Spontaneous and Directed Symmetry Breaking in the Formation of Chiral Nanocrystals

The homochirality of biomolecules remains one of the outstanding puzzles concerning the beginning of life. Chiral amplification of a randomly perturbed racemic mixture of chiral molecules is a well-accepted prerequisite for all routes to biological homochirality. Some models have suggested that such amplification occurred due to asymmetric discrimination of chiral biotic or prebiotic molecules when they adsorbed onto crystalline surfaces. While chiral amplification has been demonstrated on surfaces of both chiral and achiral crystals, the mechanism that would produce an enantiomeric imbalance in the chiral surfaces themselves has not been addressed. Here we report strong chiral amplification in the colloidal synthesis of intrinsically chiral lanthanide phosphate nanocrystals, quantitatively measured via the circularly polarized luminescence of the lanthanide ions within the nanocrystals. The amplification involves spontaneous symmetry breaking into either left- or right-handed nanocrystals below a critical temperature. Furthermore, chiral tartaric acid molecules in the solution act as an external chiral field, sensitively directing the amplified nanocrystal handedness through a discontinuous transition between left- and right-handed excess. These characteristics suggest a conceptual framework for chiral amplification, based on the statistical thermodynamics of critical phenomena, which we use to quantitatively account for the observations. Our results demonstrate how chiral minerals with high enantiomeric excess could have grown locally in a primordial racemic aqueous environment.Comment: 9 pages, 4 figure

Unsupervised Learning with Self-Organizing Spiking Neural Networks

We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are trained in an unsupervised manner to learn a self-organized lattice of filters via excitatory-inhibitory interactions among populations of neurons. We develop and test various inhibition strategies, such as growing with inter-neuron distance and two distinct levels of inhibition. The quality of the unsupervised learning algorithm is evaluated using examples with known labels. Several biologically-inspired classification tools are proposed and compared, including population-level confidence rating, and n-grams using spike motif algorithm. Using the optimal choice of parameters, our approach produces improvements over state-of-art spiking neural networks

Public Housing in Israel: From Welfare State to Neoliberalism

This paper analyzes and explains the evolution of public housing in Israel. Compared to other countries, Israel’s public housing has undergone massive change throughout the last few decades, and is therefore an interesting case study. In the 1950s and 1960s, public housing made up 60% of the state’s total housing stock, and most of the population was eligible. Today, however, public housing is marginal in scope and earmarked for only 1.9% of the population, most of which come from the poorest households. This study presents the explanation of these changes and the current state of public housing in Israel.Cet article analyse et explique l’évolution du logement public en Israël. Comparé à d’autres pays, Israël a connu les plus grands changements dans ces logements au cours des dernières décennies et constitue donc une étude de cas intéressante. Alors que, dans les années 1950 et 1960, les logements sociaux représentaient 60% du parc total de logements de l’État, et la plupart de la population était éligible; aujourd’hui, les logements sociaux sont marginaux, réservés à seulement 1.9% de la population, principalement les ménages les plus pauvres. Cette étude présente l’explication de ces changements et de la situation actuelle du logement public en Israël

Regularity, Variability and Bi-Stability in the Activity of Cerebellar Purkinje Cells

Recent studies have demonstrated that the membrane potential of Purkinje cells is bi-stable and that this phenomenon underlies bi-modal simple spike firing. Membrane potential alternates between a depolarized state, that is associated with spontaneous simple spike firing (up state), and a quiescent hyperpolarized state (down state). A controversy has emerged regarding the relevance of bi-stability to the awake animal, yet recordings made from behaving cat Purkinje cells have demonstrated that at least 50% of the cells exhibit bi-modal firing. The robustness of the phenomenon in vitro or in anaesthetized systems on the one hand, and the controversy regarding its expression in behaving animals on the other hand suggest that state transitions are under neuronal control. Indeed, we have recently demonstrated that synaptic inputs can induce transitions between the states and suggested that the role of granule cell input is to control the states of Purkinje cells rather than increase or decrease firing rate gradually. We have also shown that the state of a Purkinje cell does not only affect its firing but also the waveform of climbing fiber-driven complex spikes and the associated calcium influx. These findings call for a reconsideration of the role of Purkinje cells in cerebellar function. In this manuscript we review the recent findings on Purkinje cell bi-stability and add some analyses of its effect on the regularity and variability of Purkinje cell activity

Control flow in active inference systems Part I: Classical and quantum formulations of active inference

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. In this Part I, we introduce the free-energy principle (FEP) and the idea of active inference as Bayesian prediction-error minimization, and show how the control problem arises in active inference systems. We then review classical and quantum formulations of the FEP, with the former being the classical limit of the latter. In the accompanying Part II, we show that when systems are described as executing active inference driven by the FEP, their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implemented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales