A functional perspective on machine learning via programmable induction and abduction

We present a programming language for machine learning based on the concepts of ‘induction’ and ‘abduction’ as encountered in Peirce’s logic of science. We consider the desirable features such a language must have, and we identify the ‘abductive decoupling’ of parameters as a key general enabler of these features. Both an idealised abductive calculus and its implementation as a PPX extension of OCaml are presented, along with several simple examples

Spatial Distribution of Calcium-Gated Chloride Channels in Olfactory Cilia

Background: In vertebrate olfactory receptor neurons, sensory cilia transduce odor stimuli into changes in neuronal membrane potential. The voltage changes are primarily caused by the sequential openings of two types of channel: a cyclic-nucleotide-gated (CNG) cationic channel and a calcium-gated chloride channel. In frog, the cilia are 25 to 200 mm in length, so the spatial distributions of the channels may be an important determinant of odor sensitivity. Principal Findings: To determine the spatial distribution of the chloride channels, we recorded from single cilia as calcium was allowed to diffuse down the length of the cilium and activate the channels. A computational model of this experiment allowed an estimate of the spatial distribution of the chloride channels. On average, the channels were concentrated in a narrow band centered at a distance of 29 % of the ciliary length, measured from the base of the cilium. This matches the location of the CNG channels determined previously. This non-uniform distribution of transduction proteins is consistent with similar findings in other cilia. Conclusions: On average, the two types of olfactory transduction channel are concentrated in the same region of the cilium

Estimating the evidence of selection and the reliability of inference in unigenic evolution

<p>Abstract</p> <p>Background</p> <p>Unigenic evolution is a large-scale mutagenesis experiment used to identify residues that are potentially important for protein function. Both currently-used methods for the analysis of unigenic evolution data analyze 'windows' of contiguous sites, a strategy that increases statistical power but incorrectly assumes that functionally-critical sites are contiguous. In addition, both methods require the questionable assumption of asymptotically-large sample size due to the presumption of approximate normality.</p> <p>Results</p> <p>We develop a novel approach, termed the Evidence of Selection (EoS), removing the assumption that functionally important sites are adjacent in sequence and and explicitly modelling the effects of limited sample-size. Precise statistical derivations show that the EoS score can be easily interpreted as an expected log-odds-ratio between two competing hypotheses, namely, the hypothetical presence or absence of functional selection for a given site. Using the EoS score, we then develop selection criteria by which functionally-important yet non-adjacent sites can be identified. An approximate power analysis is also developed to estimate the reliability of inference given the data. We validate and demonstrate the the practical utility of our method by analysis of the homing endonuclease <monospace>I-Bmol</monospace>, comparing our predictions with the results of existing methods.</p> <p>Conclusions</p> <p>Our method is able to assess both the evidence of selection at individual amino acid sites and estimate the reliability of those inferences. Experimental validation with <monospace>I-Bmol</monospace> proves its utility to identify functionally-important residues of poorly characterized proteins, demonstrating increased sensitivity over previous methods without loss of specificity. With the ability to guide the selection of precise experimental mutagenesis conditions, our method helps make unigenic analysis a more broadly applicable technique with which to probe protein function.</p> <p>Availability</p> <p>Software to compute, plot, and summarize EoS data is available as an open-source package called 'unigenic' for the 'R' programming language at <url>http://www.fernandes.org/txp/article/13/an-analytical-framework-for-unigenic-evolution</url>.</p

Local Field Potential Modeling Predicts Dense Activation in Cerebellar Granule Cells Clusters under LTP and LTD Control

Local field-potentials (LFPs) are generated by neuronal ensembles and contain information about the activity of single neurons. Here, the LFPs of the cerebellar granular layer and their changes during long-term synaptic plasticity (LTP and LTD) were recorded in response to punctate facial stimulation in the rat in vivo. The LFP comprised a trigeminal (T) and a cortical (C) wave. T and C, which derived from independent granule cell clusters, co-varied during LTP and LTD. To extract information about the underlying cellular activities, the LFP was reconstructed using a repetitive convolution (ReConv) of the extracellular potential generated by a detailed multicompartmental model of the granule cell. The mossy fiber input patterns were determined using a Blind Source Separation (BSS) algorithm. The major component of the LFP was generated by the granule cell spike Na+ current, which caused a powerful sink in the axon initial segment with the source located in the soma and dendrites. Reproducing the LFP changes observed during LTP and LTD required modifications in both release probability and intrinsic excitability at the mossy fiber-granule cells relay. Synaptic plasticity and Golgi cell feed-forward inhibition proved critical for controlling the percentage of active granule cells, which was 11% in standard conditions but ranged from 3% during LTD to 21% during LTP and raised over 50% when inhibition was reduced. The emerging picture is that of independent (but neighboring) trigeminal and cortical channels, in which synaptic plasticity and feed-forward inhibition effectively regulate the number of discharging granule cells and emitted spikes generating “dense” activity clusters in the cerebellar granular layer

Sframe: An Efficient System for Detailed DC Simulation of Bipolar Analog Integrated Circuits Using Continuation Methods

In this paper we describe an experimental system called sframe which is being incorporated into the design for manufacturability initiative at the Reading Works of AT&T Bell Laboratories. Our system is able to perform detailed and accurate DC analyses of integrated circuits containing several hundred transistors to be fabricated in a relatively complex junction isolated complementary technology

Multivariate abstract approximation for banach space valued functions

Here we study quantitatively the high degree of approximation of sequences of linear operators acting on Banach space valued Fréchet differentiable functions to the unit operator, as well as other basic approximations including ones under convexity