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DTSTART;TZID=America/New_York:20260427T090000
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UID:10003757-1777280400-1777654800@cmsa.fas.harvard.edu
SUMMARY:Mathematics and Biology II: Mathematics and Science of Behavior
DESCRIPTION:Mathematics and Biology II: Mathematics and Science of Behavior \nDates: April 27 – May 1\, 2026 \nLocation: Harvard CMSA\, Room G10\, 20 Garden Street\, Cambridge MA \n\n\nThis meeting will explore the emerging mathematics and science of embodied cognition—the idea that behavior arises not solely from the brain but through the dynamic interaction of brain\, body\, and environment. Understanding how animals sense\, move\, decide\, and coordinate\, from individual sensorimotor loops to collective dynamics\, demands mathematical frameworks that integrate geometry\, dynamics\, stochastic processes\, control theory\, and multiscale physics. The meeting will bring together experimentalists studying behavior across species with theorists and engineers building mathematical models and bio-inspired machines\, to identify shared principles of adaptive behavior. \n\n\nCo-organizers: L. Mahadevan (Harvard)\, Francesco Mori (Harvard CMSA)\, Venkatesh Murthy (Harvard) \nRegister to attend in-person \n  \nSpeakers \n\nPulkit Agrawal\, MIT\nKristin Branson\, HHMI\nAntonio C. Costa\, Sorbonne University/Paris Brain Institute\nNoah Cowan\, Johns Hopkins University\nRobert Datta\, Harvard Medical School\nBen de Bivort\, Harvard University\nOfer Feinerman\, Weizmann Institute of Science\nDeborah Gordon\, Stanford University\nAlbert Kao\, UMass Boston\nAnn Kennedy\, Scripps Research Institute\nHungtang Ko\, Tufts University\nGeorge Lauder\, Harvard University\nBence Ölveczky\, Harvard University\nKirstin Petersen\, Cornell University\nPavan Ramdya\, EPFL\nElizabeth Tibbetts\, University of Michigan\nRobert Wood\, Harvard University\n\n  \n\nSchedule  \nMonday\, Apr. 27\, 2026 \n9:00–9:30am: Breakfast \n9:30–10:15 am: Deborah Gordon\, Stanford University\nTitle: The dynamics of collective behavior in changing environments\nAbstract: Collective behavior operates without central control\, using interactions among participants adjust to changing conditions. There is enormous diversity in the dynamics of collective behavior\, including in the rate of response to conditions\, in feedback regimes that set whether interactions stimulate or inhibit activity\, and the extent of centralization or modularity of information flow.  An ecological perspective suggests how this diversity of collective behavior reflects the dynamics of the environment\, including its stability\, the ratio of resources spent to resources gained\, and the distribution of resources in time and space.\nAs examples\, I will discuss field studies and modelling of the regulation of foraging behavior in two species of ants\, Harvester ant colonies in the desert regulate foraging to manage high costs\, in water loss\, to obtain scattered and stable resources. They use a centralized system\, with the default to remain inactive unless stimulated\, that is slow to adjust foraging activity. In contrast\, the turtle ant colonies form trail networks in the canopy of the tropical forest\, in unstable conditions where activity costs are low\, to find and collect ephemeral and patchy resources. They use a highly modular system\, with the default to sustain activity unless inhibited\, that can rapidly adjust trail networks to changing resources and conditions. \n10:15–10:30 am: Discussion \n10:30–11:00 am: Tea Break \n11:00–11:45 am: Hungtang Ko\, Tufts University \n11:45 am–12:00 pm: Discussion \n12:00–1:00 pm: Catered Lunch \n1:00–1:45 pm: Albert Kao\, UMass Boston \n1:45–2:00 pm: Discussion \n2:00–2:45 pm: Ann Kennedy\, Scripps Research Institute\nTitle: Neural mechanisms that gate the expression of motivated behaviors \n2:45–3:00 pm: Discussion \n3:00–4:30 pm: Break \n4:30–5:30 pm: CMSA Colloquium: Ofer Feinerman\, Weizmann Institute of Science \n  \nTuesday\, April 28\, 2026 \n9:00 –9:30 am: Breakfast \n9:30–10:15 am: Ben de Bivort\, Harvard University\nTitle: Bayesian Inference on biophysical models of connectomes \nAbstract: Recent progress in connectomics has opened new frontiers for understanding the underlying principles of neural circuits. By leveraging high-resolution maps of synaptic connections\, computational models can simulate neural dynamics with unprecedented detail. However\, it remains challenging to parsimoniously integrate circuit activity data with connectomic information to make biological in- sights. We propose a Bayesian framework as a principled method for bringing to bear existing data\, enabling uncertainty quantification for inferring parameters of interest\, as well as for predicted circuit outputs. To demonstrate this approach\, we implement a simple spiking neuron model using leaky- integrate-and-fire dynamics in the Drosophila olfactory circuit\, incorporating available firing rate data. We evaluate how models with varying levels of biological detail fit experimental data and examine how training on different subsets of data influences model predictions. \n10:15–10:30 am: Discussion \n10:30–11:00 am: Tea Break \n11:00–11:30 am: Trainee talk \n11:30 am–12:00 pm: Trainee talk \n12:00–1:00 pm: Catered Lunch \n1:00–1:45 pm: Noah Cowan\, Johns Hopkins University\nTitle: Toward a Control Theory for Active Sensing\nAbstract: Active sensing is often defined as “movement for the purpose of sensing.” Here\, I take a different perspective—that active sensing in biological systems is not a distinct class of behaviors\, but rather a set of movement phenomena that arise from a control objective. Biological sensors adapt to persistent stimuli\, acting like high-pass filters that tend to block “DC.” Such “change-detecting” sensors can support efficient coding with a high dynamic range\, and in engineering\, bio-inspired event cameras are similar: they transmit information only when a pixel changes and\, as such\, are extremely fast and make efficient use of bandwidth for the right applications. However\, such “AC” sensors pose technical challenges for control. Specifically\, event-like biological sensors can cause a nonlinear system (1) to lose local linear observability\, and (2) to become impossible to stabilize about an equilibrium point (Biswas\, Sontag\, Cowan\, Eur J Control\, 2025). Active sensing behaviors must emerge for stable control\, even in the somewhat paradoxical setting where the task-level goal is to remain stationary. Here\, I will discuss my lab’s progress in analyzing how animals use active sensing behaviors to format sensory information\, enhancing observability and control. I will also present our efforts to formalize controller synthesis with event-like sensors. \n1:45–2:00 pm: Discussion \n2:00–2:45 pm: Robert Datta\, Harvard Medical School \n2:45–3:00 pm: Discussion \n3:00–3:30 pm: Break \n3:30–4:15 pm: TBD \n4:15–4:30: Discussion \n  \nWednesday\, April 29\, 2026 \n9:00 –9:30 am: Breakfast \n9:30–10:15 am: Kristin Branson\, HHMI \n10:15–10:30 am: Discussion \n10:30–11:00 am: Tea Break \n11:00–11:30 am: Trainee talk \n11:30 am–12:00 pm: Trainee talk \n12:00–1:00 pm: Catered Lunch \n1:00–1:45 pm: Bence Ölveczky\, Harvard University \n1:45–2:00 pm: Discussion \n2:00–2:45 pm: Pavan Ramdya\, EPFL\nTitle: Towards fly-inspired legged robots\nAbstract: I will discuss our efforts to build biologically-inspired legged robots using behavioral measurements\, neuromechanical simulations\, and anatomical studies of Drosophila melanogaster. \n2:45–3:00 pm: Discussion \n3:00–3:30 pm: Break \n3:30–4:15 pm: TBD \n4:15–4:30: Discussion \n  \nThursday\, April 30\, 2026 \n9:00 –9:30 am: Breakfast \n9:30–10:15 am: Pulkit Agrawal\, MIT \n10:15–10:30 am: Discussion \n10:30–11:00 am: Tea Break \n11:00–11:30 am: Trainee talk \n11:30 am–12:00 pm: Trainee talk \n12:00–1:00 pm: Catered Lunch \n1:00–1:45 pm: Elizabeth Tibbetts\, University of Michigan\nTitle: What paper wasps can teach us about the evolution of animal minds\nAbstract: Why do animals differ in their cognitive abilities? Some animals fail at apparently simple tasks\, while others have a remarkable capacity to collect\, retain\, and use information from the environment to guide their behavior. Although paper wasps brains are smaller than a grain of rice\, Tibbetts will show that wasps can perform seemingly complex behaviors like individual face recognition\, transitive inference\, social eavesdropping\, and concept learning. She will also describe experiments that take advantage of natural variation in behavior within and among wasp species to test how social interactions shape the development and evolution of cognitive abilities. \n1:45–2:00 pm: Discussion \n2:00–2:45 pm: Robert Wood\, Harvard University\nTitle: The Mechanical Side of Artificial Intelligence\nAbstract: Artificial Intelligence research typically focuses on perception\, learning\, and control methods to enable autonomous agents\, including robots\, to make and act on decisions in real-world scenarios. However\, even the most capable AI without a well-designed physical structure is of minimal use for canonical robotics tasks. Our research is focused on the design\, mechanics\, materials\, and manufacturing of novel robot platforms that make perception\, control\, or action easier or more robust for natural\, unstructured\, and often unpredictable environments. Key principles in this pursuit include bioinspired designs\, smart materials for novel sensors and actuators\, and the development of multi-scale\, multi-material manufacturing methods. This talk will illustrate this philosophy by highlighting the creation of three classes of robots with unique hardware challenges: bioinspired microrobots\, soft-bodied robots for manipulation\, and robots for interacting with delicate marine life. \n2:45–3:00 pm: Discussion \n3:00–3:30 pm: Break \n3:30–4:15 pm: TBD \n4:15–4:30 pm: Discussion \n  \nFriday\, May 1\, 2026 \n9:00–9:30 am: Breakfast \n9:30–10:15 am: George Lauder\, Harvard University \n10:15–10:30 am: Discussion \n10:30–11:00 am: Tea Break \n11:00–11:45 am: Antonio C. Costa\, Sorbonne University/Paris Brain Institute \n11:45 am–12:00 pm: Discussion \n12:00–1:00 pm: Catered Lunch \n1:00–1:45 pm:  \n1:45–2:00 pm: Discussion \n2:00–2:45 pm: Kirstin Petersen\, Cornell University\nTitle: Harnessing Embodied Intelligence in Robot Collectives\nAbstract: In the Collective Embodied Intelligence Lab\, we study embodied intelligence as a complement to artificial intelligence in robot collectives. Our work spans scales and mechanisms\, from behaviors encoded in robot morphology to collective behaviors that emerge through physical coupling and stigmergic coordination when many robots operate in shared environments. Many of these principles are inspired by biological systems\, including our studies of construction and aggregation in honey bees and subterranean and mound-building termites. In this talk\, I will present examples from our lab\, including soft robots that exploit viscous fluid–structure interactions for articulated control\, microrobots that leverage magnetic and hydrodynamic interactions to produce a range of collective behaviors\, and entangled robotic matter that achieves cohesive motion through transient physical entanglement. Together\, these systems illustrate how intelligence can be distributed across morphology\, interactions\, and shared substrates\, enabling scalable and robust robot collectives. \n2:45–3:00 pm: Discussion \n3:00–3:30 pm: Break \n3:30–4:15 pm: TBD \n4:15–4:30 pm: Discussion
URL:https://cmsa.fas.harvard.edu/event/bioshape2_2026/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Programs
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/Biology2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260501T120000
DTEND;TZID=America/New_York:20260501T130000
DTSTAMP:20260404T210747
CREATED:20260212T190507Z
LAST-MODIFIED:20260212T190507Z
UID:10003908-1777636800-1777640400@cmsa.fas.harvard.edu
SUMMARY:Member Seminar
DESCRIPTION:Member Seminar \nSpeaker: tba
URL:https://cmsa.fas.harvard.edu/event/member-seminar-5126/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
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