ARNI

CTN: Christian Machens
2025-03-14 11:30 - 13:00 (America/New_York) | 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]

ARNI Continual Learning Project
2025-02-27 13:00 - 14:00 | CEPSR 620, Schapiro 530 W. 120th St
Followup to discussion in Meeting 1
Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

ARNI Biological Learning Working Group
2025-02-26 14:00 - 15:00 | Virtual
Ken Miller will be talking about E/I networks & balanced networks and some computational/functional implications, there’s two papers I’d suggest reading:on balanced amplification: https://www.sciencedirect.com/science/article/pii/S0896627309001287 review of loosely and tightly balanced networks: https://www.sciencedirect.com/science/article/pii/S0896627321005754.

Meeting Link: meet.google.com/nnq-csiy-yah

ARNI WG Multi-resource-cost optimization of neural network models: Paul Schrater
2025-02-25 15:00 - 17:00 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title: Control when confidence is costly
Abstract:
We develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control, or efficient control that neglects the cost of synthesizing information. Here we combine these concepts into a framework where agents rationally approximate inference for efficient control. Specifically, we study Linear Quadratic Gaussian (LQG) control with an added internal cost on the relative precision of the posterior probability over the world state. This creates a trade-off: an agent can obtain more utility overall by sacrificing some task performance, if doing so saves enough bits during inference. We discover that the rational strategy that solves the joint inference and control problem goes through phase transitions depending on the task demands, switching from a costly but optimal inference to a family of suboptimal inferences related by rotation transformations, each misestimate the stability of the world. In all cases, the agent moves more to think less. This work provides a foundation for a new type of rational computations that could be used by both brains and machines for efficient but computationally constrained control.
We develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control, or efficient control that neglects the cost of synthesizing information. Here we combine these concepts into a framework where agents rationally approximate inference for efficient control. Specifically, we study Linear Quadratic Gaussian (LQG) control with an added internal cost on the relative precision of the posterior probability over the world state. This creates a trade-off: an agent can obtain more utility overall by sacrificing some task performance, if doing so saves enough bits during inference. We discover that the rational strategy that solves the joint inference and control problem goes through phase transitions depending on the task demands, switching from a costly but optimal inference to a family of suboptimal inferences related by rotation transformations, each misestimate the stability of the world. In all cases, the agent moves more to think less. This work provides a foundation for a new type of rational computations that could be used by both brains and machines for efficient but computationally constrained control.
Zoom Link: https://columbiauniversity.zoom.us/j/98244449046?pwd=[REDACTED]

ARNI Continual Learning Working Group Spring Opening Meeting
2025-02-18 16:00 - 16:45 | CEPSR 620, Schapiro 530 W. 120th St
From: Tom Zollo

In Y2, the aim is to use this working group as a launchpad for a larger ARNI continual learning project (which we hope to spawn multiple subprojects and papers). We hope for this group to tackle issues that are relevant to both modern practitioners and the ARNI mission of connecting artificial and natural intelligence.

As a potential topic for this project, we think we might consider the problem of long and short term memory in LLMs. There has been recent interest from industry labs, e.g. Google (paper link) and Meta (paper link), in fitting an LLM with a long-term neural memory module to complement the short-term memory given by the context window. Several threads relevant to ARNI could extend from this research direction. For instance, we might consider cognitively-inspired benchmarks for LLM memory systems for lifelong learning, e.g., based on human-like tasks that might be difficult for autoregressive models. Also, we could explore methodological work in LLM memory mechanisms based on our understanding of natural intelligence. We are particularly interested in learning about relevant studies in neuroscience and cognitive science that could help constrain and inspire the methodological approaches. Beyond these, one could imagine many other related directions of interest to ARNI.

Zoom: https://columbiauniversity.zoom.us/j/99160043324?pwd=[REDACTED]

CTN Monda Lab: Liam Paninski
2025-02-10 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title and Abstract: TBD

CTN: Eva Naumann
2025-02-07 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States

CTN: Hidenori Tanaka
2025-02-05 11:30 - 13:00 | Zuckerman Institute- Kavli Auditorium 9th Fl, 3227 Broadway, NY
Hidenori Tanaka
Title and Abstract: TBD

ARNI Biological Learning Working Group
2025-01-29 14:00 - 15:00 | NY
Title: Brain-like learning with exponentiated gradients and Learning to live with Dale’s principle: ANNs with separate excitatory and inhibitory units
Meeting Summary: Our focus will be on answering the following question, which may be a focus for the next few meetings: To what degree are different learning algorithms entangled with a particular neural architecture? Can we find neural architectures that interact better with certain learning algorithms?
Meeting link: http://meet.google.com/stu-ozga-syi
More about the Biological Learning Working Group: The biological learning WG is interested in better understanding how biological neural networks perform credit assignment (i.e. how they determine which synapses should change to get better at a task). The success of credit assignment algorithms in AI, such as backpropagation-of-error, have revealed that the traditional Hebbian plasticity rules used in computational neuroscience were not nearly as powerful as is possible for learning in distributed networks. This has spurred a new field of research in neuroscience that seeks to uncover the mechanisms used for credit assignment in the brain, as many researchers expect they are quite powerful, similar to those used in AI. The goal of this WG is to explore this new field of research and consider new potential directions for explaining credit assignment in the brain. Additionally, this could inspire new mechanisms for credit assignment in AI that are more efficient from an energy perspective than backpropagation-of-error.

CTN: Monday Lab Kim Stachenfeld
2025-01-27 11:30 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Discovering Symbolic Cognitive Models from Human and Animal Behavior with CogFunSearch

Abstract: A key goal of cognitive science is to discover mathematical models that describe how the brain implements cognitive processes. These models often take the form of short computer programs, and constructing them typically requires a great deal of human effort and ingenuity. In this meeting, I’ll share current results from our recent efforts to apply FunSearch [Romera-Paredes et al 2024] to the problem of discovering programs that reproduce the behavior of humans or other animals performing simple tasks. FunSearch is a recently developed tool that uses Large Language Models (LLMs) in an evolutionary algorithm to discover programs optimized for some objective. For our investigation, we consider datasets from three species performing a classic reward-learning task that has been the focus of a great deal of modeling effort. Our approach reliably discovers models that outperform state-of-the-art cognitive models for each dataset. The discovered programs can readily be interpreted as computational cognitive models, instantiating human-interpretable hypotheses about the learning and decision-making algorithms used by the brain. This is work that we’re wrapping up at DeepMind for ICML and prepping for journal submission, so it’s a great time to get questions, comments, feedback, criticisms, and suggestions for new opportunities! We are also hoping to apply the approach to new tasks/datasets soon, and I’d love to get ideas.

CTN: Jonathan Pillow
2025-01-24 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems

Abstract:
Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types, which limits their ability to capture the functional roles of distinct cell classes or predict the effects of cell-specific perturbations. To overcome these limitations, we introduce the “cell-type dynamical systems” (CTDS) model, which extends latent linear dynamical systems to contain distinct latent variables for each cell class, with appropriate sign constraints on the interactions between them. In this talk, I will describe the CTDS model and show that fitting in the noiseless case can be reduced to non-negative matrix factorization. I will then show an application of a multi-region model CTDS to simultaneous recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. Remarkably, the model — fit only to unperturbed neural activity — predicts the time-dependent effects of different optogenetic perturbations on behavior, specifically in FOF, ADS, and FOF-to-ADS axon terminals. I will close by discussing the future directions and other applications for biologically-constrained dynamical models of neural activity and behavior.

CTN: Adam Cohen
2025-01-17 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Mapping bioelectrical signals, from dendrites to circuits

Abstract:
Neuronal dendrites are excitable, but what are these excitations for? Are dendritic excitations involved in integration? Or in mediating back-propagation? What are their footprints, and what patterns of spiking and synaptic inputs can activate them? We mapped bioelectrical signals throughout dendritic arbors of pyramidal cells in behaving mice and developed simple models relating dendritic biophysics to computation. I will also describe all-optical circuit mapping in behaving mice, and experiments recording voltage simultaneously from hundreds of genetically defined neurons during behavior. These new data sets open possibilities for modeling how cellular intrinsic properties and local circuits process information.

CTN: Mehdi Azabou, ARNI Postdoctorate Research Scientist
2025-01-13 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Building foundation models for neuroscience

Abstract: Current methodologies for recording brain activity often provide narrow views of the brain’s function. This fragmentation of datasets has hampered the development of robust and comprehensive computational models that generalize across diverse conditions, tasks, and individuals. Our work is motivated by the need for a large-scale foundation model in neuroscience–one that can go beyond the limitations of single-dataset approaches and offer a fuller, more comprehensive picture of brain function. We propose a novel, scalable and unified approach for training on diverse neural datasets. We test our model across two large collections of data: 1. on nonhuman primates performing diverse motor tasks, spanning over 158 different sessions from over 27,373 neural units, and 2. the entirety of the Allen Institute’s Brain Observatory dataset, containing responses from over 100,000 neurons in 6 areas of the brains of mice, observed with two-photon calcium imaging, recorded while the mice observed different types of visual stimuli.

CTN: Mazviita Chirimuuta
2025-01-10 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Neuromorphic Computing and the Significance of Medium Dependence

Abstract:
The increasingly prohibitive cost of energy demanded by large artificial neural networks (ANNs) is giving new impetus to research and development on neuromorphic computing. Importantly, there is an open question over how brain-like the hardware will have to be in order for an artificial intelligence to match the brain in its combination of robustness, adaptability, and energy efficiency. If biological cognition is heavily dependent on the specific properties of the material that instantiates it (i.e. living cells), then neuromorphic computing will have to merge with synthetic biology in order to achieve its ultimate goal of brain-like performance. If it is not, neuromorphic computing holds out the promise of some gains in efficiency but there is no pressure for hardware to become increasingly neuro-mimetic in order to match the functionality of the nervous system. In this talk I introduce the concept of practical medium dependence/independence in order to explore the likelihood of these two scenarios. I present the argument that practically medium independent approaches to information processing, such as digital computing, are inherently less efficient than ones dependent on the specifics of implementing media, and for that reason will not have evolved. This result has implications for how we rate the near-term possibility of human-like artificial general intelligence, and offers a new way to understand how cognition is rooted, more generally, in biological processes.

CTN Lab: Ashok Litwin-Kumar
2024-12-09 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Searching for symmetries in connectome data

Abstract: I will talk about work with Haozhe Shan on identifying structure in connectome data that suggests a cell type encodes one or a handful of variables, like heading direction or retinotopy. We are framing the problem as learning a graph embedding, but I will also mention other things we have considered which, at least for me, were educational. The project is at an early stage, so we would welcome suggestions and ideas.

Continual Learning Working Group: Lea Duncker
2024-12-06 14:00 - 15:00 | CEPSR 620, Schapiro 530 W. 120th St
Title: Task-dependent low-dimensional population dynamics for robustness and learning
Abstract: Biological systems face dynamic environments that require flexibly deploying learned skills and continual learning of new tasks. It is not well understood how these systems balance the tension between flexibility for learning and robustness for memory of previous behaviors. Neural activity underlying single, highly controlled experimental tasks has repeatedly been observed to exhibit low-dimensional structure. However, it is unclear how this organization arises and is maintained throughout learning, and how it might differ when networks are exposed to multiple tasks. In this talk, I will present work on a continual learning rule designed to minimize interference between sequentially learned tasks in recurrent networks. The learning rule preserves network dynamics within activity-defined low-dimensional subspaces used for previously learned tasks. It encourages recurrent dynamics associated with interfering tasks to explore orthogonal subspaces. Employing a set of tasks used in neuroscience, I will show that this approach can successfully eliminate catastrophic interference, while allowing for reuse of similar low-dimensional dynamics across similar tasks. This possibility for shared computation allows for faster learning during sequential training. Finally, I will highlight limitations of this approach in fully exploiting task-similarity for optimal re-use of previously learned solutions, and outline new work we are starting in my group now to address this.
Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

CTN Seminar: Andrew Leifer
2024-12-06 11:00 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: TBD
Abstract: TBD

Lecture in AI: Danqi Chen
2024-12-06 11:00 - 12:00 | Davis Auditorium, 530 W 120th St, New York, NY 10027, New York, NY, 10027
Title: Training Language Models in Academic: Research Questions and Opportunities
Abstract: Large language models have emerged as transformative tools in artificial intelligence, demonstrating unprecedented capabilities in understanding and generating human language. While these models have achieved remarkable performance across a wide range of benchmarks and enabled groundbreaking applications, their development has been predominantly concentrated within large technology companies due to substantial computational and proprietary data requirements. In this talk, I will present a vision for how academic research can play a critical role in advancing the open language model ecosystem, particularly by developing smaller yet highly capable models and advancing our fundamental understanding of training practices. Drawing from our research group’s recent projects, I will examine key research questions and challenges in both pre-training and post-training stages. Our work spans developing small language models (Sheared LLaMA; 1-3B parameters), the state-of-the-art <10B model on Chatbot Arena (gemma-2-SimPO), and long-context models supporting up to 512K tokens (ProLong). These examples illustrate how academic research can push the boundaries of model efficiency, capability, and scalability. I will conclude by exploring future directions and highlighting opportunities to shape the development of more accessible and powerful language models.

CTN: Seminar Speaker Alessandro Ingrosso
2024-11-20 10:30 - 11:30 | Zuckerman Institute – L3-079, 3227 Broadway, New York, NY, 10027, United States
Title:
Statistical mechanics of transfer learning in the proportional limit

Abstract:
Transfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related (source) task, and it crucially depends on the ability of a network to learn useful features. I will present a recent work that leverages analytical progress in the proportional regime of deep learning theory (i.e. the limit where the size of the training set P and the size of the hidden layers N are taken to infinity keeping their ratio P/N finite) to develop a novel statistical mechanics formalism for TL in Bayesian neural networks. I’ll show how such single-instance Franz-Parisi formalism can yield an effective theory for TL in one-hidden-layer fully-connected neural networks. Unlike the (lazy-training) infinite-width limit, where TL is ineffective, in the proportional limit TL occurs due to a renormalized source-target kernel that quantifies their relatedness and determines whether TL is beneficial for generalization.

