Input Connections Research Articles

Designing New Cellular Signaling Pathways

All cells respond to signals from the environment. Extracellular stimuli activate intracellular signal transduction pathways that make decisions about cell identity, behavior, and survival. A nascent field aims to design and construct new signaling pathways beyond those found in nature. Current strategies exploit the structural modularity of many signaling proteins, which makes them inherently amenable to domain-swapping tactics that exchange their input and output connections. The results reveal a remarkable degree of functional plasticity in signaling proteins and pathways, as well as regulatory logic that can be transported to new proteins. Modified adaptor and scaffold proteins can reroute signal traffic and adjust the response behavior of the pathway circuit. These synthetic biology approaches promise to deepen our understanding of existing signaling pathways and spur the development of new cellular tools for research, industry, and medicine.

E208 AUTOMATIC CODE GENERATION SYSTEM FOR POWER PLANT DYNAMIC SIMULATORS(Power System-2)

A plant dynamic simulator was developed for analyzing operational characteristics of power generation plants. The main feature of the simulator is an automatic code generation functionality through links to CAD systems containing information on piping and instrumentation of plants that have been specified during plant design. The automatic code generation system is expected to provide significant reductions in development time for new plant dynamic simulators. The automatic code generation system was applied to a conventional power plant system with a condenser and a water feed system. The following results were obtained: 1) Component models were identified from the CAD systems by automatic analysis of the data structure of piping and instrumentation diagrams. 2) The identified component models were automatically related to input and output connections according to the given system piping configurations.

Orientation processing without orientation maps in the pigeon analogue to the primary visual cortex.

Event Abstract Back to Event Orientation processing without orientation maps in the pigeon analogue to the primary visual cortex. In most mammalian brains, orientation selective neurons of the primary visual cortex are clustered into processing arrays that align vertically to the surface, such that orientation preference is varied systematically in a pinwheel-like arrangement. However in rodents, particularly the highly visual squirrel, no such clustering has been found despite clear orientation selectivity at the single cell level, thereby suggesting that extensive spatial clustering of similarly selective units is not necessary for basic visual capabilities. In this study, we evaluated the functional organization in the visual wulst of the pigeon, an avian model known to depend heavily on its visual abilities. The pigeon visual wulst is widely assumed to be functionally analogous to the mammalian primary visual cortex, mainly due to its input connectivity to the geniculate nucleus in the thalamofugal pathway. However, further details as to its specific functional role, including its functional architecture for orientation processing is virtually unknown. Using voltage sensitive dye imaging and electrophysiology, we examined neuronal population activity evoked by drifting gratings of various spatiotemporal frequencies and orientations covering a large portion of the contralateral visual field (~120°x80°). Here we report that these large stimuli evoked an activation that first emerged from a local region ~25ms after stimulus onset. Local emergence was followed by a rapid spread of activity that covered the whole visual wulst, peaking at ~200ms before rapidly adapting to near-baseline levels (after ~600ms) despite ongoing visual input. We were able to distinguish two different regions; a local, central input region that was sensitive to stimulus duration, and a surround region that showed delayed suppression independent of stimulus duration. We did not detect any clustering of orientation selective units into regular and distinct orientation domains with particular orientation preferences, but instead found a dominant representation of the vertical orientation. In conclusion, our results show that wulst processing of oriented stimuli differs from that in the striate visual cortex. In response to a large stimulus, we observed the local emergence, followed by brisk spread and rapid adaptation of activity. This argues for a local input area via which incoming information is distributed to the remainder of the visual wulst, while co-activating strong suppressive mechanisms. Further, the lack of orientation maps in the pigeon reinforces the idea that similar-preference clustering may not be crucial to orientation processing. Finally, we suggest that the dominance of the vertical orientation can be a functional adaptation to the specific visual demands of flight. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Orientation processing without orientation maps in the pigeon analogue to the primary visual cortex.. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.051 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Jan 2009; Published Online: 30 Jan 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Memory traces in dynamical systems

To perform nontrivial, real-time computations on a sensory input stream, biological systems must retain a short-term memory trace of their recent inputs. It has been proposed that generic high-dimensional dynamical systems could retain a memory trace for past inputs in their current state. This raises important questions about the fundamental limits of such memory traces and the properties required of dynamical systems to achieve these limits. We address these issues by applying Fisher information theory to dynamical systems driven by time-dependent signals corrupted by noise. We introduce the Fisher Memory Curve (FMC) as a measure of the signal-to-noise ratio (SNR) embedded in the dynamical state relative to the input SNR. The integrated FMC indicates the total memory capacity. We apply this theory to linear neuronal networks and show that the capacity of networks with normal connectivity matrices is exactly 1 and that of any network of N neurons is, at most, N. A nonnormal network achieving this bound is subject to stringent design constraints: It must have a hidden feedforward architecture that superlinearly amplifies its input for a time of order N, and the input connectivity must optimally match this architecture. The memory capacity of networks subject to saturating nonlinearities is further limited, and cannot exceed square root N. This limit can be realized by feedforward structures with divergent fan out that distributes the signal across neurons, thereby avoiding saturation. We illustrate the generality of the theory by showing that memory in fluid systems can be sustained by transient nonnormal amplification due to convective instability or the onset of turbulence.

