Integrated Calendar

Some animals, especially those living under water use mirrors rather than lenses to form images. Two general strategies exist in nature for forming images using mirrors, exemplified by the concave mirrored eyes of the scallop1 and the reflecting compound eyes of crustaceans2. Here we discuss these two remarkable visual systems and show how the whole hierarchical organization of the mirrors are exquisitely controlled for image-formation from the structure and morphology of the substituent reflecting crystals at the nanoscale to the overall shape of the mirrors at the millimeter scale. Based on our understanding of the optics and structure we can predict what the animal should be seeing. Whether the neural system can integrate all this information, has yet to be determined. From a materials science perspective, understanding how organisms exert such extraord! inary control over the formation and organization of organic crystals provides inspiration for the development of new organic crystalline materials with rationally designed morphologies and properties.
1B.A. Palmer*, G.J. Taylor, V. Brumfeld, D. Gur, M. Shemesh, N. Elad, A. Osherov, D. Oron, S. Weiner, L. Addadi, Science 2017, 358, 1172.
2B.A. Palmer*, A. Hirsch, V. Brumfeld, N. Elad, D. Oron, L. Kronik, L. Leiserowitz, S. Weiner, L. Addadi,* PNAS, 2018, 115, 2299.

Abstract:
I will discuss our work on visualizing “topology atlases” which act as a map of possible circuit designs for small 3-node regulatory motifs. These can help in understanding the relationship between a circuit's structure and its function, but how is this relationship affected if the circuit must perform multiple distinct functions within the same organism? In particular, to what extent do multi‐functional circuits contain modules which reflect the different functions? We computationally surveyed a range of bi‐functional circuits which show no simple structural modularity: They can switch between two qualitatively distinct functions, while both functions depend on all genes of the circuit. Our analysis revealed two distinct classes: hybrid circuits which overlay two simpler mono‐functional sub‐circuits within their circuitry, and emergent circuits, which do not. In this second class, the bi‐functionality emerges from more complex designs which are not fully decomposable into distinct modules and are consequently less intuitive to predict or understand. These non‐intuitive emergent circuits are just as robust as their hybrid counterparts, and we therefore suggest that the common bias toward studying modular systems may hinder our understanding of real biological circuits.

Relevant papers:
1. A spectrum of modularity in multi-functional gene circuits.
Jiménez A, Cotterell J, Munteanu A, Sharpe J. (2017)
Mol Syst Biol 13(4):925. doi: 10.15252/msb.20167347
http://msb.embopress.org/content/13/4/925
2. An atlas of gene regulatory networks reveals multiple three-gene mechanisms for interpreting morphogen gradients.
Cotterell J, Sharpe J. (2010)
Mol Syst Biol 6:425. doi: 10.1038/msb.2010.74
http://msb.embopress.org/content/6/1/425

Bio:
James Sharpe was originally captivated by computer programming, but upon learning about the digital nature of the genetic code, chose to study Biology for his undergraduate degree at Oxford University (1988-1991). He then did his PhD on the genetic control of embryo development at NIMR, London (1992-1997) and in parallel started writing computer simulations of multicellular development. During his post-doc in Edinburgh, he began modelling the dynamics of limb development, and also invented a new optical imaging technology called Optical Projection Tomography (OPT), which is dedicated to imaging specimens too large for microscopy - tissues and organs. In 2006 he moved to Barcelona, becoming a senior group leader at the Centre for Genomic Regulation, and focusing on a systems biology approach to modelling limb development – combining experimentation with computer modelling. In this way the group demonstrated that the signalling proteins which pattern the fingers during embryogenesis, act as a Turing reaction-diffusion system. In 2011 he became the coordinator of the Systems Biology Program, and in 2017 was recruited to EMBL as Head of the new Barcelona outstation on Tissue Biology and Disease Modelling.

In this talk, we will present the basic concept, operational principle and experimental perfor-mance of a novel computing machine based on the network of degenerate optical parametric oscillators. The developed machine has 2048 qubits with all-to-all connections and is now available as a cloud system via internet.
There are at least three quantum computing models proposed today: they are unitary quan-tum computation, adiabatic quantum computation and dissipative quantum computation. A gate model quantum computer implements the unitary quantum computation model and is expected to solve particular problems with hidden periodicity or specific structure [1,2], while a coherent Ising machine (CIM), implements the dissipative quantum computation model [3,4] and is expected to solve unstructured combinatorial optimization problems. We will dis-cuss the two types of CIMs, optical delay line coupling machine [5] and measurement feed-back coupling machine [6], as well as the performance comparison against modern digital computers and algorithms [7].



