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

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

/pdf/whatever-next-predictive-brains-situated-agents-and-the-epj56cs868.pdf

Whatever next? Predictive brains, situated agents, and the future of cognitive science

Synchronized oscillations and inhibitory interneurons have important and interconnected roles within cortical microcircuits. In particular, interneurons defined by the fast-spiking phenotype and expression of the calcium-binding protein parvalbumin have been suggested to be involved in gamma (30-80 Hz) oscillations, which are hypothesized to enhance information processing. However, because parvalbumin interneurons cannot be selectively controlled, definitive tests of their functional significance in gamma oscillations, and quantitative assessment of the impact of parvalbumin interneurons and gamma oscillations on cortical circuits, have been lacking despite potentially enormous significance (for example, abnormalities in parvalbumin interneurons may underlie altered gamma-frequency synchronization and cognition in schizophrenia and autism). Here we use a panel of optogenetic technologies in mice to selectively modulate multiple distinct circuit elements in neocortex, alone or in combination. We find that inhibiting parvalbumin interneurons suppresses gamma oscillations in vivo, whereas driving these interneurons (even by means of non-rhythmic principal cell activity) is sufficient to generate emergent gamma-frequency rhythmicity. Moreover, gamma-frequency modulation of excitatory input in turn was found to enhance signal transmission in neocortex by reducing circuit noise and amplifying circuit signals, including inputs to parvalbumin interneurons. As demonstrated here, optogenetics opens the door to a new kind of informational analysis of brain function, permitting quantitative delineation of the functional significance of individual elements in the emergent operation and function of intact neural circuitry.

https://web.stanford.edu/group/dlab/media/papers/Sohal%20Nature%202009.pdf

Parvalbumin neurons and gamma rhythms enhance cortical circuit performance

Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher-order interactions among large groups of elements have an important role. Here we show, in the vertebrate retina, that weak correlations between pairs of neurons coexist with strongly collective behaviour in the responses of ten or more neurons. We find that this collective behaviour is described quantitatively by models that capture the observed pairwise correlations but assume no higher-order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behaviour. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.

/pdf/weak-pairwise-correlations-imply-strongly-correlated-network-321qdg5dxz.pdf

Weak pairwise correlations imply strongly correlated network states in a neural population.

There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation.

/pdf/normalization-as-a-canonical-neural-computation-3uvgzn1unc.pdf

Normalization as a canonical neural computation.

https://www.cns.nyu.edu/csh/csh06/PDFs/Baccus2002.pdf

Fast and Slow Contrast Adaptation in Retinal Circuitry

An important task in vision is to detect objects moving within a stationary scene. During normal viewing this is complicated by the presence of eye movements that continually scan the image across the retina, even during fixation. To detect moving objects, the brain must distinguish local motion within the scene from the global retinal image drift due to fixational eye movements. We have found that this process begins in the retina: a subset of retinal ganglion cells responds to motion in the receptive field centre, but only if the wider surround moves with a different trajectory. This selectivity for differential motion is independent of direction, and can be explained by a model of retinal circuitry that invokes pooling over nonlinear interneurons. The suppression by global image motion is probably mediated by polyaxonal, wide-field amacrine cells with transient responses. We show how a population of ganglion cells selective for differential motion can rapidly flag moving objects, and even segregate multiple moving objects.

Segregation of object and background motion in the retina

Retinal ganglion cells convey the visual image from the eye to the brain. They generally encode local differences in space and changes in time rather than the raw image intensity. This can be seen as a strategy of predictive coding, adapted through evolution to the average image statistics of the natural environment. Yet animals encounter many environments with visual statistics different from the average scene. Here we show that when this happens, the retina adjusts its processing dynamically. The spatio-temporal receptive fields of retinal ganglion cells change after a few seconds in a new environment. The changes are adaptive, in that the new receptive field improves predictive coding under the new image statistics. We show that a network model with plastic synapses can account for the large variety of observed adaptations.

/pdf/dynamic-predictive-coding-by-the-retina-39ekdlxe1b.pdf

Dynamic predictive coding by the retina.

It has been demonstrated that electrical stimulation of the retina can produce visual percepts in blind patients suffering from macular degeneration and retinitis pigmentosa. However, current retinal implants provide very low resolution (just a few electrodes), whereas at least several thousand pixels would be required for functional restoration of sight. This paper presents the design of an optoelectronic retinal prosthetic system with a stimulating pixel density of up to 2500 pix mm−2 (corresponding geometrically to a maximum visual acuity of 20/80). Requirements on proximity of neural cells to the stimulation electrodes are described as a function of the desired resolution. Two basic geometries of sub-retinal implants providing required proximity are presented: perforated membranes and protruding electrode arrays. To provide for natural eye scanning of the scene, rather than scanning with a head-mounted camera, the system operates similar to 'virtual reality' devices. An image from a video camera is projected by a goggle-mounted collimated infrared LED-LCD display onto the retina, activating an array of powered photodiodes in the retinal implant. The goggles are transparent to visible light, thus allowing for the simultaneous use of remaining natural vision along with prosthetic stimulation. Optical delivery of visual information to the implant allows for real-time image processing adjustable to retinal architecture, as well as flexible control of image processing algorithms and stimulation parameters.

https://web.stanford.edu/~palanker/publications/prosthesis%20design.pdf

Design of a high-resolution optoelectronic retinal prosthesis.

It has been demonstrated that electrical stimulation of the retina can produce visual percepts in blind patients suffering from macular degeneration and retinitis pigmentosa. However, current retinal implants provide very low resolution (just a few electrodes), whereas at least several thousand pixels would be required for functional restoration of sight. This paper presents the design of an optoelectronic retinal prosthetic system with a stimulating pixel density of up to 2500 pix mm(-2) (corresponding geometrically to a maximum visual acuity of 20/80). Requirements on proximity of neural cells to the stimulation electrodes are described as a function of the desired resolution. Two basic geometries of sub-retinal implants providing required proximity are presented: perforated membranes and protruding electrode arrays. To provide for natural eye scanning of the scene, rather than scanning with a head-mounted camera, the system operates similar to 'virtual reality' devices. An image from a video camera is projected by a goggle-mounted collimated infrared LED-LCD display onto the retina, activating an array of powered photodiodes in the retinal implant. The goggles are transparent to visible light, thus allowing for the simultaneous use of remaining natural vision along with prosthetic stimulation. Optical delivery of visual information to the implant allows for real-time image processing adjustable to retinal architecture, as well as flexible control of image processing algorithms and stimulation parameters.

Stephen A. Baccus

Papers

Fast and Slow Contrast Adaptation in Retinal Circuitry

Segregation of object and background motion in the retina

Dynamic predictive coding by the retina.

Design of a high-resolution optoelectronic retinal prosthesis.

Design of a High–Resolution Optoelectronic Retinal Prosthesis