A stimulus-dependent connectivity analysis of neuronal networks

We present an analysis of interactions among neurons in stimulus-driven networks that is designed to control for effects from unmeasured neurons. This work builds on previous connectivity analyses that assumed connectivity strength to be constant with respect to the stimulus. Since unmeasured neuron activity can modulate with the stimulus, the effective strength of common input connections from such hidden neurons can also modulate with the stimulus. By explicitly accounting for the resulting stimulus-dependence of effective interactions among measured neurons, we are able to remove ambiguity in the classification of causal interactions that resulted from classification errors in the previous analyses. In this way, we can more reliably distinguish causal connections among measured neurons from common input connections that arise from hidden network nodes. The approach is derived in a general mathematical framework that can be applied to other types of networks. We illustrate the effects of stimulus-dependent connectivity estimates with simulations of neurons responding to a visual stimulus.

Development of input connections in neural cultures

We introduce an approach for the quantitative assessment of the connectivity in neuronal cultures, based on the statistical mechanics of percolation on a graph. This allows us to monitor the development of the culture and to see the emergence of connectivity in the network. The culture becomes fully connected at a time equivalent to the expected time of birth. The spontaneous bursting activity that characterizes cultures develops in parallel with the connectivity. The average number of inputs per neuron can be quantitatively determined in units of m(0), the number of activated inputs needed to excite the neuron. For m(0) approximately 15 we find that hippocampal neurons have on average approximately 60-120 inputs, whereas cortical neurons have approximately 75-150, depending on neuronal density. The ratio of excitatory to inhibitory neurons is determined by using the GABA(A) antagonist bicuculine. This ratio changes during development and reaches the final value at day 7-8, coinciding with the expected time of the GABA switch. For hippocampal cultures the inhibitory cells comprise approximately 30% of the neurons in the culture whereas for cortical cultures they are approximately 20%. Such detailed global information on the connectivity of networks in neuronal cultures is at present inaccessible by any electrophysiological or other technique.

A model of associative learning in the mushroom body

The mushroom body is a prominent invertebrate neuropil strongly associated with learning and memory. We built a high-level computational model of this structure using simplified but realistic models of neurons and synapses, and developed a learning rule based on activity dependent pre-synaptic facilitation. We show that our model, which is consistent with mushroom body Drosophila data and incorporates Aplysia learning, is able to both acquire and later recall CS-US associations. We demonstrate that a highly divergent input connectivity to the mushroom body and strong periodic inhibition both serve to improve overall learning performance. We also examine the problem of how synaptic conductance, driven by successive training events, obtains a value appropriate for the stimulus being learnt. We employ two feedback mechanisms: one stabilises strength at an initial level appropriate for an association; another prevents strength increase for established associations.

Flow Cytometric FRET Analysis of erbB Receptor Interaction on a Cell‐by‐Cell Basis

Lateral interaction of c-erbB family receptors resulting in dimer formation is the key event initiating signal transduction. Consequently cross-activation and intracellular signaling is triggered with immediate impact on cell proliferation, migration, cell survival, and differentiation. In order to elucidate the connection of signal input (receptor activation) and signal output (altered cellular behavior) we dynamically assessed cell proliferation of BT474 and SK-BR-3 breast cancer cell lines. We quantitated c-erbB2 receptor homodimerization upon treatment with the therapeutic monoclonal anti-c-erbB2 antibodies trastuzumab (Herceptin) and pertuzumab by flow cytometric FRET (FCET) measurements on a cell-by-cell basis and calculated the extent of antibody-induced cell cycle exit. The results confirm that trastuzumab does not decrease c-erbB2 homodimers despite its strong potency to drive c-erbB2-overexpressing cells into quiescence. Pertuzumab, however, is able to prevent c-erbB2 homodimerization and thereby enhance the antiproliferative effect of trastuzumab when administered in combination.

