轻度认知损伤患者的联系记忆损伤特征

遗忘型轻度认知损伤患者(aMCI)在项目记忆和联系记忆上都有损伤．本文通过临床记忆量表中的项目记忆和联系记忆测验,研究aMCI的联系记忆是否比项目记忆有更显著的损伤．另外,通过分析配对联想学习测验,进一步研究aMCI联系记忆损伤的特点．25名aMCI和28名健康老人参与了两个联系记忆测验(配对联想学习测验和联想回忆测验)和两个项目记忆测验(图像自由回忆和无意义图形再认),aMCI患者在联系记忆测验上表现出了更显著的损伤,即使控制了项目记忆的损伤,aMCI的联系记忆仍然比健康老人显著降低．另外,ROC分析表明联系记忆测验比项目记忆测验对aMCI病人有更高的区分度．对配对联想学习测验的分析表明,相对于健康老人,aMCI患者在记忆有语言联系的词对要比记忆无语义联系的词对更为困难．本研究进一步表明aMCI患者的联系记忆比项目记忆有更大的损伤．相对于健康老人,aMCI患者不仅难以在两个无关项目间创建记忆连接,而且在有效利用项目间本身的语义联系方面存在更大的损伤．联系记忆测验比项目记忆测验对aMCI患者有更高的区分度．在神经心理评估中增加联系记忆测验,能更加有效地识别aMCI患者．     

Associative memory design using overlapping decomposition and generalized brain-state-in-a-box neural networks

This paper is concerned with large scale associative memory design. A serious problem with neural associative memories is the quadratic growth of the number of interconnections with the problem size. An overlapping decomposition algorithm is proposed to attack this problem. Specifically, a pattern to be processed is decomposed into overlapping sub-patterns. Then, neural sub-networks are constructed that process the sub-patterns. An error correction algorithm operates on the outputs of each sub-network in order to correct the mismatches between sub-patterns that are obtained from the independent recall processes of individual sub-networks. The performance of the proposed large scale associative memory is illustrated using two-dimensional images. It is shown that the proposed method reduces the computing cost of the design of the associative memories compared with non-interconnected associative memories.     

Efficient and robust associative memory from a generalized Bloom filter

We develop a variant of a Bloom filter that is robust to hardware failure and show how it can be used as an efficient associative memory. We define a measure of the information recall and show that our new associative memory is able to recall more than twice as much information as a Hopfield network. The extra efficiency of our associative memory is all the more remarkable as it uses only bits while the Hopfield network uses integers.     

基于性诱和深度学习的草地贪夜蛾成虫自动识别计数方法

【目的】探究深度学习在草地贪夜蛾Spodoptera frugiperda成虫自动识别计数上的可行性,并评估模型的识别计数准确率,为害虫机器智能监测提供图像识别与计数方法。【方法】设计一种基于性诱的害虫图像监测装置,定时自动采集诱捕到的草地贪夜蛾成虫图像,结合采集船形诱捕器粘虫板上草地贪夜蛾成虫图像,构建数据集;应用YOLOv5深度学习目标检测模型进行特征学习,通过草地贪夜蛾原始图像、清除边缘残缺目标、增加相似检测目标(斜纹夜蛾成虫)、无检测目标负样本等不同处理的数据集进行模型训练,得到Yolov5s-A1, Yolov5s-A2, Yolov5s-AB, Yolov5s-ABC 4个模型,对比在不同遮挡程度梯度下的测试样本不同模型检测结果,用准确率(P)、召回率(R)、F1值、平均准确率(average precision, AP)和计数准确率(counting accuracy, CA)评估各模型的差异。【结果】通过原始图像集训练的模型Yolov5s-A1的识别准确率为87.37%,召回率为90.24%,F1值为88.78;清除边缘残缺目标图像集训练得到的模型Yolov5s-A2的识别准确率为93.15%,召回率为84.77%,F1值为88.76;增加斜纹夜蛾成虫样本图像训练的模型Yolov5s-AB的识别准确率为96.23%,召回率为91.85%,F1值为93.99;增加斜纹夜蛾成虫和无检测对象负样本训练的模型Yolov5s-ABC的识别准确率为94.76%,召回率为88.23%,F1值为91.38。4个模型的AP值从高到低排列如下：Yolov5s-AB>Yolov5s-ABC> Yolov5s-A2>Yolov5s-A1,其中Yolov5s-AB与Yolov5s-ABC结果相近;CA值从高到低排列如下：Yolov5s-AB>Yolov5s-ABC>Yolov5s-A2>Yolov5s-A1。【结论】结果表明本文提出的方法应用于控制条件下害虫图像监测设备及诱捕器粘虫板上草地贪夜蛾成虫的识别计数是可行的,深度学习技术对于草地贪夜蛾成虫的识别和计数是有效的。基于深度学习的草地贪夜蛾成虫自动识别与计数方法对虫体姿态变化、杂物干扰等有较好的鲁棒性,可从各种虫体姿态及破损虫体中自动统计出草地贪夜蛾成虫的数量,在害虫种群监测中具有广阔的应用前景。     

