ARNI

ARNI Continual Learning Working Group Project
2025-05-14 13:00 - 14:00 (America/New_York) | NY
Zoom link: https://columbiauniversity.zoom.us/j/97176853843?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 Emerging Researchers Talk Series #3: Matteo Alleman
2025-05-08 14:00 - 15:00 | NY
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]

ARNI Continual Learning Working Group Project
2025-05-07 13:00 - 14:00, 2025-04-23 13:00 - 14:00 | CEPSR 620, Schapiro 530 W. 120th St

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!

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.

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]

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

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

CTN: Inês Laranjeira
2025-04-25 11:30 - 13:00 | 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.

CTN: Ivan Davidovich
2025-04-23 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Uncovering latent low-dimensional structure in network connectivity
Abstract: Network connectivity constrains the patterns of neural activity in the brain. These constraints are often observed as low-dimensional manifolds in neural activity space. Continuous Attractor Networks (CANs) are a prime example of this type of network phenomenon. Interestingly, there are examples of CANs where the structure or topology of the manifold observed in the space of neural activity does not match the corresponding structure or topology of the connection weights in the network. To learn more about this relationship, we need to go beyond studying the structure of neural activity and investigate the structure in the connectivity of those systems. To this end, we wish to identify a minimal set of parameters, or coordinates, that are enough to characterize the connectivity weights between any pair of neurons given their coordinate values. In the simplest cases, this is equivalent to finding an appropriate ordering (labelling) of cells that will reveal the underlying structure in the connectivity weights. Traditional approaches use properties of neural activity, such as neural selectivity, to identify such an ordering. However, there are many situations that are not amenable to this treatment, either because neural activity data is not available, for example in connectome data sets, because tuning curves are disordered, or because of the particular architecture of the network. To address this issue, we employ tools from Dimensionality Reduction and Topological Data Analysis to uncover structure directly from the connectivity weights in different examples of CAN models. I will show that this approach can uncover connectivity structure that is different from the one observed in activity space, and in some cases works even when a fairly large fraction of neurons in the system is not observed. We argue that this perspective towards the study of structure in network connectivity can lead to the discovery of organization in cases where no obvious structure is present in the activity of the neural population, or where connectomics data is available without corresponding activity recordings.

ARNI Emerging Researchers Talk Series #2: Itzel Olivos-Castillo
2025-04-22 15:00 - 16:00 | NY
Bio: Itzel is a Ph.D. student at Rice University working with Prof. Xaq Pitkow. She studies perception and control mechanisms that give biological organisms an advantage over machines. She believes understanding how the brain works using mathematical principles is essential to build the next generation of AI systems which are more robust, more general-purpose, less artificial, and more intelligent. She holds a bachelor’s degree (Telematics Engineering) and master’s degree (Computer Science) from Instituto Politécnico Nacional (IPN-Mexico).
Title: Resource-Efficient Control in Brains and Machines
Abstract:
The brain can turn noisy stimuli into rational behaviors that address a wide variety of tasks using limited
experience, relying on limited processing capacity, and consuming less energy than a lightbulb. What makes the
brain such an efficient control system? Cognitive studies have identified meta-reasoning, the ability to reason
about one’s own reasoning process, as a crucial factor behind this remarkable performance. However, it remains
unclear how meta-level rational agents—whether biological or artificial—successfully balance internal
computation costs against task performance in uncertain environments. To help bridge this gap, we develop a
novel approach to stochastic control where the internal computation cost of inference (a resource-intensive
mechanism that aids in mitigating uncertainty) is optimized alongside task performance. We apply our framework
to quantitatively examine how meta-level rational agents solve Linear Quadratic Gaussian problems. Our findings
reveal that when the estimation error is a meta-control variable the agent can regulate, the dynamics of inference
and control become tightly coupled. This coupling leads to intriguing phase transitions in what is worth
representing, switching from a costly but maximally informative strategy to a family of solutions that differ in
how the agent integrates new evidence, corrects estimation errors, and models the world to lessen the burden of
optimal inference. The fundamental principles we found generalize efficient coding ideas, extend the principle of
minimal intervention in control, and provide a foundation for a new type of rational behavior that both brains and
machines could use for effective but computationally constrained control.
Zoom Link: https://columbiauniversity.zoom.us/j/91436346202?pwd=[REDACTED]

CTN: Sam Gershman
2025-04-18 11:30 - 13:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: Reimagining the biology of memory

Abstract: Over the last half century, there has been a remarkable convergence on the idea that memories are stored at synapses. I will argue that this is only part of the story. A more complete story compels us to recognize the radical ubiquity of memory in living systems, including free-living unicellular organisms and many kinds of non-neural cells. Memory existed from the moment life began; in a sense it is built into the logic of life. Its molecular mechanisms are therefore likely to be ancient in origin, and a number of clues are already available. Computational considerations help us organize these clues into a theory of the division of labor and interaction between cell-intrinsic and synaptic storage mechanisms. From this new starting point, I will explore how we can make sense of many strange and puzzling phenomena: the transfer of memory between organisms, the survival of memory after radical synaptic remodeling (even decapitation!), the transience of amnesia following protein synthesis inhibition, and the ability of unicellular organism to learn, among others.
Zoom Link: https://columbiauniversity.zoom.us/j/91727702242?pwd=[REDACTED]

ARNI Continual Learning Working Group Project
2025-04-16 13:00 - 14:00 | CEPSR 620, Schapiro 530 W. 120th St
LLM Benchmarks
Zoom link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

CTN: Alex Williams
2025-04-11 11:30 - 13:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: Quantifying individuality in neural circuit representations

Abstract: Signatures of neural computation are thought to be reflected in the coordinated activity of large neural populations. Neuroscience is now flush with measurements of these activity patterns in humans, animal subjects, and large-scale artificial network models. In this talk, I will address an extensively studied, yet unresolved, question: How should we quantify the extent to which two or more neural circuits have “similar” activation patterns? Without an answer to this question, the field has struggled to investigate basic questions about biological variability and individuality, such as: How do neural representations vary across a healthy population? How do differences in neural population activity correlate with behavioral idiosyncrasies and disorders? How similar are computational mechanisms in biological brains and artificial neural networks? In this talk, I will summarize several mathematical methods that quantify similarity in neural representations and demonstrate how they provide early insights into these questions when applied to biological data and artificial networks.

Zoom Link: https://columbiauniversity.zoom.us/j/93699792071?pwd=[REDACTED]

CTN Special Speaker Steve Fleming
2025-04-09 13:00 - 14:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: How the human brain thinks about itself

Abstract: The human brain has a remarkable ability to monitor and evaluate its own mental states, known as metacognition. Metacognition is crucial to success, enabling us to recognise gaps in our knowledge and collaborate effectively. Problems with metacognition are linked to maladaptive behaviours, such as endorsing false beliefs or being unaware of our own limitations. In my talk I will review the development of experimental and modelling tools that allow us to isolate how metacognitive capacity relates to human brain function and supports a rich awareness of our skills and capabilities. I will explore the psychological structure of metacognition across different tasks and cognitive domains, and ask how self-evaluative judgment contributes to belief formation and changes of mind. I’ll end by considering the implications of a science of metacognition for mental health, education and AI.

Zoom link: https://columbiauniversity.zoom.us/j/93699792071?pwd=[REDACTED]

ARNI Continual Learning Working Group Guest Speaker: Christopher A. Baldassano
2025-04-09 13:00 - 14:00 | CEPSR 620, Schapiro 530 W. 120th St
Speaker: Christopher A. Baldassano

Title: Remembering events using schematic knowledge
Abstract: Our everyday experiences consist of familiar sequences of events in familiar contexts, and we use our knowledge of the past to understand and remember the present. Research in my lab combines behavioral, eye-tracking, and neuroimaging methods to investigate how prior knowledge of temporal and spatial structure impacts perception and memory, by allowing participants to draw on their real-world experiences or build detailed expertise in controlled yet naturalistic domains. I’ll discuss our recent studies showing how the brain’s internal cognitive models can be used to organize event perception in narratives, structure episodic memories, and anticipate upcoming information. These studies argue for a central role of top-down and anticipatory processes in constructing neural event memories.