Risk perception in epidemic modeling

We investigate the effects of risk perception in a simple model of epidemic spreading. We assume that the perception of the risk of being infected depends on the fraction of neighbors that are ill. The effect of this factor is to decrease the infectivity, that therefore becomes a dynamical component of the model. We study the problem in the mean-field approximation and by numerical simulations for regular, random, and scale-free networks. We show that for homogeneous and random networks, there is always a value of perception that stops the epidemics. In the "worst-case" scenario of a scale-free network with diverging input connectivity, a linear perception cannot stop the epidemics; however, we show that a nonlinear increase of the perception risk may lead to the extinction of the disease. This transition is discontinuous, and is not predicted by the mean-field analysis.

Topological and dynamical properties of genetic and social networks

AbstractTheoretical investigations of complex systems have provided statistical methodologies for the analysis of topological and dynamical properties of biological networks. We have compared the topology and cluster structures of the genes in plasmids and of the transcriptional network from three different studies on S. pombe cell cycle. The topology of the plasmid genes provides insights into the ecology of gene diffusion among bacterial species. We have used a phase and a clustering related parameters to gauge the level of inter‐connectivity around nodes in a transcriptional network. The analysis reveals genetic relationships at the boundaries of different cell cycle events. In human networks topological changes occur because of risk awareness, trust estimation i.e. the perception of the state of the other individuals (nodes). We found that this effect introduces a much richer dynamics. We show that for finite average network connectivity, there is always a value of risk perception that stops the epidemics. For scale‐free networks with diverging input connectivity, a linear perception cannot stop the epidemics. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

COUP-TFI regulates the balance of cortical patterning between frontal/motor and sensory areas

We used cortex-specific deletion of the transcription factor gene COUP-TFI (also known as Nr2f1) in mice to demonstrate previously unknown fundamental roles for it in patterning mammalian neocortex into areas. The highest COUP-TFI expression is observed in the cortical progenitors and progeny in parietal and occipital cortex that form sensory areas, and the lowest expression was observed in frontal cortex that includes motor areas. Cortical deletion of COUP-TFI resulted in massive expansion of frontal areas, including motor, to occupy most of neocortex, paralleled by marked compression of sensory areas to caudal occipital cortex. These area patterning changes are preceded and paralleled by corresponding changes in molecular markers of area identity and altered axonal projections to maintain patterned area-specific input and output connections. We conclude that COUP-TFI is required for balancing patterning of neocortex into frontal/motor and sensory areas by acting in its expression domain to repress frontal/motor area identities and to specify sensory area identities.

Tester Board for testing mass-produced SMB modules for CMS Preshower

We have developed a Tester Board to test the electrical characteristics of theSystem Motherboard (SMB) for the CMS Preshower detector at CERN. The board is designedto test input resistances, output resistances, connections, interconnections and possible short-circuits of a module having up to 640 connector pins. The Tester Board is general-purpose innature: it could be used to test any electronic module or cable by using dedicated cable sets. Themodule can detect a variety of problems not detected by either functional tests or the "flyingprobes" technique. The design, algorithms and results of using the Tester Board during massproduction of CMS Preshower SMBs are presented.

Greedy network-growing algorithm with Minkowski distances

In this paper, we propose a new network-growing method to accelerate learning and to extract explicit features in complex input patterns. We have so far proposed a new type of network-growing algorithm called greedy network growing algorithm (Kamimura et al. 2002, Kamimura 2003). With this algorithm, a network can grow gradually by maximizing information on input patterns. In the algorithm, the inverse of the square of the ordinary Euclidean distance between input patterns and connection weights is used to produce competitive unit outputs. When applied to some problems, the method has shown slow learning, and sometimes the method cannot produce a state where information is large enough to produce explicit internal representations. To remedy this shortcoming, we introduce here Minkowski distance between input patterns and connection weights used to produce competitive unit outputs. When the power for the Minkowski distance is larger, some detailed parts in input patterns can be eliminated, which enables networks to converge faster and to extract main parts of input patterns. We applied our new method to the famous dipole problem, a student survey on computer skills and the analysis of some economic data. In these experiments, results confirmed that, compared with the previous algorithm with Euclidean distance, the new method with Minkowski distance can significantly accelerate learning, and clearer features can be extracted.

Analysis and Implementation of a High Efficiency, Interleaved Current-Fed Full Bridge Converter for Fuel Cell System

An interleaved current-fed full bridge (ICFFB) dc-dc converter is proposed in this paper that has low input current ripple to meet the fuel cell demands. By interleaving two isolated CFFB converters with parallel input and series output connection, both input current ripple and output voltage ripple can be reduced. In addition, the size of the magnetic components and current stress of the semiconductor devices on the input side are also reduced. Similarly, smaller voltage rating components can be used on the output side. Only one digital signal processor microcontroller is used to generate phase-shifted gate signals and to implement a cascaded digital control system. The main features of the proposed converter are high efficiency, small passive component size, and small input current ripple. Experimental results for a 1.2-kW interleaved CFFB converter are provided in the paper

Associative learning method in a hypercolumn model

We propose an associatively learnable hypercolumn model (AHCM). A hyper-column model is a self-organized, competitive, and hierarchical multilayer neural network. It is derived from the neocognitron by replacing each S cell and C cell with a two-layer hierarchical self-organizing map. The HCM can recognize images with variant object size, position, orientation and spatial resolution. However, feature maps may integrate some features extracted in the lower layer even if the features are extracted from input data which belong to different categories. The learning algorithm of the HCM causes this problem because it is an unsupervised learning used by a self-organizing map. An associative learning method is therefore introduced, which is derived from the HCM by appending associative signals and associative weights to traditional input data and connection weights, respectively. The AHCM was applied to hand-shape recognition. We found that the AHCM could generate an appropriate feature map and higher recognition accuracy compared with the HCM.

Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data

In a previous study it was shown that a simple random Boolean network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1.

Top-down and bottom-up effects in pure alexia: Evidence from eye movements

The eye movements of a patient with pure alexia, GJ, were recorded as he read sentences in order to explore the roles of top-down and bottom-up information during letter-by-letter reading. Specifically, the effects of word frequency and word predictability were examined. Additional analyses examined the interaction of these effects with the lower level influences of word length and letter confusability. The results indicate that GJ is sensitive to all four of these variables in sentence reading. These findings support an interactive account of reading where letter-by-letter readers use both bottom-up and top-down information to decode words. Due to the disrupted bottom-up processes caused by damage to the Visual Word Form Area or the input connections to it, pure alexic patients rely more heavily on intact top-down information in reading.

Exploiting History-Dependent Effects to Infer Network Connectivity

We present an approach to distinguish between causal connections and common input connections among nodes in a network. By modeling how the activity of a node depends on its own recent history, we demonstrate how this history dependence predicts different patterns of activity depending on the nature of the network connectivity. In particular, a causal connection between a pair of observed nodes can be distinguished from common input connections that originate from nodes whose activity remains unobserved. This work builds on previous results where this same distinction was made based on modeling how the activity of a node depends on measured external variables such as stimuli. The results have a potentially broad range of application as the analysis can be based on a fairly generic class of models.

A Front-End Readout for Micro Bolometers Having Sub Nano Volt Noise Floor

We present our circuit solution for the second stage of the front-end readout of an array of mu-bolometers, cryogenic detectors having Si or Ge thermistor as sensing element. The front-end consists in a cold unity gain buffer stage (with single sided input connection) followed by a room temperature second differential amplifying stage. The optimization of the input transistor of the set-up has been determined by comparing the performances obtained applying a low pass filter or the optimum filter at output of the front-end. To minimize EMI interference the output of the cold buffer stage and its ground reference signal, taken close to the buffer itself, are readout in differential way from the second stage. To minimize noise, in our circuit solution the differential amplifier has an input stage with a noise that is 1.6 times smaller (1.27 times smaller if we consider the Square Root noise, SRn) than in a standard differential solution. Since the series noise is inversely proportional to the Gate area, to further reduce the noise the input JFET of the second stage has a large Gate area, of about 1 nF, and a very small feedback resistance. The performances of the presented second differential stage amplifier are about 0.194 (nV) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /Hz (440pV/radicHz for the SRn), and a figure of less than 1.21 (nV) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2 </sup> /Hz (or 1100 pV/radicHz SRn) at 1 Hz. Parallel noise is completely negligible over the ranges of the expected output impedances of the cold stage. Dynamic input impedance is a few tens of pF. The voltage gain is 500 V/V, AC coupled down to 80 mHz

Design, in vitro and in vivo assessment of a multi-channel sieve electrode with integrated multiplexer

This paper reports on the design, in vitro and in vivo investigation of a flexible, lightweight, polyimide based implantable sieve electrode with a hybrid assembly of multiplexers and polymer encapsulation. The integration of multiplexers enables us to connect a large number of electrodes on the sieve using few input connections. The implant assembly of the sieve electrode with the electronic circuitry was verified by impedance measurement. The 27 platinum electrodes of the sieve were coated with platinum black to reduce the electrode impedance. The impedance magnitude of the electrode sites on the sieve (geometric surface area 2200 µm2) was |Zf=1kHz| = 5.7 kΩ. The sieve electrodes, encased in silicone, have been implanted in the transected sciatic nerve of rats. Initial experiments showed that axons regenerated through the holes of the sieve and reinnervated distal target organs. Nerve signals were recorded in preliminary tests after 3–7 months post-implantation.