Neuronal dynamics under periodic stimuli

The convergence characteristics of a single dissipative Hopfield-type neuron with self-interaction under periodic external stimuli are considered. Sufficient conditions are established for associative encoding and recall of the periodic patterns associated with the external stimuli. Both continuous-time-continuous-state and discrete-time-continuous-state models are discussed. It is shown that when the neuronal gain is dominated by the neuronal dissipation on average, associative recall of the encoded temporal pattern is guaranteed and this is achieved by the global asymptotic stability of the encoded pattern.     

基于帧间光流特征和改进RNN的草鱼摄食状态分类

针对鱼类连续摄食行为较难识别与量化的问题, 提出一种基于帧间光流特征和改进递归神经网络(Recurrent neural network, RNN)的草鱼摄食状态分类方法。首先利用偏振相机搭建户外池塘采样系统, 采集不同偏振角度水面图像, 并基于图像饱和度和亮度模型自动选择低反光角度图像, 构建图像样本库; 其次通过光流法提取帧间运动特征, 并基于投饲机开关状态构建时间序列帧间特征样本集, 然后利用样本集训练改进RNN分类网络。以上海市崇明区瑞钵水产养殖专业合作社的试验数据对该方法进行验证。结果表明, 研究方法综合准确率为91%, 召回率为92.2%, 均优于传统的鱼类摄食行为识别方法。研究结果可为鱼类精准投喂技术研究提供参考。     

Associative sequential recall in a synaptic matrix

An adaptive neuronal network (synaptic matrix) was simulated. After learning a number of randomly generated scenes, the network was tested for associative sequential recall in a stimulus-bound mode and in an image-bound mode. It is demonstrated that neuronal mechanisms of this kind can exhibit orderliness or looseness of associative response suggestive of human recall behavior.     

Monkeys recall and reproduce simple shapes from memory

If you draw from memory a picture of the front of your childhood home, you will have demonstrated recall. You could also recognize this house upon seeing it. Unlike recognition, recall demonstrates memory for things that are not present. Recall is necessary for planning and imagining, and it can increase the flexibility of navigation, social behavior, and other cognitive skills. Without recall, memory is more limited to recognition of the immediate environment. Amnesic patients are impaired on recall tests [1, 2], and recall performance often declines with aging [3]. Despite its importance, we know relatively little about nonhuman animals' ability to recall information; we lack suitable recall tests for them and depend instead on recognition tests to measure nonhuman memory. Here we report that rhesus monkeys can recall simple shapes from memory and reproduce them on a touchscreen. As in humans [4, 5], monkeys remembered less in recall than recognition tests, and their recall performance deteriorated more slowly. Transfer tests showed that monkeys used a flexible memory mechanism rather than memorizing specific actions for each shape. Observation of recall in Old World monkeys suggests that it has been adaptive for over 30 million years [6] and does not depend on language.     

Perirhinal cortex supports encoding and familiarity-based recognition of novel associations

Results from imaging and lesion studies of item recognition memory have suggested that the hippocampus supports memory for the arbitrary associations that form the basis of episodic recollection, whereas the perirhinal cortex (PRc) supports familiarity for individual items. This view has been challenged, however, by findings showing that PRc may contribute to associative recognition, a task thought to measure relational or recollective memory. Here, using functional magnetic resonance imaging, we demonstrate that PRc activity is increased when pairs of items are processed as a single configuration or unit and that this activity predicts subsequent familiarity-based associative memory. These results explain the discrepancy in the literature by showing that novel associations can be encoded in a unitized manner, thereby allowing PRc to support associative recognition based on familiarity.     

The characteristics of perceptive learning and memory in psychopathological states]

Dynamics is studied of visual images recognition in patients with schizophrenia under the influence of small doses of ionizing radiation used with therapeutic purpose. Functional state of the brain is evaluated by amplitude-temporal parameters of visual and auditory EPs. After conducted therapy no changes in responses to light flashes are found, and in auditory EPs in a part (the first group) of patients there is an increase of the component N1 amplitude in the frontal area. The same group is characterized by lower parameters of visual images recognition. Conclusion is made about the presence of non-specific changes of visual gnosis in patients with schizophrenia and about involvement of the associative frontal structures in pathologic process. Possible mechanisms of disturbance of the perceptive learning and memory processes in schizophrenia are discussed.     