Zoom link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

ARNI Emerging Researchers Talk Series #1: Rahul Ramesh
2025-04-08 15:00 - 16:00 | NY
Title: Principles of Learning from Multiple Tasks

Abstract:

Deep networks are increasingly trained on data from multiple tasks with the goal of sharing synergistic information across related tasks. A language model, for example, is trained on 10 trillion tokens on tasks ranging from programming, finance, trivia to translation and a vision model is trained on over a billion images for tasks like object recognition, depth prediction and semantic segmentation. With this motivation, in this talk, I will present the principles behind how to optimally train on multiple tasks and attempt to answer why we are able to learn on these tasks. In the first part of the talk we develop a theory that shows that dissimilar tasks fight for model capacity when trained together. We use this insight to design Model Zoo — a learner that splits its capacity to train many small models on related subsets of tasks — which is state-of-the-art for task-incremental continual learning. In the second half of this talk, we show that typical tasks are highly redundant functions of the input, i.e., the subspaces that vary the most and ones that vary the least are both highly predictive of typical tasks. This result suggests that there are many subspaces that can be used to solve typical tasks, which allows us to learn a shared representation for these tasks. We believe that organisms choose to solve redundant tasks because they are the only ones that agents with bounded resources can readily learn.

Speaker Bio:
Rahul Ramesh is a 6th year PhD student at the University of Pennsylvania in the department of computer and information science and is advised by Pratik Chaudhari. He previously received his B.Tech from the Indian Institute of Technology Madras in Computer science and Engineering. Rahul is interested in using perspectives from statistical learning theory, information theory and neuroscience to study self-supervised and multitask learning.

Zoom Link: https://columbiauniversity.zoom.us/j/91436346202?pwd=[REDACTED]

Girls' Science Day
2025-04-05 09:00 - 17:00 | NY
ARNI is committed to promoting science education among New York City’s youth. This year, ARNI is supporting Girls’ Science Day on April 5, 2025.
Location: TBD
Mission
Girls’ Science Day at Columbia University seeks to champion the advancement of women and underrepresented groups in the fields of science, technology, engineering, and math (STEM). By offering a full day of hands-on experiments, we aim to provide middle school girls (5th– 8th grade) with an engaging introduction to science, spark their curiosity and confidence so they can envision themselves as the next generation of STEM explorers. Purpose Girls’ Science Day is designed to offer participants immersive, hands-on experiments led by Columbia students. It serves as a lively, fun, and accessible entry point into STEM, providing opportunities for active learning and reflection.
Goals
1. Empower Young Scientists: Provide a welcoming space and foster curiosity and excitement about science among middle school girls.
2. Provide Mentorship: Connect participants with enthusiastic Columbia volunteers — undergraduates, graduate students, and postdocs—who can share personal journeys and inspiration.
3. Strengthen Community Ties: Keep building our local STEM network through close collaboration with parents, teachers, and NYC tri-state area schools.

