ARNI

CTN: Inês Laranjeira
2025-04-25 11:30 - 13:00 (America/New_York) | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: The structure of individuality in micro-behavioral features of task performance
Abstract: Individuality is an intrinsic and essential aspect of mammalian behavior that emerges even in genetically identical organisms experiencing the same environmental conditions. In the International Brain Laboratory (IBL), mice were trained on a visual decision-making task with the explicit goal of establishing a rigorously standardized experimental protocol. This effort led to an automated pipeline that produced trained mice whose behavior was indistinguishable across seven different labs, when considering trial-level descriptors of behavior. Nevertheless, substantial inter-individual variability was evident in both training time and proficient behavior, but its nature remains poorly characterized. To address this, we developed a behavioral segmentation approach to characterize mouse behavior across multiple variables. This yielded a discrete space of behavioral syllables which we further analyzed in the context of the trial structure. Variability in the expression of behavioral syllables was highly non-random, revealing structure in how different behavioral features co-vary at the sub-trial level. Moreover there was further evidence that mice fell into several clusters, suggestive of strategy types or even mouse personality types. The micro-behavioral structure derived from trained mice was further informative of differences in learning speed across individual mice, supporting its stability and biological significance. Overall, these results provide evidence that even in a cohort of mice whose overt task performance behavior is indistinguishable, there exist latent variables, manifesting in the details of micro-behavioral features, which appear to explain important aspects of behavioral individuality.

ARNI Emerging Researchers Symposium
2025-04-25 12:00 - 17:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
This event is for all ARNI trainees and graduate students who work on an ARNI related project. The goal of this symposium is to foster networking and career development.
Location: Zuckerman Institute L3-079
Time: 12pm to 5pm
Registration form: https://forms.gle/4e2AHqP54X8VvkKS8

Program

12pm: Lunch

Catering by FUMO

1pm: ARNI Postdoc Presentations

Haozhe Shan
Mehdi Azabou

2pm: Postdoc Panel Session

Academic Research and Entrepreneurship

2:45pm – 3:15pm: Break

3:15pm: Academic Speed Dating

4pm: Large Group Discussion

5-6 important but challenging and controversial topics in understanding the principle of intelligence and NeuroAI

ARNI Biological Learning Working Group
2025-04-29 11:00 - 12:00
The working group will discuss: https://www.nature.com/articles/s41593-020-0671-1
Join via Google Meets: meet.google.com/nnq-csiy-yah

Lecture in AI: Eric Xing
2025-04-29 11:00 - 12:00 | Davis Auditorium, 530 W 120th St, New York, NY 10027, New York, NY, 10027
Columbia Engineering Lecture Series in AI
“Toward General and Purposeful Reasoning in Real World Beyond Lingual Intelligence“
April 29: Dr. Eric Xing, President of Mohammed bin Zayed University of Artificial Intelligence
Register here!
Schedule

10:30AM-11:00AM Registration
11:00AM-12:00PM Lecture

Advance registration is required for both Columbia affiliates and non-affiliates
ABOUT THE SPEAKER
Professor Xing is the inaugural president of MBZUAI, where he has led the university’s remarkable growth in AI research and assembled a world-class faculty. Under his leadership, MBZUAI has developed a platform for students and faculty to advance research aligned with national priorities, balancing excellence in both fundamental research and translational R&D. His vision has fostered numerous high-profile partnerships with institutions like IBM, Carnegie Mellon University, and the Weizmann Institute of Science, while also creating specialized training programs for senior executives and government leaders. He has also overseen the development of state-of-the-art facilities, including a supercomputing center optimized for AI research.
A world-renowned computer scientist, Professor Xing has made pioneering contributions to statistical machine learning, including innovations in distance metric learning, network analysis, and distributed machine learning systems. His research spans core machine learning, large-scale system architecture, healthcare applications, and computational biology. He is a champion for open-source AI, having founded the CASL project to standardize AI operating systems for better scalability and industry integration. Professor Xing is a recipient of numerous prestigious awards, including the NSF Career Award and the Alfred P. Sloan Research Fellowship. He is a fellow of multiple esteemed organizations, including the AAAI, IEEE, and ACM.

ARNI Frontier Models for Neuroscience and Behavior Working Group (Priorly: Animal Behavior)
2025-04-30 14:00 - 16:00 | Zuckerman Institute – L7-119, 3227 Broadway, New York, NY, 10027, United States
Description: Advances in neurotechnology and behavioral tracking have enabled the collection of large-scale neural and behavioral datasets, offering new opportunities to study brain function in complex settings. However, researchers face significant challenges in integrating and analyzing data across different brain regions, individuals, and behavioral contexts. Inspired by recent successes in large-scale “foundation” models in natural language and computational biology, there is growing interest in developing both neurofoundation models—which learn from diverse neural recordings—and behavior foundation models—which capture structure in high-dimensional behavioral data. These models can be used for downstream tasks such as neural encoding and decoding, cell-type classification, and behavioral prediction. The goal of this working group is to explore this emerging field, address challenges in scaling these models, identify needs for benchmarks and tools to accelerate progress, and determine how these models can be used to answer scientific questions in both neuroscience and behavioral research.
Meeting Description: Our inaugural meeting will begin with a short talk summarizing the landscape of foundation models for neuroscience and behavior, highlighting recent advances, key challenges, and opportunities for improvement. This will be followed by an open discussion to define the group’s collaborative focus and shared priorities.
Zoom Link: https://columbiauniversity.zoom.us/j/95445157877?pwd=[REDACTED]

CTN: Kenneth Harris
2025-05-02 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Multidimensional structure of activity in transcriptomically identified cortical cell types
Abstract: The cerebral cortex is comprised of hundreds of distinct cell types, connected into a network that underpins cognition. To characterize the geometry of population activity in these cells, we recorded from thousands of neurons simultaneously in mouse visual cortex, and used post-hoc in situ transcriptomics to characterize their fine subtypes. We found that cortical population activity is organized around a single dimension shared between the subspace of spontaneous activity and responses to natural image stimuli, and that a neuron’s coupling with this dimension can be predicted from a its transcriptome.
Spontaneous population activity differed substantially with the animal’s behavioral state. Population activity in alert states could be classified by two primary dimensions, the first distinguishing locomoting vs. stationary periods, and the second correlating with running speed; neurons correlated positively and negatively with both dimensions. In non-alert states, spontaneous activity was organized around a single dominant dimension which spontaneously oscillated, and was close to orthogonal to the two running-related dimensions. The coupling of neurons with this shared dimension was almost always non-negative.
Natural image stimuli drove population activity in a subspace that overlapped the subspace of spontaneous activity only in one dimension: the dimension that was active in spontaneous oscillations of non-alert states. Even though this dimension was spontaneously oscillatory only during non-alert states, sensory responses along this dimension were larger during running periods. This single shared dimension accounted for a larger fraction of total stimulus-related variance in inhibitory than excitatory neurons, and in superficial than deep excitatory populations.
The neurons to these three dimensions could be predicted from their transcriptomes. This prediction was weaker amongst superficial excitatory neurons, whose strongest transcriptomic prediction was found for coupling to the oscillatory dimension.
We conclude that cortical activity is organized around a single dimension shared between spontaneous oscillations and natural image responses, and hypothesize that this dimension encodes the salience of sensory or non-sensory messages broadcast from visual cortex to other cortical regions.

Workshop on Emerging Trends in AI
2025-05-05 08:30 - 2025-05-06 17:00 | Pulitzer Hall, 2950 Broadway, New York, NY, 10027, United States
May 5th and 6th
8:30am to 5:30pm
Pulitzer Hall – 2950 Broadway New York, NY 10027
This two-day workshop brings together leading experts in machine learning (ML) and neuroscience to examine two emerging themes: (1) the relationship between brain resilience and algorithmic robustness, and (2) the role of ML-driven data generation in social sciences and the possible acceleration of scientific discovery. The workshop will hold two panels: one on how insights from ML and neuroscience can potentially inform each other toward the development of more resilient and robust systems, and the second on the ethical and practical implications of synthetic data in shaping research outcomes and policy decisions.
The workshop will be hosted by Columbia Engineering, the NSF AI Institute for Artificial and Natural Intelligence (ARNI), and Simons Institute for the Theory of Computing.
Register here!

ARNI Continual Learning Working Group Project
2025-05-07 13:00 - 14:00, 2025-05-21 13:00 - 14:00 | CEPSR 620, Schapiro 530 W. 120th St
Zoom link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

ARNI Emerging Researchers Talk Series #3: Matteo Alleman
2025-05-08 14:00 - 15:00
Title: Discovery of categorical concepts

Abstract: We seem to like reasoning in terms of discrete, logical categories, even in the face of continuous variation. A generous interpretation of this phenomenon is that we create abstractions about the world which enable powerful generalization to new situations — once I know what “hot-and-sour” is, I can instantly know if I will like any dish. But state of the art learning systems, and as far as we can tell our own brains, use highly distributed, continuous representations. A way that we reconcile these two ideas — abstract symbols and continuous representations — is to imagine that there are certain “component-level” directions in the vector space which are re-used across all instances which contain the component. Like the famous “king:queen :: man:woman” square from Miklov et al (2013). This provides a clear way of encoding abstract categories in continuous vector space, but is it possible to go the other way, and infer the categories from the vectors?
The first thing that may come to mind when hearing about “inferring categories” is clustering. But this has a limitation — you cannot put together “king” and “man” into a “male” category while also clustering “king” and “queen” into a “monarch” one. It also doesn’t quite seem right to say that “king” is 0.5 “male” and 0.5 “monarch” (a soft clustering), or that one cluster is a subset of the other (a hierarchical clustering). Instead, I want a way to group together items into multiple clusters based on their vector representations. When data are free to belong to multiple clusters (not in a sum-to-one probabilistic way) it becomes most natural to think of the problem as a matrix factorization, where we are trying to express our data as the product of an otherwise unconstrained binary matrix of item-cluster assignments (call it S), and a real-valued matrix of cluster means (call it W), i.e. X = SW. That is the model I will present on.
Zoom Link: https://columbiauniversity.zoom.us/j/92200158294?pwd=[REDACTED]

CTN: Stephanie Palmer
2025-05-09 11:30 - 13:00 | 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.

