Identification of the transition rule in a modified cellular automata model: the case of dendritic NH4Br crystal growth

A method of identifying the transition rule, encapsulated in a modified cellular automata (CA) model, is demonstrated using experimentally observed evolution of dendritic crystal growth patterns in NH4Br crystals. The influence of the factors, such as experimental set-up and image pre-processing, colour and size calibrations, on the method of identification are discussed in detail. A noise reduction parameter and the diffusion velocity of the crystal boundary are also considered. The results show that the proposed method can in principle provide a good representation of the dendritic growth anisotropy of any system

Learning image components for object recognition

In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or features. Non-negative matrix factorisation is a generative model that has been specifically proposed for finding such meaningful representations of image data, through the use of non-negativity constraints on the factors. This article reports on an empirical investigation of the performance of non-negative matrix factorisation algorithms. It is found that such algorithms need to impose additional constraints on the sparseness of the factors in order to successfully deal with occlusion. However, these constraints can themselves result in these algorithms failing to identify image components under certain conditions. In contrast, a recognition model (a competitive learning neural network algorithm) reliably and accurately learns representations of elementary image features without such constraints

How gut sampling and microbial invasiveness/noninvasiveness provides mucosal immunity with a nonmolecular pattern means to distinguish commensals from pathogens: A review

Mucosal immunity distinguishes not only different microbial antigens but also separates those of pathogens from those of commensals. How this is done is unknown. The present view is that the pathogen/commensal determination of antigens depends upon as yet to be discovered molecular patterns. Here I review the biological feasibility that it also involves the detection of the invasive differences in their motility towards the gut wall when they are sampled by differently biased methods. 

By their nature, pathogens and commensals have different motility – invasive and noninvasive – in regard to the epithelium. The immune system is in a position to detect such motility differences. This biological opportunity arises since different microbe sampling methods can “catch” different groups of microbes depending upon how their motility interacts with the epithelium. A biological method biased to sample those with invasive motility—pathogens—could be achieved by ‘honey pot traps’ that preferentially (but not exclusively) sample microbes that have a taxis to breaches in the epithelium. A biological method biased to sample those that are noninvasive—commensals—could be done by capturing microbes that are passively and stably residing in the biofilm “offshore” of the epithelium. Such differential sampling strategies would seem to relate to those carried out respectively by (i) M-cells (working with subepithelial dome dendritic cells), and (ii) sub- and intraepithelial dendritic cells.

The interactions of antigen presentation can be arranged so that the immune system links antigens from biased microbial sampling with pathogenic or commensal appropriate immune responses. Such immune classification could feasibly occur biologically through a winner-take-all competition between inhibiting and activating antigen presentation. Winner-takes-all types of processing classification are already known to underlie the biologically interactions between neurons that classify sensory inputs making it also plausible that they are exploited by the immune system. In pathogen identification, M-cell antigens would be activating and biofilm antigens inhibitory, and vise versa for commensal identification. This winner-take-all competition between antigen presentation would act to amplify small statistical biases in the two samples linked to invasiveness/noninvasiveness into a reliable pathogen/commensal distinction. This process would both complement, and acts as independent guarantor, upon the alternative pathogenicity/commensality recognition provided by molecular pattern recognition. 

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Identification of Dendritic Processing in Spiking Neural Circuits

A large body of experimental evidence points to sophisticated signal processing taking place at the level of dendritic trees and dendritic branches of neurons. This evidence suggests that, in addition to inferring the connectivity between neurons, identifying analog dendritic processing in individual cells is fundamentally important to understanding the underlying principles of neural computation. In this thesis, we develop a novel theoretical framework for the identification of dendritic processing directly from spike times produced by spiking neurons. The problem setting of spiking neurons is necessary since such neurons make up the majority of electrically excitable cells in most nervous systems and it is often hard or even impossible to directly monitor the activity within dendrites. Thus, action potentials produced by neurons often constitute the only causal and observable correlate of dendritic processing. In order to remain true to the underlying biophysics of electrically excitable cells, we employ well-established mechanistic models of action potential generation to describe the nonlinear mapping of the aggregate current produced by the tree into an asynchronous sequence of spikes. Specific models of spike generation considered include conductance-based models such as Hodgkin-Huxley, Morris-Lecar, Fitzhugh-Nagumo, as well as simpler models of the integrate-and-fire and threshold-and-fire type. The aggregate time-varying current driving the spike generator is taken to be produced by a dendritic stimulus processor, which is a nonlinear dynamical system capable of describing arbitrary linear and nonlinear transformations performed on one or more input stimuli. In the case of multiple stimuli, it can also describe the cross-coupling, or interaction, between various stimulus features. The behavior of the dendritic stimulus processor is fully captured by one or more kernels, which provide a characterization of the signal processing that is consistent with the broader cable theory description of dendritic trees. We prove that the neural identification problem, stated in terms of identifying the kernels of the dendritic stimulus processor, is mathematically dual to the neural population encoding problem. Specifically, we show that the collection of spikes produced by a single neuron in multiple experimental trials can be treated as a single multidimensional spike train of a population of neurons encoding the parameters of the dendritic stimulus processor. Using the theory of sampling in reproducing kernel Hilbert spaces, we then derive precise results demonstrating that, during any experiment, the entire neural circuit is projected onto the space of input stimuli and parameters of this projection are faithfully encoded in the spike train. Spike times are shown to correspond to generalized samples, or measurements, of this projection in a system of coordinates that is not fixed but is both neuron- and stimulus-dependent. We examine the theoretical conditions under which it may be possible to reconstruct the dendritic stimulus processor from these samples and derive corresponding experimental conditions for the minimum number of spikes and stimuli that need to be used. We also provide explicit algorithms for reconstructing the kernel projection and demonstrate that, under natural conditions, this projection converges to the true kernel. The developed methodology is quite general and can be applied to a number of neural circuits. In particular, the methods discussed span all sensory modalities, including vision, audition and olfaction, in which external stimuli are typically continuous functions of time and space. The results can also be applied to circuits in higher brain centers that receive multi-dimensional spike trains as input stimuli instead of continuous signals. In addition, the modularity of the approach allows one to extend it to mixed-signal circuits processing both continuous and spiking stimuli, to circuits with extensive lateral connections and feedback, as well as to multisensory circuits concurrently processing multiple stimuli of different dimensions, such as audio and video. Another important extension of the approach can be used to estimate the phase response curves of a neuron. All of the theoretical results are accompanied by detailed examples demonstrating the performance of the proposed identification algorithms. We employ both synthetic and naturalistic stimuli such as natural video and audio to highlight the power of the approach. Finally, we consider the implication of our work on problems pertaining to neural encoding and decoding and discuss promising directions for future research

Cortical region interactions and the functional role of apical dendrites

The basal and distal apical dendrites of pyramidal cells occupy distinct cortical layers and are targeted by axons originating in different cortical regions. Hence, apical and basal dendrites receive information from distinct sources. Physiological evidence suggests that this anatomically observed segregation of input sources may have functional significance. This possibility has been explored in various connectionist models that employ neurons with functionally distinct apical and basal compartments. A neuron in which separate sets of inputs can be integrated independently has the potential to operate in a variety of ways which are not possible for the conventional model of a neuron in which all inputs are treated equally. This article thus considers how functionally distinct apical and basal dendrites can contribute to the information processing capacities of single neurons and, in particular, how information from different cortical regions could have disparate affects on neural activity and learning

Neural coding strategies and mechanisms of competition

A long running debate has concerned the question of whether neural representations are encoded using a distributed or a local coding scheme. In both schemes individual neurons respond to certain specific patterns of pre-synaptic activity. Hence, rather than being dichotomous, both coding schemes are based on the same representational mechanism. We argue that a population of neurons needs to be capable of learning both local and distributed representations, as appropriate to the task, and should be capable of generating both local and distributed codes in response to different stimuli. Many neural network algorithms, which are often employed as models of cognitive processes, fail to meet all these requirements. In contrast, we present a neural network architecture which enables a single algorithm to efficiently learn, and respond using, both types of coding scheme

Irrigation and phytolith formation:an experimental study

It has been proposed that phytoliths from archaeological sites can be indicators of water availability and hence inform about past agricultural practices (Rosen and Weiner, 1994; Madella et al., 2009). Rosen and Weiner (1994) found that the number of conjoined phytoliths fromcereal husks increased with irrigationwhile Madella et al. (2009) demonstrated that the ratio of long-celled phytoliths to short-celled phytoliths increased with irrigation. In order to further explore these hypotheses, wheat and barley were experimentally grown from 2005 to 2008 in three different crop growing stations in Jordan. Four different irrigation regimes were initially employed: 0% (rainfall only), 80%, 100%and 120% of the optimum crop water requirements, with a 40% plot being added in the second and third growing seasons. Each plot measured 5 m � 5 m and a drip irrigation system was used. Environmental variables were measured on a daily basis, and soil and water samples were taken and analysed at the University of Reading. Phytoliths from the husks of these experimentally grown plants were extracted using the dry ashing method. Results demonstrate that although the number of conjoined cells increases with irrigation, there were considerable intersite and inter-year differences suggesting that environmental variables other than water availability affect phytolith uptake and deposition. Furthermore, analytical experiments demonstrated that conjoined phytoliths are subject to change or breakage by external factors, making this methodology problematic to apply to archaeological phytolith assemblages that have an unknown taphonomic history. The ratio of long cells to short cells also responded to increased irrigation, and these forms are not subject to break up as are conjoined forms. Our results from the modern samples of durum wheat and six-row barley show that if an assemblage of single-celled phytoliths consists of over 60% dendritic long cells then this strongly suggests that the crop received optimum levels of water. Further research is needed to determine if this finding is consistent in phytolith samples from the leaves and stems, as suggested byMadella et al. (2009), and in other species of cereals. If this is the case then phytoliths are a valuble tool for assessing the level of past water availability and, potentially, past irrigation

Identification of geometrical models of interface evolution for dendritic crystal growth

This paper introduces a method for identifying geometrical models of interface evolution, directly from experimental imaging data. These local growth models relate normal growth velocity to curvature and its derivatives estimated along the growing interface. Such models can reproduce many qualitative features of dendritic crystal growth as well as predict quantitatively its early stages of evolution. Numerical simulations and experimental crystal growth data are used to demonstrate the applicability of this approach

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies