Flatiron Institute
Flatiron Institute
Center for Computational Neuroscience
POSITION SUMMARY Applications are invited for Flatiron Research Fellowships (FRF) at the Center for Computational Neuroscience. The CCN FRF program offers the opportunity for postdoctoral research in areas that have strong synergy with one or more of the existing research groups at CCN or other centers at the Flatiron Institute. CCN FRF’s will be assigned a primary mentor from a CCN research group or project, though affiliations and collaborations with other research groups within CCN and throughout the Flatiron Institute are encouraged. In addition to carrying out an independent research program, Flatiron Research Fellows are expected to: disseminate their results through scientific presentations, publications, and software release, collaborate with other members of the CCN or Flatiron Institute, and participate in the scientific life of the CCN and Flatiron Institute by attending seminars, colloquia, and group meetings. Flatiron Research Fellows may have the opportunity to organize workshops and to mentor graduate and undergraduate students. The mission of CCN is to develop theories, models, and computational methods that deepen our knowledge of brain function — both in health and in disease. CCN takes a “systems" neuroscience approach, building models that are motivated by fundamental principles, that are constrained by properties of neural circuits and responses, and that provide insights into perception, cognition and behavior. This cross-disciplinary approach not only leads to the design of new model-driven scientific experiments, but also encapsulates current functional descriptions of the brain that can spur the development of new engineered computational systems, especially in the realm of machine learning. CCN currently has research groups in computational vision, neural circuits and algorithms, neuroAI and geometry, and statistical analysis of neural data; interested candidates should review the CCN public website for specific information on CCN’s research areas. Review of applications for positions starting between July and October 2022 will begin in mid-January 2022. Application Materials Cover letter (optional); Curriculum Vitae with bibliography; Research statement of no more than three pages describing past work and proposed research program. Applicants are encouraged to discuss the broad impact of the past and proposed research on computational neuroscience. Applicants should also indicate the primary CCN group(s) with which they’d seek to conduct research, and any desired affiliation with other Flatiron Centers. Three (3) letters of recommendation submitted confidentially by direct email to ccnjobs@simonsfoundation.org Selection Criteria: Applicants must have a PhD in a related field or expect to receive their PhD before the start of the appointment. Applications will be evaluated based on 1) past research accomplishments 2) proposed research program 3) synergy of applicant’s expertise and research proposal topic with existing CCN staff and research programs. Education PhD in computational neuroscience or a relevant technical field such as electrical engineering, machine learning, statistics, physics, or applied math. Related Skills Flexible multi-disciplinary mindset; Strong interest and experience in the scientific study of the brain; Demonstrated abilities in analysis, software and algorithm development, modeling and/or scientific simulation; Ability to do original and outstanding research in neuroscience; Ability to work well independently as well as in a collaborative team environment. FRF positions are two-year appointments and are generally renewed for a third year, contingent on performance. FRF receive a research budget and have access to the Flatiron Institute’s powerful scientific computing resources. FRF may be eligible for subsidized housing within walking distance of the CCN. THE SIMONS FOUNDATION'S DIVERSITY COMMITMENT Many of the greatest ideas and discoveries come from a diverse mix of minds, backgrounds and experiences, and we are committed to cultivating an inclusive work environment. The Simons Foundation actively seeks a diverse applicant pool and encourages candidates of all backgrounds to apply. We provide equal opportunities to all employees and applicants for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, genetic disposition, neurodiversity, disability, veteran status or any other protected category under federal, state and local law.
Reimagining the neuron as a controller: A novel model for Neuroscience and AI
We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers. This innovative approach enables us to coherently explain various experimental findings that previously seemed unrelated. Our research has profound implications, potentially revolutionizing the modeling of neuronal circuits and paving the way for the creation of alternative, biologically inspired artificial neural networks.
Membrane mechanics meet minimal manifolds
Changes in the geometry and topology of self-assembled membranes underlie diverse processes across cellular biology and engineering. Similar to lipid bilayers, monolayer colloidal membranes studied by the Sharma (IISc Bangalore) and Dogic (UCSB) Labs have in-plane fluid-like dynamics and out-of-plane bending elasticity, but their open edges and micron length scale provide a tractable system to study the equilibrium energetics and dynamic pathways of membrane assembly and reconfiguration. First, we discuss how doping colloidal membranes with short miscible rods transforms disk-shaped membranes into saddle-shaped minimal surfaces with complex edge structures. Theoretical modeling demonstrates that their formation is driven by increasing positive Gaussian modulus, which in turn is controlled by the fraction of short rods. Further coalescence of saddle-shaped surfaces leads to exotic topologically distinct structures, including shapes similar to catenoids, tri-noids, four-noids, and higher order structures. We then mathematically explore the mechanics of these catenoid-like structures subject to an external axial force and elucidate their intimate connection to two problems whose solutions date back to Euler: the shape of an area-minimizing soap film and the buckling of a slender rod under compression. A perturbation theory argument directly relates the tensions of membranes to the stability properties of minimal surfaces. We also investigate the effects of including a Gaussian curvature modulus, which, for small enough membranes, causes the axial force to diverge as the ring separation approaches its maximal value.
Structure, Function, and Learning in Distributed Neuronal Networks
A central goal in neuroscience is to understand how orchestrated computations in the brain arise from the properties of single neurons and networks of such neurons. Answering this question requires theoretical advances that shine light into the ‘black box’ of neuronal networks. In this talk, I will demonstrate theoretical approaches that help describe how cognitive and behavioral task implementations emerge from structure in neural populations and from biologically plausible learning rules. First, I will introduce an analytic theory that connects geometric structures that arise from neural responses (i.e., neural manifolds) to the neural population’s efficiency in implementing a task. In particular, this theory describes how easy or hard it is to discriminate between object categories based on the underlying neural manifolds’ structural properties. Next, I will describe how such methods can, in fact, open the ‘black box’ of neuronal networks, by showing how we can understand a) the role of network motifs in task implementation in neural networks and b) the role of neural noise in adversarial robustness in vision and audition. Finally, I will discuss my recent efforts to develop biologically plausible learning rules for neuronal networks, inspired by recent experimental findings in synaptic plasticity. By extending our mathematical toolkit for analyzing representations and learning rules underlying complex neuronal networks, I hope to contribute toward the long-term challenge of understanding the neuronal basis of behaviors.