Networks

Networks provide organization through nodes that are connected by links. Characteristics of networks that matter include clusters, path lengths, link weights, and hubs. A semantic network displays connections among concepts in which shorter links represent stronger associations between two concepts. A spreading activation model is a theory of how related concepts become activated. Variations of the theory enable predictions, such as spreading activation, is partitioned among the links at a node. This assumption leads to the testable prediction that the strength of activation along each link diminishes as the number of links increases. Brain imaging has revealed that information transfer depends not only on the direct path between nodes but also on the availability of alternative detour paths. This hyperconnectivity following a lesion lowers efficiency and is reduced with recovery from brain injury.

The effect of time-dilation on Bell experiments in the retrocausal Brans model

The possibility of using retrocausality to obtain a fundamentally relativistic account of the Bell correlations has gained increasing recognition in recent years. It is not known, however, the extent to which these models can make use of their relativistic properties to account for relativistic effects on entangled systems. We consider here a hypothetical relativistic Bell experiment, where one of the wings experiences time-dilation effects. We show that the retrocausal Brans model ( Found. Phys. , 49 (2), 2019) can be easily generalized to analyse this experiment, and that it predicts less separation of eigenpackets in the wing experiencing the time-dilation. This causes the particle distribution patterns on the photographic plates to differ between the wings—an experimentally testable prediction of the model. We discuss the difficulties faced by other hidden variable models in describing this experiment, and their natural resolution in our model due to its relativistic properties. In particular, we discuss how a ψ -epistemic interpretation may resolve several difficulties encountered in relativistic generalizations of de Broglie–Bohm theory and objective state reduction models. Lastly, we argue that it is not clear at present, due to technical difficulties, if our prediction is reproduced by quantum field theory. We conclude that if it is, then the retrocausal Brans model predicts the same result with great simplicity in comparison. If not, the model can be experimentally tested.

Accounting Fundamentals and Systematic Risk: Corporate Failure over the Business Cycle

ABSTRACT In this paper, we use accounting fundamentals to measure systematic risk of distress. Our main testable prediction—that this risk increases with the probability of recessionary failure, P(R|F)—is based on a stylized model that guides our empirical analyses. We first apply the lasso method to select accounting fundamentals that can be combined into P(R|F) estimates. We then use the obtained estimates in asset-pricing tests. This approach successfully extracts systematic risk information from accounting data—we document a significant positive premium associated with P(R|F) estimates. The premium covaries with the news about the business cycle and aggregate failure rates. Additional tests underscore the importance of the “structure” imposed through recessionary-failure-probability estimation. The “agnostic” return predictor that relies only on past correlations between the same fundamental variables and returns exhibits markedly different properties. JEL Classifications: G12; G32; G33; M41.

Dual stochasticity in the cortex as a biologically plausible learning with the most efficient coding

Neurons and synapses in the cerebral cortex behave stochastically. The advantages of such stochastic properties have been proposed in several works, but the relationship and synergy between the stochasticities of neurons and synapses remain largely unexplored. Here, we show that these stochastic features can be inseparably integrated into a simple framework that provides a practical and biologically plausible learning algorithm that consistently accounts for various experimental results, including the most efficient power-law coding of the cortex. The derived algorithm overcomes many of the limitations of conventional learning algorithms of neural networks. As an experimentally testable prediction, we derived the slow retrograde modulation of the excitability of neurons from this algorithm. Because of the simplicity and flexibility of this algorithm, we anticipate that it will be useful in the development of neuromorphic devices and scalable AI chips, and that it will help bridge the gap between neuroscience and machine learning.

Reconciling CV phonotactics and high vowel deletion in Japanese

High vowel devoicing is a productive process in Japanese, where /i, u/ become unphonated between voiceless obstruents. Recent studies have shown that the vowels can completely delete as a result of the process, resulting in surface consonant clusters. This seemingly conflicts with the strong CV phonotactic preference that has repeatedly been shown in both phonological and psycholinguistic studies of Japanese. This paper proposes that the apparent conflict can be resolved by having phonotactic repairs and high vowel devoicing apply at different phonological levels, adopting a more sophisticated phonological representation than simple /underlying/ vs. [surface] forms. The proposed framework also makes an empirically testable prediction regarding syllabification of clusters that result from high vowel deletion.

