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

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

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.

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

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

CTN: Reza Shadmehr
2025-10-03 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States

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: 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
2025-09-22 15:00 - 16:00 | CSB 480, Mudd Building, 500 W 120th Street
Zoom Link: 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
2025-09-15 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: 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
2025-09-08 15:00 - 16:00 | CSB 480, Mudd Building, 500 W 120th Street
Zoom link: Upon request @arni@columbia.edu

CTN: Christine Constantinople
2025-09-05 11:30 - 13:00 | 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.

Speakers: Vinam Arora and Ji Xia – ARNI Frontier Models for Neuroscience and Behavior Working Group
2025-08-27 14:00 - 16:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title and Abstracts:
1st Speaker: Vinam Arora, UPenn
Title: Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
Abstract: Identifying the functional identity of individual neurons is essential for interpreting circuit dynamics, yet remains a major challenge in large-scale in vivo recordings where anatomical and molecular labels are often unavailable. Here we introduce NuCLR, a self-supervised framework that learns context-aware representations of neuron identity by modeling each neuron’s role within the broader population. NuCLR employs a spatiotemporal transformer that captures both within-neuron dynamics and across-neuron interactions, and is trained with a sample-wise contrastive objective that encourages stable, discriminative embeddings across time. Across multiple open-access datasets, NuCLR outperforms prior methods in both cell type and brain region classification. It enables zero-shot generalization to entirely new populations—without retraining or access to stimulus labels—offering a scalable approach for real-time, functional decoding of neuron identity across diverse experimental settings.
2nd Speaker: Ji Xia, Columbia
Title: In painting the neural picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets.
Abstract: Understanding how the brain drives memory-guided movements requires recording neural activity from the motor cortex and interconnected subcortical areas. Neuropixels probes now allow simultaneous recordings from subsets of these areas, but no single session captures all areas of interest, and different neurons are sampled from each area across sessions. This poses a key challenge: how to integrate neural data across sessions to reconstruct the complete multi-area picture. We address this with a transformer-based autoencoder that aligns neural activity into a shared latent space across sessions and animals, separately for each brain area, including those not recorded in a given session. This approach enables single-trial analysis of multi-area neural dynamics from all areas of interest. I am now working on improving this method, and will discuss both its present challenges and promising directions for future work.
Zoom: Upon request at @ arni@columbia.edu.

Speaker: Kwabena Boahen ARNI WG Multi-resource-cost optimization of neural network models
2025-08-07 14:30 - 16:30 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: From 2D Chips to 3D Brains
Abstract:
Artificial intelligence (AI) realizes a synaptocentric conception of the learning brain with dot-products and advances by performing twice as many multiplications every two months. But the semiconductor industry tiles twice as many multipliers on a chip only every two years. Moreover, the returns from tiling these multipliers ever more densely now diminish, because signals must travel relatively farther and farther, expending energy and exhausting heat that scales quadratically. As a result, communication is now much more expensive than computation. Much more so than in biological brains, where energy-use scales linearly rather than quadratically with neuron count. That allows an 86-billion-neuron human brain to use as little power as a single lightbulb (25W) rather than as much as the entire US (3TW). Hence, rescaling a chip’s energy-use from quadratic to linear is critical to scale AI sustainably from trillion (1012) parameters (mouse scale) today to a quadrillion (1015) parameters (human scale) in the next five years. But this would require communication cost to be reduced radically. Towards that end, I will present a recent re-conception of the brain’s fundamental unit of computation that sparsifies signals by moving away from synaptocentric learning with dot-products to dendrocentric learning with sequence detectors.
Zoom: Request @ ARNI@columbia.edu

Speaker: Memming Park – ARNI Frontier Models for Neuroscience and Behavior Working Group
2025-07-30 14:00 - 16:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: Meta-dynamical state space modeling for integrative neural data analysis
Abstract:
Uncovering the organizing principles of neural systems requires integrating information across diverse datasets—each alone offering a limited view and signal-to-noise ratio, but together revealing coherent dynamical structures. We present a meta-dynamical state-space modeling framework that learns a shared solution space of neural dynamics from heterogeneous recordings across sessions, animals, and tasks. By capturing cross-dataset similarity and variability on a low-dimensional manifold that spans a space of dynamical systems, our approach enables few-shot inference, rapid adaptation to new recordings, and discovery of latent dynamical motifs that underlie behavior. We demonstrate its utility in modeling motor cortex activity, revealing dynamics that generalize across individuals and track the change in dynamics during learning. We argue that for understanding neural computation and real-time neuroscience applications, our approach is well-suited as a foundation model for integrative neuroscience.
Zoom: Request @arni@columbia.edu

AI and Neuroscience/Cognitive Science Activities Brainstorming
2025-07-15 18:00 - 19:30
ARNI will host an informal brainstorming session on July 15th (ZI Education Lab) focused on developing AI and neuroscience/cognitive science activities for K–12 students. The goal is to create engaging ways to help young learners better understand the brain and artificial intelligence. Trainees are encouraged to attend—if you’re interested in making an impact on youth education, this is a great opportunity to get involved. Join if you are free! There will be free pizza!
This is the registration form!

Biological Learning Working Group Meeting
2025-07-09 11:00 - 12:30 | Virtual
Continuation of the prior meeting.
Zoom: Upon request @arni@columbia.edu

CTN: Blake Richards
2025-06-27 11:30 - 13:30 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Brain-like learning with exponentiated gradients
Abstract: Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that it is effective at learning difficult tasks. However, it produces ANNs that are a poor phenomenological fit to biology, making them less relevant as models of the brain. Specifically, it violates Dale’s law, by allowing synapses to change from excitatory to inhibitory, and leads to synaptic weights that are not log-normally distributed, contradicting experimental data. Here, starting from first principles of optimisation theory, we present an alternative learning algorithm, exponentiated gradient (EG), that respects Dale’s Law and produces log-normal weights, without losing the power of learning with gradients. We also show that in biologically relevant settings EG outperforms GD, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether, our results show that EG is a superior learning algorithm for modelling the brain with ANNs.
Zoom Link: By Request

Speaker: Dr. Guillaume Lajoie - ARNI Frontier Models for Neuroscience and Behavior Working Group
2025-06-25 14:00 - 15:30 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: POSSM: Generalizable, real-time neural decoding with hybrid state-space models
Abstract:
Real-time decoding of neural spiking data is a core aspect of neurotechnology applications such as brain-computer interfaces, where models are subject to strict latency constraints. Traditional methods, including simple recurrent neural networks, are fast and lightweight but are less equipped for generalization to unseen data. In contrast, recent Transformer-based approaches leverage large-scale neural datasets to attain strong generalization performance. However, these models typically have much larger computational requirements and are not suitable for settings requiring low latency or limited memory. To address these shortcomings, we present POSSM, a novel architecture that combines individual spike tokenization and an input cross-attention module with a recurrent state-space model (SSM) backbone, thereby enabling (1) fast and causal online prediction on neural activity and (2) efficient generalization to new sessions, individuals, and tasks through multi-dataset pre-training. We evaluate our model’s performance in terms of decoding accuracy and inference speed on monkey reaching datasets, and show that it extends to clinical applications, namely handwriting and speech decoding. Notably, we demonstrate that pre-training on monkey motor-cortical recordings improves decoding performance on the human handwriting task, highlighting the exciting potential for cross-species transfer. In all of these tasks, we find that POSSM achieves comparable decoding accuracy with state-of-the-art Transformers, at a fraction of the inference cost. These results suggest that hybrid SSMs may be the key to bridging the gap between accuracy, inference speed, and generalization when training neural decoders for real-time, closed-loop applications.
Zoom Link: Request via email arni@columbia.edu

CTN: Andrew Saxe
2025-06-20 11:00 - 13:00
Zoom Link: https://columbiauniversity.zoom.us/j/92032394293?pwd=[REDACTED]

ARNI Continual Learning Working Group Project
2025-06-17 15:00 - 16:00 | CEPSR 620, Schapiro 530 W. 120th St
Title: Benchmark Development for Lifelong Learning in LLMs

Abstract: The ARNI Continual Learning working group continues its work towards developing a benchmark for lifelong learning in LLMs. Discussions will be centered around learning over time as well as catastrophic forgetting in LLM post-training.
Zoom link: By request

CTN: Preeya Khanna
2025-06-13 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Mapping and Mending Dexterous Movement Control with Neurotechnology

Abstract: Dexterous movement is a hallmark of human motor ability, enabling us to interact skillfully with our environment. The loss of this capability due to movement disorders, such as Parkinson’s disease or stroke, strips individuals of independence and quality of life. This talk explores the neural underpinnings of dexterity, focusing on how the nervous system integrates sensory and motor signals to achieve precise control. We then examine how these mechanisms break down in movement disorders, leading to impaired motor function. Finally, we turn to neuroengineering technologies which aim to restore movement in affected individuals. By leveraging advances in neural interfaces and wearable systems, we are seeking to design systems to repair motor function. Overall, we highlight our highly interdependent scientific and translational goals to understand and restore complex movement.

Saturday Science
2025-06-07 13:00 - 16:00 | JLGSC, 605 W 129th Street
If you have children or know any young people interested in hands-on STEM fun, bring them to Saturday Science, hosted by Columbia’s CUNO group! The event takes place on Saturday, June 7th at the Jerome L. Greene Science Center (605 West 129th Street). Kids will enjoy engaging, interactive activities while exploring exciting science concepts. ARNI will also host a station featuring an image classifier activity that highlights the similarities between how AI and the brain process information. You can attend at any time but we also suggest that you complete this registration form!

CTN: György Buzsáki
2025-06-06 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Seminar Time: 11:30am
Date: Fri 6/6/25
Seminar Location: JLG, L5-084
Host: Erfan Zabeh

Title: Selection and consolidation of memory

AI and Neuroscience/Cognitive Science Activities Brainstorming
2025-06-03 18:00 - 19:30 | Fairchild 700, 1212 Amsterdam Ave
ARNI will host an informal brainstorming session on June 3rd at 6pm (Fairchild 700) focused on developing AI and neuroscience/cognitive science activities for K–12 students. The goal is to create engaging ways to help young learners better understand the brain and artificial intelligence. Trainees are encouraged to attend—if you’re interested in making an impact on youth education, this is a great opportunity to get involved. Join if you are free!
This is the registration form!