Modelling studies on the computational function of fast temporal structure in cortical circuit activity

The interplay between modelling and experimental studies can support the exploration of the function of neuronal circuits in the cortex. We exemplify such an approach with a study on the role of spike timing and gamma-oscillations in associative memory in strongly connected circuits of cortical neurones. It is demonstrated how associative memory studies on different levels of abstraction can specify the functionality to be expected in real cortical neuronal circuits. In our model overlapping random configurations of sparse cell populations correspond to memory items that are stored by simple Hebbian coincidence learning. This associative memory task will be implemented with biophysically well tested compartmental neurones developed by Pinsky and Rinzel . We ran simulation experiments to study memory recall in two network architectures: one interconnected pool of cells, and two reciprocally connected pools. When recalling a memory by stimulating a spatially overlapping set of cells, the completed pattern is coded by an event of synchronized single spikes occurring after 25-60 ms. These fast associations are performed even at a memory load corresponding to the memory capacity of optimally tuned formal associative networks (>0.1 bit/synapse). With tonic stimulation or feedback loops in the network the neurones fire periodically in the gamma-frequency range (20-80 Hz). With fast changing inputs memory recall can be switched between items within a single gamma cycle. Thus, oscillation is not a primary coding feature necessary for associative memory. However, it accompanies reverberatory feedback providing an improved iterative memory recall completed after a few gamma cycles (60-260 ms). In the bidirectional architecture reverberations do not express in a rigid phase locking between the pools. For small stimulation sets bursting occurred in these cells acting as a supportive mechanism for associative memory.     

Age-Related Decline in Associative Learning in Healthy Chinese Adults

Paired associates learning (PAL) has been widely used in aging-related research, suggesting an age-related decline in associative learning. However, there are several cognitive processes (attention, spatial and recognition memory, strategy, and associative learning) involved in PAL. It is unclear which component contributes to the decline in PAL performance associated with age effects. The present study determines whether age effects on associative learning are independent of other cognitive processes involved in PAL. Using a validated computerized cognitive program (CANTAB), we examined cognitive performance of associative learning, spatial and recognition memory, attention and strategy use in 184 Singaporean Chinese adults aged from 21 to 80 years old. Linear regression revealed significant age-related decline in associative learning, spatial and recognition memory, and the level of strategy use. This age-related decline in associative learning remains even after adjusting for attention, spatial and recognition memory, and strategy use. These results show that age effects on associative learning are independent of other cognitive processes involved in PAL.     

Physical models of neural networks

The state of art in computer modelling of neural networks with associative memory is reviewed. The available experimental data are considered on learning and memory of small neural systems, on isolated synapses and on molecular level. Computer simulations demonstrate that realistic models of neural ensembles exhibit properties which can be interpreted as image recognition, categorization, learning, prototype forming, etc. A bilayer model of associative neural network is proposed. One layer corresponds to the short-term memory, the other one to the long-term memory. Patterns are stored in terms of the synaptic strength matrix. We have studied the relaxational dynamics of neurons firing and suppression within the short-term memory layer under the influence of the long-term memory layer. The interaction among the layers has found to create a number of novel stable states which are not the learning patterns. These synthetic patterns may consist of elements belonging to different non-intersecting learning patterns. Within the framework of a hypothesis of selective and definite coding of images in brain one can interpret the observed effect as the "idea? generating" process.     

A cross correlation analysis of the electrical activity of different regions of the cerebral cortex during visual pattern recognition in children]

Characteristics of spatial-temporal organization of brain biopotentials were examined in one to 2.5 year old children during recognition of visual images. Crosscorrelation EEG analysis of frontal, motor, inferior parietal, temporal and occipital cortical zones has shown that recognition of familiar visual objects is accompanied by an increase in spatial synchronization of biopotentials, especially in the inferior parietal zones of both hemispheres and occipital centres of the left hemisphere. There is a considerable increase in the number of highly synchronous synphasic oscillations at the 4--5 per sec frequency with an intensified periodicity of processes. Recognition of unfamiliar objects does not produce a similar effect. Temporal organization of biopotentials of the associative (frontal and inferior parietal) and projection visual areas in the course of recognition of images depends on the existence of a notion of the whole object in the child's memory.     

Recognition of masked significant and insignificant images of household items

Psychophysiological experiments were carried out to study the probability and latency of the recognition of significant and insignificant stimuli, namely, partially masked images of household items during their tachystoscopic presentation to subjects with normal vision. Data on a group of 16 subjects showed that the probability of recognition of significant stimuli was substantially higher as compared to the recognition of insignificant stimuli. The recognition latencies for significant stimuli were significantly higher as compared to insignificant stimuli. These effects were found using analysis of the distribution of the respective values and ANOVA. We did not find any gender effects. We suppose that these differences were related to a longer and more detailed recall and comparison of significant stimuli with other images of the set used, as well as to the effect of selective visual attention and interaction between the recognitions of significant and insignificant stimuli on the final result of identification of images.     

