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

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

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Building machines that learn and think like people.

By utilizing new information from both clinical and experimental (lesion, electrophysiological, and gene-activation) studies with animals, the anatomy underlying anterograde amnesia has been reformulated. The distinction between temporal lobe and diencephalic amnesia is of limited value in that a common feature of anterograde amnesia is damage to part of an “extended hippocampal system” comprising the hippocampus, the fornix, the mamillary bodies, and the anterior thalamic nuclei. This view, which can be traced back to Delay and Brion (1969), differs from other recent models in placing critical importance on the efferents from the hippocampus via the fornix to the diencephalon. These are necessary for the encoding and, hence, the effective subsequent recall of episodic memory. An additional feature of this hippocampal–anterior thalamic axis is the presence of projections back from the diencephalon to the temporal cortex and hippocampus that also support episodic memory. In contrast, this hippocampal system is not required for tests of item recognition that primarily tax familiarity judgements. Familiarity judgements reflect an independent process that depends on a distinct system involving the perirhinal cortex of the temporal lobe and the medial dorsal nucleus of the thalamus. In the large majority of amnesic cases both the hippocampal–anterior thalamic and the perirhinal–medial dorsal thalamic systems are compromised, leading to severe deficits in both recall and recognition.

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Episodic memory, amnesia, and the hippocampal–anterior thalamic axis.

Our understanding of the cellular implementation of systems-level neural processes like action, thought and emotion has been limited by the availability of tools to interrogate specific classes of neural cells within intact, living brain tissue. Here we identify and develop an archaeal light-driven chloride pump (NpHR) from Natronomonas pharaonis for temporally precise optical inhibition of neural activity. NpHR allows either knockout of single action potentials, or sustained blockade of spiking. NpHR is compatible with ChR2, the previous optical excitation technology we have described, in that the two opposing probes operate at similar light powers but with well-separated action spectra. NpHR, like ChR2, functions in mammals without exogenous cofactors, and the two probes can be integrated with calcium imaging in mammalian brain tissue for bidirectional optical modulation and readout of neural activity. Likewise, NpHR and ChR2 can be targeted together to Caenorhabditis elegans muscle and cholinergic motor neurons to control locomotion bidirectionally. NpHR and ChR2 form a complete system for multimodal, high-speed, genetically targeted, all-optical interrogation of living neural circuits.

Multimodal fast optical interrogation of neural circuitry

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Role of WHO.

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron’s axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning. Multi-layered neural architectures that implement learning require elaborate mechanisms for symmetric backpropagation of errors that are biologically implausible. Here the authors propose a simple resolution to this problem of blame assignment that works even with feedback using random synaptic weights.

Random synaptic feedback weights support error backpropagation for deep learning.

During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses are embedded within multilayered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. The backpropagation algorithm solves this problem in deep artificial neural networks, but historically it has been viewed as biologically problematic. Nonetheless, recent developments in neuroscience and the successes of artificial neural networks have reinvigorated interest in whether backpropagation offers insights for understanding learning in the cortex. The backpropagation algorithm learns quickly by computing synaptic updates using feedback connections to deliver error signals. Although feedback connections are ubiquitous in the cortex, it is difficult to see how they could deliver the error signals required by strict formulations of backpropagation. Here we build on past and recent developments to argue that feedback connections may instead induce neural activities whose differences can be used to locally approximate these signals and hence drive effective learning in deep networks in the brain.

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Backpropagation and the brain

Of 11 genes involved in nonspecific X-linked mental retardation (MRX), three encode regulators or effectors of the Rho GTPases, suggesting an important role for Rho signaling in cognitive function. It remains unknown, however, how mutations in Rho-linked genes lead to MRX. Here we report that oligophrenin-1, a Rho-GTPase activating protein that is absent in a family affected with MRX, is required for dendritic spine morphogenesis. Using RNA interference and antisense RNA approaches, we show that knock-down of oligophrenin-1 levels in CA1 neurons in rat hippocampal slices significantly decreases spine length. This phenotype can be recapitulated using an activated form of RhoA and rescued by inhibiting Rho-kinase, indicating that reduced oligophrenin-1 levels affect spine length by increasing RhoA and Rho-kinase activities. We further demonstrate an interaction between oligophrenin-1 and the postsynaptic adaptor protein Homer. Our findings provide the first insight into how mutations in a Rho-linked MRX gene may compromise neuronal function.

The X-linked mental retardation protein oligophrenin-1 is required for dendritic spine morphogenesis.

To determine how patterned visual activity regulates the development of axonal projections, we collected in vivo time-lapse images of retinal axons from albino Xenopus tadpoles in which binocular innervation of the optic tectum was induced. Axons added branch tips with nearly equal probability in all territories, but eliminated them preferentially from territory dominated by the opposite eye. This selective branch elimination was abolished by blockade of N-methyl-D-aspartate receptors. These results describe a correlation-based mechanism by which visual experience directly governs axon branch dynamics that contribute to the development of topographic maps.

https://science.sciencemag.org/content/sci/301/5629/66.full.pdf

Control of axon branch dynamics by correlated activity in vivo.

In this study, the authors show that optogenetic silencing of neurons with a commonly used light-driven chloride pump can result in alterations in GABAergic synaptic transmission that persist after the stimulation period and that may result in changes to neuronal excitability.

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Colin J. Akerman

Papers

Random synaptic feedback weights support error backpropagation for deep learning.

Backpropagation and the brain

The X-linked mental retardation protein oligophrenin-1 is required for dendritic spine morphogenesis.

Control of axon branch dynamics by correlated activity in vivo.

Optogenetic silencing strategies differ in their effects on inhibitory synaptic transmission.