Ambiguous model learning made unambiguous with 1/f priors

What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the answer depends on the prior expectations of the dynamics of the model parameter that is to be inferred from the data. Local time constraints on the priors are insufficient to pick one interpretation over another. On the other hand, nonlocal time constraints, induced by a $1/f$ noise spectrum of the priors, is shown to permit learning of a specific model parameter even when there are infinitely many equally plausible interpretations of the data. This transition is inferred by a remarkable mapping of the model estimation problem to a dissipative physical system, allowing the use of powerful statistical mechanical methods to uncover the transition from indeterminate to determinate model learning.Comment: 8 pages, 2 figure

Developmental Coordination of Gene Expression between Synaptic Partners During GABAergic Circuit Assembly in Cerebellar Cortex

The assembly of neural circuits involves multiple sequential steps such as the specification of cell-types, their migration to proper brain locations, morphological and physiological differentiation, and the formation and maturation of synaptic connections. This intricate and often prolonged process is guided by elaborate genetic mechanisms that regulate each step. Evidence from numerous systems suggests that each cell-type, once specified, is endowed with a genetic program that unfolds in response to, and is regulated by, extrinsic signals, including cell–cell and synaptic interactions. To a large extent, the execution of this intrinsic program is achieved by the expression of specific sets of genes that support distinct developmental processes. Therefore, a comprehensive analysis of the developmental progression of gene expression in synaptic partners of neurons may provide a basis for exploring the genetic mechanisms regulating circuit assembly. Here we examined the developmental gene expression profiles of well-defined cell-types in a stereotyped microcircuit of the cerebellar cortex. We found that the transcriptomes of Purkinje cell and stellate/basket cells are highly dynamic throughout postnatal development. We revealed “phasic expression” of transcription factors, ion channels, receptors, cell adhesion molecules, gap junction proteins, and identified distinct molecular pathways that might contribute to sequential steps of cerebellar inhibitory circuit formation. We further revealed a correlation between genomic clustering and developmental co-expression of hundreds of transcripts, suggesting the involvement of chromatin level gene regulation during circuit formation

An information-theoretic analysis of genetics, gender and age in cancer patients

Peer reviewe

Statistical mechanics of multistable perception

The stochastic dynamics of multistable perception poses an enduring challenge to our understanding of neural signal processing in the brain. We show that the emergence of perception switching and stability can be understood using principles of probabilistic Bayesian inference where the prior temporal expectations are matched to a scale-free power spectrum, characteristic of fluctuations in the natural environment. The optimal percept dynamics are inferred by an exact mapping of the statistical estimation problem to the motion of a dissipative quantum particle in a multi-well potential. In the bistable case the problem is further mapped to a long-ranged Ising model. Optimal inference in the presence of a 1/f noise prior leads to critical dynamics, exhibiting a dynamical phase transition from unstable perception to stable perception, as demonstrated in recent experiments. The effect of stimulus fluctuations and perception bias is also discussed.Received August 19, 2014.Accepted August 19, 2014.© 2014, Published by Cold Spring Harbor Laboratory PressThis pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0

The Inheritance of p53

The p53 pathway constitutes a major cellular gene network that is crucial in directing the suppression of cancer formation, mediating the response to commonly used cancer therapies, as well as the regulation of germline maintenance, fertility, and reproduction. It has been demonstrated that various cancer predisposition syndromes are caused by low-frequency, highly penetrant inherited mutations in the p53 network, the knowledge of which is already positively affecting patient survival. Mounting evidence from studies utilizing human material, patient cohorts, and mouse models suggests that higher frequency, lesser penetrant genetic variants can also affect p53 signaling, resulting in differences in cancer risk, prognosis, response to therapies, and/or natural selection. Indeed, multiple genes in the p53 network have been shown to harbor functional single nucleotide polymorphisms (SNPs). Comprehensive analyses of two SNPs have demonstrated that their effects on cancer can be modified by factors such as gender, estrogen, and other p53 pathway SNPs. Together these insights suggest that genetic variants in the p53 network could present an excellent opportunity to further define individuals in their abilities to react to stress, suppress tumor formation, and respond to therapies