References
[1] D. Deutsch, Proc. of the Royal Society of London. Series A, Mathematical and Physical Sciences, 400, 97–117 (1985); D. Deutsch and R. Jozsa, Proc. Roy. Soc. (London) A 439, 553-558 (1992).
[2] P. W. Shor, Proc. of the 35th Annual Symposium on Foundations of Computer Science, IEEE Computer Socie-ty Press,124-134 (1994).
[3] W. H. Zurek, Rev. Mod. Phys. 75, 715-775 (2003).
[4] F. Verstraete, M. M. Wolf, and J. I. Cirac, Nature Phys. 5, 633-636 (2009).
[5] A. Marandi Z. Wang, K. Takata, R. L. Byer, and Y. Yamamoto, Nature Photonics 8, 937-942 (2014); T. Inagaki, K. Inaba, R. Hamerly, K. Inoue, Y. Yamamoto, and H. Takesue, Nature Photonics 10, 415-419 (2016).
[6] T. Inagaki, Y. Haribara, K. Igarashi, T. Sonobe, S. Tamate, T. Honjo, A. Marandi, P. L. McMahon, T. Umeki, K. Enbutsu, O. Tadanaga, H. Takenouchi, K. Aihara, K. Kawarabayashi, K. Inoue, S. Utsunomiya, and H. Takesue, Science 354, 603-606 (2016); P. L. McMahon, A. Marandi, Y. Haribara, R. Hamerly, C. Langrock, S. Tamate, T. Inagaki, H. Takesue, S. Utsunomiya, K. Aihara, R. L. Byer, M. M. Fejer, H. Mabuchi, and Y. Yamamoto, Science 354, 614-617 (2016).
[7] Y. Haribara, H. Ishikawa, S. Utsunomiya, K. Aihara, and Y. Yamamoto, Quantum Sci. Tech. 2, 044002 (2017).

The prospect of judiciously utilizing both optical gain and loss has been recently suggested as a means to control the flow of light. This proposition makes use of some newly developed concepts based on non-Hermiticity and parity-time (PT) symmetry-ideas first conceived within quantum field theories. By harnessing such notions, recent works indicate that novel synthetic structures and devices with counter-intuitive properties can be realized, potentially enabling new possibilities in the field of optics and integrated photonics. Non-Hermitian degeneracies, also known as exceptional points (EPs), have also emerged as a new paradigm for engineering the response of optical systems. In this talk, we provide an overview of recent developments in this newly emerging field. The use of other type symmetries in photonics will be also discussed.

Spatial learning is a complex behavior which includes, among others, encoding of space, sensory and motivational processes, arousal and locomotor performance. Today, our view on spatial navigation is largely hippocampus-centrist. Less is known about the involvement of brain structures up- and downstream, or out of this circuit. Here, we provide the fist in vivo assessment of the neural matrix underlying spatial learning, using functional manganese-enhanced MRI (MEMRI) and voxel-wise whole brain analysis. Mice underwent place-learning (PL) vs. response-learning (RL) in the water cross maze (WCM) and its readout was correlated to the Mn2+ contrasts. Thus, we identified structures involved in spatial learning largely overlooked in the past, due to methods focused on region of interest (ROI) analyses. Add-on experiments pointed to bias in Mn2+ accumulati! on towards projection terminals, suggesting that our mapping was mostly formed by projection sites of the originally activated structures. This is corroborated by in-depth analysis of MEMRI data after WCM learning showing mostly downstream targets of the hippocampus. These differ between fornical afferences from vCA1 and direct innervation from dCA1/iCA1 (for PL), and structures along the longitudinal association bundle originating in vCA1 (for RL). To elucidate the pattern of Mn2+ accumulation seen on the scans we performed c-fos expression analyses following learning in the WCM. This helped us identify the structures initially activated during spatial learning and its underlying connectivity to establish the matrix. Finally, to test the causal involvement of these structures we inhibited them (through DREADDs) while mice performed in the WCM task. We also focused on the causal involvement of the mPFC-HPC circuit on strategy switch during WCM learning. We believe that this study might shed light into new brain structures involved in spatial learning and strategy switch and complement the current knowledge on these circuits’ connectivity. Moreover, we elucidated some functional mechanisms of MEMRI, clarifying the interpretation of data obtained with this method and its possible future applications.