Many plasticity rules rely on adjusting the strength of synapses between pairs of cells based on their coincident activity. We uncovered a new mechanism for coincidence detection in the Drosophila head direction network. To maintain an accurate sense of direction, head direction neurons that signal orientation during navigation must learn to anchor to relevant external sensory cues in novel environments. Yet the synaptic mechanism for this form of unsupervised learning is unknown in any organism. In Drosophila, GABAergic visual inputs converge onto head direction neurons, and these inhibitory synapses change strength with experience to learn the relationship between visual landmarks and head direction. However, how coincident pre- and postsynaptic activity is detected across this inhibitory synapse is not understood. We discovered that neurons which release the monoamine octopamine close a feedback loop that conveys postsynaptic head direction activity onto presynaptic terminals of visual inputs. This octopamine pathway is required for anchoring the head direction network to visual cues. Furthermore, pairing structured activation of octopamine neurons with a visual cue is sufficient to drive rapid plasticity, even without postsynaptic head direction cell activity. Previous work has extensively characterized coincidence detection mechanisms at excitatory synapses; our work defines a novel mechanism for coincidence detection at an inhibitory synapse, in which postsynaptic activity is relayed via a neuromodulatory neuron onto presynaptic terminals.
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You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/theoretical-realisation-of-quantum-phenomena-in-computational-materials-discovery-1485.
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\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
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\r\nDescription
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\r\nQuantum phenomena in materials underpin a range of emerging technologies, including spin-based quantum technologies, efficient energy transport materials and ultra-narrow bandwidth lasers.1,2,3 Emergent behaviour such as quantum magnetism, superconductivity and superradiance4 arise from the complex interplay between electronic and structural properties; electronic features including strong electron correlation, spin-orbit coupling and reduced dimensionality can lead to phenomena such as unconventional superconductivity and room-temperature spin coherences, whilst structural factors such as crystal symmetry, doping concentrations and Moiré twist patterns are pivotal in shaping these quantum characteristics.5,6 Computational quantum materials discovery requires both highly advanced theoretical models of the electronic structure and high-throughput approaches for identifying stable crystal structures and predicting their properties.3,7
\r\nStrongly correlated electrons, ubiquitous in quantum materials, challenge conventional density functional theory (DFT). Quantum embedding methods, such as Density Matrix Embedding Theory (DMET) and Quantum Defect Embedding Theory (QDET), are powerful tools for describing strongly correlated electronic states in materials. QDET solves an effective Hamiltonian for a strongly-correlated subset of DFT orbitals using full configuration interaction, parameterized via a Green's function approach.8 DMET, however, maps the solid-state problem onto a self-consistent quantum impurity coupled to a mean-field bath, with the impurity solved by high-level methods.9 The application of these advanced techniques is rapidly growing, from analysing superconducting cuprates to describing quantum spin defects in semiconductors.8,9
\r\nModel Hamiltonians, such as the multi-band Hubbard model, are increasingly used to describe the low-energy physics of quantum materials.10 While the constrained random phase approximation is the traditional choice for parametrising these models,11 the newly developed moment-conserved RPA may offer superior accuracy by conserving instantaneous two-point correlation functions.12,13 Powerful numerical techniques like Determinant Quantum Monte Carlo have recently been pioneered for solving the model Hamiltonian and predicting quantum phenomena such as pairing susceptibilities.14
\r\nSuch theoretical methods are also essential for computational discovery of spin defects in semiconductors, a promising platform for room-temperature qubits.3,15 Advanced theoretical treatments are essential to predict defect electronic, magnetic, and optical properties, incorporating effects like spin-orbit and spin-phonon coupling which determine spin coherence and optical manipulation characteristics. The current state-of-the-art combines DFT studies of semiconductor bulk properties with ab initio treatments of the defect; quantum embedding methods are emerging as a promising alternative.16,17
\r\nGiven the immense diversity of materials, high-throughput screening is a cornerstone of modern materials discovery. DFT, particularly with state-of-the-art approximations like r2SCAN+rVV10, remains the workhorse for reliably determining material structures; such calculations often offer critical insight into both a systems stability and electronic structure.7,18,19,20 Machine learning (ML) is transforming materials discovery by slashing the computational cost of such calculations, allowing a wider exploration of composition space.21,22
\r\nComputational quantum materials modelling is advancing rapidly, however reconciling methods treating strongly correlated electrons with computational workflows employed in modern materials discovery remains relatively unexploited. The synergy of advanced theory, high-performance computing and ML has the potential to drive breakthroughs in quantum materials discovery and accelerate development of emerging technologies, from novel qubit platforms to room-temperature superconductors.