CTN: Catherine Hartley
2024-11-15 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States

Continual Learning Working Group: Nikita Rajaneesh
2024-11-13 12:00 - 13:00 | CEPSR 6LW4, Computer Science Department 500 West 120 Street
Title: Wandering Within a World
A discussion on Wandering Within a World: Online Contextualized Few-Shot Learning, this 2021 paper by our very own Rich Zemel leverages contextual information in a continually changing environment to improve model performance in realistic settings.
Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

CTN: Tanya Sharpee
2024-11-08 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Seminar Time: 11:30am
Date: 11/8/2024
Location: JLG, L5-084
Host: Krishan Kumar

Title: Building mechanistic models of neural computations with simulation-based machine learnin

Continual Learning Working Group: Brainstorming
2024-11-06 12:30 - 13:00 | CEPSR 620, Schapiro 530 W. 120th St
Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=[REDACTED]

ARNI Seminar Series Kick Off: Speaker Jim DiCarlo
2024-11-01 15:00 - 17:00 | Zuckerman Institute – L7-119, 3227 Broadway, New York, NY, 10027, United States
Title: Do contemporary, machine-executable models (aka digital twins) of the primate ventral visual system unlock the ability to non-invasively, beneficially modulate high level brain states?
Abstract:
In this talk, I will first briefly review the story of how neuroscience, cognitive science and computer science (“AI”) converged to create specific, image-computable, deep neural network models intended to appropriately abstract, emulate and explain the mechanisms of primate core visual object identification and categorization behaviors. Based on a large body of primate neurophysiological and behavioral data, some of these network models are now the most accurate emulators of the primate ventral visual stream — they well-approximate both its internal neural mechanisms and how those mechanisms support the ability of humans and other primates to rapidly and accurately infer object identity, position, pose, etc. from the set of pixels (image) received during typical natural viewing.
Because these leading neuroscientific emulator models — aka “digital twins” — are fully observable and machine-executable, they offer predictive and potential application power that our field’s prior conceptual models did not. I will describe two recent examples from our team. First, the current leading digital twins predict that the brain’s high level visual neurons (inferior temporal cortex, IT) should be highly susceptible to “adversarial attacks” in which an agent (the adversary) aims to strongly disrupt the normal neural response (here, neural firing rate) to any given natural image via small magnitude, targeted changes to that image. We verified this surprising prediction in monkey IT neurons. Second, we show how we can turn this result around and extend it: instead of making adversarial “attacks”, we propose using digital twin models to support non-invasive, beneficial brain modulation. Specifically, we show that we can use a digital twin to design spatial patterns of light energy that, when applied to the organism’s retina in the context of ongoing natural visual processing, results in precise modulation (i.e. rate bias) of the pattern of a population of IT neurons (where any intended modulation pattern is chosen ahead of time by the scientist). Because the IT visual neural populations are known to directly modulate downstream neural circuits involved in mood and anxiety, we speculate that this could provide a new, non-invasive application avenue of potential future human clinical benefit.
Zoom Link: https://columbiauniversity.zoom.us/j/97757217278?pwd=[REDACTED]

CTN: Jacob Macke
2024-11-01 11:30 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Building mechanistic models of neural computations with simulation-based machine learning

Abstract: Experimental techniques now make it possible to measure the structure and function of neural circuits at an unprecedented scale and resolution. How can we leverage this wealth of data to understand how neural circuits perform computations underlying behaviour? A mechanistic understanding will require models that align with experimental measurements and biophysical mechanisms, while also being capable of performing behaviorally relevant computations. Building such models has remained a central challenge.
I will present our work on addressing his challenge: We have developed machine learning methods and differentiable simulators that make it possible to algorithmically identify models that link biophysical mechanisms, neural data, and behaviour. I will show how these approaches—in combination with modern connectomic measurements—make it possible to build large-scale mechanistic models of the fruit fly visual system, and how such a model can make experimentally testable predictions for each neuron in the system.

CTN: Naureen Ghani
2024-10-31 11:00 - 13:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Mice wiggle a wheel to boost the salience of low visual contrast stimuli

Abstract: From the Welsh tidy mouse to the New York City pizza rat, movement belies rodent intelligence. We show that head-fixed mice develop an active sensing strategy while performing a visual perceptual decision-making task (The International Brain Laboratory, 2021). Akin to humans shaking a computer mouse to find the cursor on a screen, we demonstrate that mice wiggle the wheel that controls the movement of a visual stimulus to boost low contrast salience. Moreover, mice wiggle the wheel at a temporal frequency (11.9 ± 2.9 Hz) optimal for their visual systems (Umino et al, 2018). With the “old method of watching and wondering about behavior,” we reveal that mice exploit that it is easier to see something moving than something stationary by wiggling (Tinbergen, 1973).
