抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

In cultures of dissociated rat hippocampal neurons, persistent potentiation and depression of glutamatergic synapses were induced by correlated spiking of presynaptic and postsynaptic neurons. The relative timing between the presynaptic and postsynaptic spiking determined the direction and the extent of synaptic changes. Repetitive postsynaptic spiking within a time window of 20 msec after presynaptic activation resulted in long-term potentiation (LTP), whereas postsynaptic spiking within a window of 20 msec before the repetitive presynaptic activation led to long-term depression (LTD). Significant LTP occurred only at synapses with relatively low initial strength, whereas the extent of LTD did not show obvious dependence on the initial synaptic strength. Both LTP and LTD depended on the activation of NMDA receptors and were absent in cases in which the postsynaptic neurons were GABAergic in nature. Blockade of L-type calcium channels with nimodipine abolished the induction of LTD and reduced the extent of LTP. These results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb’s rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification.

https://www.jneurosci.org/content/jneuro/18/24/10464.full.pdf

Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type

N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

Whereas for smaller animals the eardrums are well-characterized as excitable membranes or drums, some animals such as several archosaurs feature, as a first approximation, a rather stiff elastic shell supported by an elastic ring. Mathematically, the theory of plates and shells is applicable but its governing equations overly complicate the modeling. Here the notion of tympanic structure is introduced as a generalization of “ordinary” tympanic membranes so as to account for sound perception as it occurs in archosaurs, such as birds and crocodilians. A mathematical model for the tympanic structure in many archosaurs called two-spring model implements this notion. The model is exactly soluble and solutions are presented in closed form and as a series expansion. Special emphasis is put onto offering an easy-to-apply model for describing experiments and performing numerical studies. The analytic treatment is supplemented by a discussion of the applicability of the two-spring model in auditory research. An elasticity-theoretic perspective of the two-spring model is given in the Appendix.

/pdf/effective-spring-model-of-tympanic-response-in-archosaurs-3r1ukba66j.pdf

Effective-Spring Model of Tympanic Response in Archosaurs

Crickets and other orthoptera such as locusts and cock-roaches possess two sensory organs, called cerci, on theback of their body. The cerci are covered by a variety of dif-ferent sensor types, viz., acceleration sensors, chemore-ceptors, touch sensors and, what we focus on here, flowsensors. Flow senors, also called filiform hairs, consist ofa long thin hair, coupling viscously to the motion of thesurrounding air, and a mechano-sensory hair cell, trans-lating hair deflection into spikes emitted into ascendingnerve fibers. The ascending axons project into the termi-nal abdominal ganglion and form a map, representing thedirection of the air flow around the cerci. Four of theinterneurons (R10-2a, R10-3a, L10-2a and L10-3a) arisingfrom the cricket's terminal abdominal ganglion seem toencode the direction of the flow field by voting throughmaximal firing rates for one of four directions approxi-mately given by 45°, 135°, 225° and 315° with respect tothe long axis of the cricket in its plane of motion [1].Behavioral studies e.g., [2], indicate that determining thestimulus direction is an essential feature of an appropriateresponse.We propose that the four interneurons implement a pop-ulation vector code and show that it is capable of estimat-ing the direction properly. To test this hypothesis, we havecomputed a realistic flow field around the cerci caused byan attacking predator so as to determine the stimulus tothe sensor system during attack. A good description of themotion of a filiform hair is that of a harmonic oscillatorwith the hair oscillating in the plane of preferred direc-tion. By applying the population vector code to randomlydistributed hairs of different lengths and directions allover a cercus, we obtain a good neuronal reconstructionof the direction of an attacking predator.

/pdf/how-crickets-determine-the-direction-of-a-flow-field-276xwh65yi.pdf

How crickets determine the direction of a flow field

Introduction Spike-Timing-Dependent Plasticity (STDP) describes the change in the strength (or weight) of neuronal connections depending on the preand post-synaptic firing activity [1-3], which in turn changes the spiking properties of the post-synaptic neurons on a slower time scale. The interplay between the parameters of the neurons, the plasticity rule and the input stimulation results in changes to the network structure by strengthening or weakening pathways. This leads to functional changes of the neuronal information processing that takes place in the network. In addition to learning spiking-rate-based information, STDP captures the effect of spike-time correlations on short time scales, down to milliseconds, which are neglected by rate-based learning. STDP has been demonstrated to capture, for example, interaural time differences [1] and the presence of narrowly distributed correlations among input spike trains [2].

/pdf/interplay-between-spike-timing-dependent-plasticity-and-1d9ai81qjd.pdf

Interplay between spike-timing-dependent plasticity and neuronal correlations gives rise to network structure

Feature linking and segmentation of four stationary patterns are shown to be performed as simultaneous processes by a fully connected, auto-associative neural network of spiking neurons The patterns have been learned through an asymmetric, Hebbian rule that can handle a varying low activity In this case the total activity of the patterns ranges between 4 and 7% The underlying model is the ‘spike response model’ Spiking is achieved by an absolute refractory period (1ms) while an inhibitory delay loop prevents continuous firing Reaching the synapse after some axonal delay each spike evokes an excitatory or inhibitory postsynaptic potential (EPSP or IPSP) with a realistic response at the receiving neuron Each neuron sums up its input signals linearly and acts as a noisy threshold element for generating a new spike

J. Leo van Hemmen

Papers

Effective-Spring Model of Tympanic Response in Archosaurs

How crickets determine the direction of a flow field

Interplay between spike-timing-dependent plasticity and neuronal correlations gives rise to network structure

Pattern Segmentation and Feature Linking as Simultaneous Processes in an Associative Network of Spiking Neurons

An analytically solvable model of collective excitation patterns in cortical tissue