ARNI Continual Learning Working Group Project
2025-05-14 13:00 - 14:00

CTN: Gaia Tavoni
2025-05-16 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: A Unified Framework for Sensory Coding in Feedback-Modulated Canonical Networks

Abstract: In recent decades, the principles of neural coding have largely been studied at the level of single neurons or unimodal sensory networks. However, brain networks interact in complex ways, often integrating information across sensory modalities. Notably, we lack a theoretical framework for understanding coding in interacting networks, where information can converge from different sources. In this talk, I will introduce a fully analytical normative framework for neural coding in feedback-modulated canonical networks, a ubiquitous motif in the brain. In our model, feedback is exogenous rather than endogenous to a given modality, mediating interactions between the senses. Our theory demonstrates that predictive coding is an emergent property of efficient codes, unifying two primary coding schemes. It further demonstrates how the computational principles of efficient and predictive coding can be implemented at the algorithmic level by a shared neural substrate, with different network components performing distinct and interpretable mathematical operations. Finally, our theory explains a variety of observed unimodal and multimodal sensory effects within the same normative framework and makes new predictions about the role of feedback in optimizing multimodal codes. I will conclude by showing how optimal sensory codes can be learned in biological networks through distributed Hebbian learning. Altogether, our theory provides a unifying view of computational, algorithmic, and implementational principles of sensory coding in feedback-modulated canonical networks.

CTN: Erin Rich
2025-05-23 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Dynamics of evaluation and choice in the orbitofrontal cortex

Abstract: The orbitofrontal cortex is known to be important for evaluation, choice, and motivated behavior, but theories on its precise role have continually evolved. In this talk, I will discuss new perspectives on evaluation and choice in the orbitofrontal cortex in light of work from our lab and others that emphasizes information encoded by neural populations and their dynamics. I will argue that a useful framework to understand these data comes from theories of embodied decision-making, in which moment-by-moment processing is determined by ongoing interactions between the individual and their environment.

ARNI Frontier Models for Neuroscience and Behavior Working Group (Priorly: Animal Behavior): Meeting 2
2025-05-28 14:00
Speakers: Matt Whiteway and Mehdi Azabou
Meeting Description: We will share progress on a benchmark using the IBL brain-wide map dataset
Working group Description: Advances in neurotechnology and behavioral tracking have enabled the collection of large-scale neural and behavioral datasets, offering new opportunities to study brain function in complex settings. However, researchers face significant challenges in integrating and analyzing data across different brain regions, individuals, and behavioral contexts. Inspired by recent successes in large-scale “foundation” models in natural language and computational biology, there is growing interest in developing both neurofoundation models—which learn from diverse neural recordings—and behavior foundation models—which capture structure in high-dimensional behavioral data. These models can be used for downstream tasks such as neural encoding and decoding, cell-type classification, and behavioral prediction. The goal of this working group is to explore this emerging field, address challenges in scaling these models, identify needs for benchmarks and tools to accelerate progress, and determine how these models can be used to answer scientific questions in both neuroscience and behavioral research.
Location: ZI L4-082
Zoom Link: https://columbiauniversity.zoom.us/j/95445157877?pwd=[REDACTED]

ARNI WG Multi-resource-cost optimization of neural network models: Mitya Chklovskii
2025-05-29 15:00 - 17:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Can resource optimization explain neuronal morphology and placement?
Abstract: TBD
Zoom: https://columbiauniversity.zoom.us/j/98788275902?pwd=[REDACTED]

CTN: Shaul Druckmann
2025-05-30 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Neural dynamics of short term memory, from mice to human speech
Abstract: Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed, even in extremely simplified experimental conditions, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning, i.e., a small change in the evolution of activity along a particular dimension may have a bigger effect on a given computation than a large change in another. We term such conditions dimension-specific computation.
I will first briefly discuss our work on motor preparatory activity in a delayed response task in mice were we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. We first revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, as if the circuit was setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations.
I will then present unpublished work on understanding the neural dynamics underlying preparation of speech. First, we found that as words or brief sentences are being prepared to be spoken, we can decode not just the first phoneme to be spoken, but rather multiple components of the speech sequence are decodable, i.e., prepared in parallel. Second, we found that unlike some previous descriptions of sequence preparation, components were not always separated into distinct subspaces, but rather were found in overlapping subspaces with a structured organization of their neural geometry by element and sequence position.

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!

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

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: 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.

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

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: 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

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

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

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

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.