Is the sky the limit? On the expansion threshold of a species’ range

More than a hundred years after Grigg’s influential analysis of species’ borders, the causes of limits to species’ ranges still represent a puzzle that has never been understood with clarity. The topic has become especially important recently as many scientists have become interested in the potential for species’ ranges to shift in response to climate change – and yet, nearly all of those studies fail to recognise or incorporate evolutionary genetics in a way that relates to theoretical developments. I show that range margins can be understood based on just two measurable parameters: i) the fitness cost of dispersal – a measure of environmental heterogeneity – and ii) the strength of genetic drift, which reduces genetic diversity. Together, these two parameters define an expansion threshold: adaptation fails when genetic drift reduces genetic diversity below that required for adaptation to environmental heterogeneity. When the key parameters drop below this expansion threshold locally, a sharp range margin forms. When they drop below this threshold throughout the species’ range, adaptation collapses everywhere, resulting in either extinction, or formation of a fragmented meta-population. Because the effects of dispersal differ fundamentally with dimension, the second parameter – the strength of genetic drift – is qualitatively different compared to a linear habitat. In two-dimensional habitats, genetic drift becomes effectively independent of selection. It decreases with neighbourhood size – the number of individuals accessible by dispersal within one generation. Moreover, in contrast to earlier predictions, which neglected evolution of genetic variance and/or stochasticity in two dimensions, dispersal into small marginal populations aids adaptation. This is because the reduction of both genetic and demographic stochasticity has a stronger effect than the cost of dispersal through increased maladaptation. The expansion threshold thus provides a novel, theoretically justified and testable prediction for formation of the range margin and collapse of the species’ range.Author summaryGene flow across environments has conflicting effects: while it increases the genetic variation necessary for adaptation and counters the loss of genetic diversity due to genetic drift, it may also swamp adaptation to local conditions. This interplay is crucial for the dynamics of a species’ range expansion, which can thus be understood based on two dimensionless parameters: i) the fitness cost of dispersal – a measure of environmental heterogeneity – and ii) the strength of genetic drift – a measure of reduction of genetic diversity. Together, these two parameters define an expansion threshold: adaptation fails when the number of individuals accessible by dispersal within one generation is so small that genetic drift reduces genetic diversity below the level required for adaptation to environmental heterogeneity. This threshold provides a novel, theoretically justified and testable prediction for formation of a range margin and a collapse of a species’ range in two-dimensional habitats.

A Gaussian Attractor Network for Memory and Recognition with Experience-Dependent Learning

Attractor networks are widely believed to underlie the memory systems of animals across different species. Existing models have succeeded in qualitatively modeling properties of attractor dynamics, but their computational abilities often suffer from poor representations for realistic complex patterns, spurious attractors, low storage capacity, and difficulty in identifying attractive fields of attractors. We propose a simple two-layer architecture, gaussian attractor network, which has no spurious attractors if patterns to be stored are uncorrelated and can store as many patterns as the number of neurons in the output layer. Meanwhile the attractive fields can be precisely quantified and manipulated. Equipped with experience-dependent unsupervised learning strategies, the network can exhibit both discrete and continuous attractor dynamics. A testable prediction based on numerical simulations is that there exist neurons in the brain that can discriminate two similar stimuli at first but cannot after extensive exposure to physically intermediate stimuli. Inspired by this network, we found that adding some local feedbacks to a well-known hierarchical visual recognition model, HMAX, can enable the model to reproduce some recent experimental results related to high-level visual perception.

Locally Balanced Dendritic Integration by Short-Term Synaptic Plasticity and Active Dendritic Conductances

The high degree of variability observed in spike trains and membrane potentials of pyramidal neurons in vivo is thought to be a consequence of a balance between excitatory and inhibitory inputs, which depends on the dynamics of the network. We simulated synaptic currents and ion channels in a reconstructed hippocampal CA1 pyramidal cell and show here that a local balance can be achieved on a dendritic branch with a different mechanism, based on presynaptic depression of quantal release interacting with active dendritic conductances. This mechanism, which does not require synaptic inhibition, allows each dendritic branch to remain sensitive to correlated synaptic inputs, induces a high degree of variability in the output spike train, and can be combined with other balance mechanisms based on network dynamics. This hypothesis makes a testable prediction for the cause of the observed variability in the firing of hippocampal place cells.