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\r\nReferences
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\r\n[1] C. Scott, G. Booth, Phys. Rev. Lett., 132, 076401 (2024)
\r\n[2] X. Jiang, W. Wang, S. Tian, H. Wang, T. Lookman, Y. Su, npj. Comput. Mater., 11, 79 (2025)
\r\n[3] S. Giaremis, M. Righi, Tribology International, 204, 110438 (2025)
\r\n[4] Z. Zhu, J. Park, H. Sahasrabuddhe, A. Ganose, R. Chang, J. Lawson, A. Jain, npj. Comput. Mater., 10, 258 (2024)
\r\n[5] R. Nelson, C. Ertural, J. George, V. Deringer, G. Hautier, R. Dronskowski, J. Comput. Chem., 41, 1931-1940 (2020)
\r\n[6] M. Kothakonda, A. Kaplan, E. Isaacs, C. Bartel, J. Furness, J. Ning, C. Wolverton, J. Perdew, J. Sun, ACS Mater. Au, 3, 102-111 (2022)
\r\n[7] V. Briganti, A. Lunghi, npj. Comput. Mater., 11, 62 (2025)
\r\n[8] A. Kundu, F. Martinelli, G. Galli, J. Phys. Chem. Lett., 16, 1973-1979 (2025)
\r\n[9] A. Gali, A. Schleife, A. Heinrich, A. Laucht, B. Schuler, C. Chakraborty, C. Anderson, C. Déprez, J. McCallum, L. Bassett, M. Friesen, M. Flatté, P. Maurer, S. Coppersmith, T. Zhong, V. Begum-Hudde, Y. Ping, MRS Bulletin, 49, 256-276 (2024)
\r\n[10] P. Mai, B. Cohen-Stead, T. Maier, S. Johnston, Proc. Natl. Acad. Sci. U.S.A., 121, (2024)
\r\n[11] C. Pellegrini, C. Kukkonen, A. Sanna, Phys. Rev. B, 108, 064511 (2023)
\r\n[12] R. Goyal, S. Maharaj, P. Kumar, M. Chandrasekhar, J Mater. Sci: Mater Eng., 20, 4 (2025)
\r\n[13] Y. Chang, E. van Loon, B. Eskridge, B. Busemeyer, M. Morales, C. Dreyer, A. Millis, S. Zhang, T. Wehling, L. Wagner, M. Rösner, npj. Comput. Mater., 10, 129 (2024)
\r\n[14] H. Padma, J. Thomas, S. TenHuisen, W. He, Z. Guan, J. Li, B. Lee, Y. Wang, S. Lee, Z. Mao, H. Jang, V. Bisogni, J. Pelliciari, M. Dean, S. Johnston, M. Mitrano, Phys. Rev. X, 15, 021049 (2025)
\r\n[15] Z. Cui, J. Yang, J. Tölle, H. Ye, S. Yuan, H. Zhai, G. Park, R. Kim, X. Zhang, L. Lin, T. Berkelbach, G. Chan, Nat. Commun., 16, 1845 (2025)
\r\n[16] L. Otis, Y. Jin, V. Yu, S. Chen, L. Gagliardi, G. Galli, J. Phys. Chem. Lett., 16, 3092-3099 (2025)
\r\n[17] A. Ganose, H. Sahasrabuddhe, M. Asta, K. Beck, T. Biswas, A. Bonkowski, J. Bustamante, X. Chen, Y. Chiang, D. Chrzan, J. Clary, O. Cohen, C. Ertural, M. Gallant, J. George, S. Gerits, R. Goodall, R. Guha, G. Hautier, M. Horton, T. Inizan, A. Kaplan, R. Kingsbury, M. Kuner, B. Li, X. Linn, M. McDermott, R. Mohanakrishnan, A. Naik, J. Neaton, S. Parmar, K. Persson, G. Petretto, T. Purcell, F. Ricci, B. Rich, J. Riebesell, G. Rignanese, A. Rosen, M. Scheffler, J. Schmidt, J. Shen, A. Sobolev, R. Sundararaman, C. Tezak, V. Trinquet, J. Varley, D. Vigil-Fowler, D. Wang, D. Waroquiers, M. Wen, H. Yang, H. Zheng, J. Zheng, Z. Zhu, A. Jain, Digital Discovery, (2025)
\r\n[18] W. Ko, Z. Gai, A. Puretzky, L. Liang, T. Berlijn, J. Hachtel, K. Xiao, P. Ganesh, M. Yoon, A. Li, Advanced Materials, 35, (2022)
\r\n[19] L. Du, M. Molas, Z. Huang, G. Zhang, F. Wang, Z. Sun, Science, 379, (2023)
\r\n[20] C. Zhu, S. Boehme, L. Feld, A. Moskalenko, D. Dirin, R. Mahrt, T. Stöferle, M. Bodnarchuk, A. Efros, P. Sercel, M. Kovalenko, G. Rainò, Nature, 626, 535-541 (2024)
\r\n[21] Á. Gali, Nanophotonics, 12, 359-397 (2023)
\r\n[22] V. Harris, P. Andalib, Front. Mater., 11, (2024)
Eine App programmieren? Ein Computerspiel erfinden? Eine Animation gestalten?
\r\nWenn dich eines dieser Dinge interessiert, bist du hier am richtigen Ort! Tausche und teile deine Ideen, lerne zu programmieren und entdecke Informatikberufe.
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\r\nDie Coding Club for Girls Workshops sind für Mädchen im Alter von 11 bis 15 Jahren und die Teilnahme ist kostenlos. Ab der Teilnahme an vier Workshops pro Jahr wird eine Teilnahmebestätigung ausgehändigt.
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\r\nOrt: Kantonschule Musegg, Luzern
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\r\nDatum und Zeit:
\r\n13. Juni 2026 : 09:30 bis 12:00 oder 13:30 bis 16:00 Uhr // Schildkröte in Python
\r\n20. Juni 2026 : 09:30 bis 12:00 oder 13:30 bis 16:00 Uhr // Pixel Art
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\r\nDie Inhalte der Workshops sind untenstehend beschrieben.
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\r\nBedingungen\r\n