ARNI Distinguished Seminar Series: Eftychios A. Pnevmatikakis, Research Scientist, Reality labs at Meta
2025-04-04 15:00 - 17:00 | NY
Research Scientist, Reality labs at Meta
Title: TBD
Location: TBD
Abstract: TBD

ARNI Continual Learning Working Group Project
2025-04-02 13:00 - 14:00 | CEPSR 620, Schapiro 530 W. 120th St
Continuation of meeting from prior working group meetings.
Zoom link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

CTN: Anna Levina and
2025-03-21 11:00 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: TBD
Abstract: TBD

CTN: Soledad Gonzalo Cogno
2025-03-19 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Soledad Gonzalo Cogno
Seminar Time: 11:30am
Date: Wed 3/19/25
Location: JLG, L5-084
Title: Ultraslow patterns of neural population activity in the entorhinal-hippocampal circuit
Note: Everything I will present in this talk is preliminary – Feedback and ideas will be very much appreciated!
Abstract: The medial entorhinal cortex hosts many of the brain’s circuit elements for spatial navigation and episodic memory, operations that require neural activity to be organized across long durations of experience. We have previously found that entorhinal cells can organize their activity into ultraslow oscillations (frequency < 0.1 Hz) that manifest as periodic sequences of activity in the neural population (Gonzalo Cogno et al., 2024). These ultraslow periodic sequences were recorded while mice ran at free pace on a rotating wheel in darkness, with no change in running direction and no scheduled rewards. It remains unknown, however, whether the sequences also occur during more naturalistic behaviours, for example while mice run in an open field arena, or during sleep. In this presentation I will show that in free foraging conditions, MEC neuronal activity can organize into sequences. However, the sequential activity is now characterized by resettings and interruptions. By developing a computational model, we investigate the conditions under which the sequences reset. In addition, we found that during slow-wave-sleep neural activity is also organized into ultraslow oscillations, but not into sequences. The oscillations also manifest in the hippocampus, and are highly synchronized with those in the MEC. These results suggest the presence of internal dynamics that unfold at ultraslow time scales, and that are modulated by sensory information and cognitive demands.
Because oscillations and sequences are not the only way into which neural activity can organize at ultraslow time scales, we next sought to determine whether other slowly changing patterns of activity are present in the MEC. If those exists, it is yet an open question whether, and how, those are transformed in the hippocampal-entorhinal circuit. We found that when animals ran at free pace on a rotating wheel in darkness, the activity in the MEC, lateral entorhinal cortex (LEC) and hippocampus slowly drifted over session time, enabling a readout of episodic time. However, the drift in the MEC and the hippocampus, but not in the LEC, significantly decreased when animals ran in an open field arena. These results suggest that the slow drift of hippocampal and MEC activity is attenuated by spatial landmarks when these are present.
All in all, our results point to the existent of ultraslow dynamics in the entorhinal-hippocampal circuit that may facilitate the encoding of experience at behavioral time scales.

CTN: Christian Machens
2025-03-14 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Computing with spikes: A geometric approach

Abstract: How can recurrent spiking networks perform computations in a biologically realistic regime? I will outline the progress we have made in answering this question. Our approach follows two principles. First, we don’t average over spikes, but focus on the contribution of each individual spike. Second, we study the decision to spike in a low-dimensional space of latent population modes (or readouts, components, factors, you name it) rather than in the original neural space. Neural thresholds then become convex boundaries in latent space, and the latent dynamics is either attracted (I population) or repelled (E population) by these boundaries. The combination of E and I populations results in balanced, inhibition-stabilized networks which are capable of producing (arbitrary) dynamical systems or input-output mappings. Moreover, there are profound differences between computation in these spiking networks compared to classical rate networks. I will illustrate all of these insights through geometrical pictures and movies and thereby demonstrate that we are far from having exhausted analytical and geometric methods in understanding recurrent spiking neural networks [joint work with William Podlaski].