Pattern recognition in neural networks with competing dynamics: coexistence of fixed-point and cyclic attractors

We study the properties of the dynamical phase transition occurring in neural network models in which a competition between associative memory and sequential pattern recognition exists. This competition occurs through a weighted mixture of the symmetric and asymmetric parts of the synaptic matrix. Through a generating functional formalism, we determine the structure of the parameter space at non-zero temperature and near saturation (i.e., when the number of stored patterns scales with the size of the network), identifying the regions of high and weak pattern correlations, the spin-glass solutions, and the order-disorder transition between these regions. This analysis reveals that, when associative memory is dominant, smooth transitions appear between high correlated regions and spurious states. In contrast when sequential pattern recognition is stronger than associative memory, the transitions are always discontinuous. Additionally, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of the same set of patterns, there is a discontinuous transition between associative memory and sequential pattern recognition. In contrast, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of independent sets of patterns, the network is able to perform both associative memory and sequential pattern recognition for a wide range of parameter values.     

Associative neural network model for the generation of temporal patterns. Theory and application to central pattern generators.

Cyclic patterns of motor neuron activity are involved in the production of many rhythmic movements, such as walking, swimming, and scratching. These movements are controlled by neural circuits referred to as central pattern generators (CPGs). Some of these circuits function in the absence of both internal pacemakers and external feedback. We describe an associative neural network model whose dynamic behavior is similar to that of CPGs. The theory predicts the strength of all possible connections between pairs of neurons on the basis of the outputs of the CPG. It also allows the mean operating levels of the neurons to be deduced from the measured synaptic strengths between the pairs of neurons. We apply our theory to the CPG controlling escape swimming in the mollusk Tritonia diomedea. The basic rhythmic behavior is shown to be consistent with a simplified model that approximates neurons as threshold units and slow synaptic responses as elementary time delays. The model we describe may have relevance to other fixed action behaviors, as well as to the learning, recall, and recognition of temporally ordered information.     

Balanced dopamine is critical for pattern completion during associative memory recall

Pattern completion, the ability to retrieve complete memories initiated by partial cues, is a critical feature of the memory process. However, little is known regarding the molecular and cellular mechanisms underlying this process. To study the role of dopamine in memory recall, we have analyzed dopamine transporter heterozygous knockout mice (DAT(+/-)), and found that while these mice possess normal learning, consolidation, and memory recall under full cue conditions, they exhibit specific deficits in pattern completion under partial cue condition. This form of memory recall deficit in the dopamine transporter heterozygous knockout mice can be reversed by a low dose of the dopamine antagonist haloperidol, further confirming that the inability to retrieve memory patterns is a result of dopamine imbalance. Therefore, our results reveal that a delicate control of the brain's dopamine level is critical for pattern completion during associative memory recall.     

Associative learning in a network model of Hermissenda crassicornis

A companion paper in a previous issue of this journal presented a resistance-capacitance circuit computer model of the four-neuron visual-vestibular network of the invertebrate marine mollusk Hermissenda crassicornis. In the present paper, we demonstrate that changes in the model's output in response to simulated associative training is quantitatively similar to behavioral and electrophysiological changes in response to associative training of Hermissenda crassicornis. Specifically, the model demonstrates many characteristics of conditioning: sensitivity to stimulus contingency, stimulus specificity, extinction, and savings. The model's learning features also are shown to be devoid of non-associative components. Thus, this computational model is an excellent tool for examining the information flow and dynamics of biological associative learning and for uncovering insights concerning associative learning, memory, and recall that can be applied to the development of artificial neural networks.     

Neural Population-Level Memory Traces in the Mouse Hippocampus

One of the fundamental goals in neurosciences is to elucidate the formation and retrieval of brain''s associative memory traces in real-time. Here, we describe real-time neural ensemble transient dynamics in the mouse hippocampal CA1 region and demonstrate their relationships with behavioral performances during both learning and recall. We employed the classic trace fear conditioning paradigm involving a neutral tone followed by a mild foot-shock 20 seconds later. Our large-scale recording and decoding methods revealed that conditioned tone responses and tone-shock association patterns were not present in CA1 during the first pairing, but emerged quickly after multiple pairings. These encoding patterns showed increased immediate-replay, correlating tightly with increased immediate-freezing during learning. Moreover, during contextual recall, these patterns reappeared in tandem six-to-fourteen times per minute, again correlating tightly with behavioral recall. Upon traced tone recall, while various fear memories were retrieved, the shock traces exhibited a unique recall-peak around the 20-second trace interval, further signifying the memory of time for the expected shock. Therefore, our study has revealed various real-time associative memory traces during learning and recall in CA1, and demonstrates that real-time memory traces can be decoded on a moment-to-moment basis over any single trial.