ARNI Continual Learning Working Group
2025-03-11 16:00 - 17:00 | CEPSR 620, Schapiro 530 W. 120th St
Continuation of Year 3 proposal meeting!
Zoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

CTN: Tim Buschman
2025-03-07 11:30 - 13:00 | Zuckerman Institute – L7-119, 3227 Broadway, New York, NY, 10027, United States
Title: The geometry of cognitive flexibility

Abstract: Humans and animals are remarkably good at multi-tasking: we quickly learn many different tasks and flexibly switch between them. Theoretical work suggests such cognitive flexibility requires representing the current task and then using this task representation to selectively engage in task-relevant computations. In this talk, I will discuss recent research from my lab aimed at understanding the neural mechanisms underlying cognitive flexibility. I will discuss how tasks are represented in the brain and how new task representations can be learned. I will also discuss how the brain flexibly re-uses neural representations of sensory inputs and motor actions across different tasks. This allows the brain to compositionally construct complex tasks from simpler sub-tasks by routing task-relevant information between subspaces of neural activity.

Columbia AI Summit
2025-03-04 09:00 - 17:00 | NY
Columbia University is bringing its community together for an exhilarating, day-long exploration of artificial intelligence and its transformative impact across disciplines. Across the Morningside, Manhattanville, and Medical Center campuses, specialized workshops will dive deep into AI’s role in fields ranging from healthcare to the humanities. The event will feature a must-see keynote by Sami Haddadin, Director of the Munich Institute of Robotics and Machine Intelligence and Vice President for Research at MBZUAI.
Link: https://ai.columbia.edu/ai-summit#!#text-1655

ARNI Distinguished Seminar Series: Marlene Behrmann
2025-02-28 15:30 - 17:00 | Zuckerman Institute- Kavli Auditorium 9th Fl, 3227 Broadway, NY
About Dr. Marlene Behrmann:
Marlene Behrmann joined the Department of Ophthalmology at the University of Pittsburgh School of Medicine, where she holds the John and Clelia Sheppard Chair, in 2022. She also holds the position of Emeritus Professor at Carnegie Mellon University. Dr. Behrmann’s research is concerned with the psychological and neural bases of visual processing, with specific attention to the mechanisms by which the signals from the eye are transformed into meaningful percepts by the brain. She adopts an interdisciplinary approach combining computational, neuropsychological and neuroimaging studies with adults and children in health and disease. Examples of her recent studies include investigations of the cortical visual system in paediatric patients following hemispherectomy and identifying mechanisms of plasticity and elucidating the potential for cortical reorganization, but she has also studied visual cortical function in individuals with inherited retinal dystrophy. Dr. Behrmann was elected a member of the Society for Experimental Psychologists in 2008, and was inducted into the National Academy of Sciences in 2015, and into the American Academy of Arts and Sciences in 2019. Dr Behrmann has received many awards including the Presidential Early Career Award for Engineering and Science, the APA Distinguished Scientific Award for Early Career Contributions and the Fred Kavli Distinguished Career Contributions in Cognitive Neuroscience Award from the Cognitive Neuroscience Society.
Title: The development, hemispheric organization, and plasticity of high-level vision
Abstract:
Adults recognize complex visual inputs, such as faces and words, with remarkable speed, accuracy and ease, but a full understanding of these abilities is still lacking. Much prior research has favoured a binary separation of faces and words, with the right hemisphere specialized for the representation of faces, and the left hemisphere specialized for the representation of words. Close scrutiny of the data, however, suggest a more graded and distributed hemispheric organization, as well as differing hemispheric profiles across individuals. Combining detailed behavioral data with structural and functional imaging data reveals how the distribution of function both within and between the two cerebral hemispheres emerges over the course of development, and a computational account of this mature organization is offered and tested. Provocatively, this mature profile is more malleable than previously thought, and cross-sectional and longitudinal data acquired from individuals with hemispherectomy reveal how a single hemisphere can subserve both visual classes. Together, the findings support a view of cortical visual organization (and perhaps, the organization of other functions too) as plastic and dynamic, both within and between hemispheres.
Location: Zuckerman Institute, Kavli Auditorium 9th Floor (for access to Zuckerman Institute, please email Lena Mei @ lm3440@columbia.edu 24 hours prior to the event)
Zoom link: https://columbiauniversity.zoom.us/j/96156119664?pwd=[REDACTED]
