Information Processing In Networks Research Articles

DNA-empowered synthetic cells as minimalistic life forms.

Cells, the fundamental units of life, orchestrate intricate functions - motility, adaptation, replication, communication, and self-organization within tissues. Originating from spatiotemporally organized structures and machinery, coupled with information processing in signalling networks, cells embody the 'sensor-processor-actuator' paradigm. Can we glean insights from these processes to construct primitive artificial systems with life-like properties? Using de novo design approaches, what can we uncover about the evolutionary path of life? This Review discusses the strides made in crafting synthetic cells, utilizing the powerful toolbox of structural and dynamic DNA nanoscience. We describe how DNA can serve as a versatile tool for engineering entire synthetic cells or subcellular entities, and how DNA enables complex behaviour, including motility and information processing for adaptive and interactive processes. We chart future directions for DNA-empowered synthetic cells, envisioning interactive systems wherein synthetic cells communicate within communities and with living cells.

Nonreciprocal Unconventional Photon Blockade with Kerr Magnons

AbstractNonreciprocal devices, allowing to manipulate one‐way signals, are crucial to quantum information processing and quantum networks. Here a nonlinear cavity‐magnon system is proposed, consisting of a microwave cavity coupled to one or two yttrium–iron–garnet (YIG) spheres supporting magnons with Kerr nonlinearity, to investigate nonreciprocal unconventional photon blockade. The nonreciprocity originates from the direction‐dependent Kerr effect, distinctly different from previous proposals with spinning cavities and dissipative couplings. For a single sphere case, nonreciprocal unconventional photon blockade can be realized by manipulating the nonreciprocal destructive interference between two active paths, via varying the Kerr coefficient from positive to negative, or vice versa. By optimizing the system parameters, the perfect and well‐tuned nonreciprocal unconventional photon blockade can be predicted. For the case of two spheres with opposite Kerr effects, only reciprocal unconventional photon blockade can be observed when two cavity‐magnon coupling strengths Kerr strengths are symmetric. However, when coupling strengths or Kerr strengths become asymmetric, nonreciprocal unconventional photon blockade appears. This implies that two‐sphere nonlinear cavity‐magnon systems can be used to switch the transition between reciprocal and nonreciprocal unconventional photon blockades. This study offers a potential platform for investigating the nonreciprocal photon blockade effect in nonlinear cavity magnonics.

A Comparison of Madaline and Perceptron Algorithms on Classification with Quantum Computing Approach

The fundamental problem in this research is to explore a more profound understanding regarding both performance and efficiency in quantity computing. Successful implementation of algorithms in computational computing environments can unlock the potential for significant improvements in information processing and neural network modelling. This research focuses on developing the Madaline and Perceptron algorithm using a quantum approach. This study compares the two algorithms regarding the accuracy and epoch of the test results. The dataset used in this study is using a lens dataset. There are four attributes: 1) patient age: young, pre-presbyopia, presbyopia 2) eyeglass prescription: myopia, hypermetropia 3) astigmatic: no, yes. 4) tear production rate: reduced, normal. There are three classes: 1) patients must have hard contact lenses installed, 2) patients must have soft contact lenses installed, and 3) patients cannot have contact lenses installed. The number of data is 24 data. The result of this research is the development of the Madaline and Perceptron algorithms with a quantum computing approach. Comparing these algorithms shows the best accuracy is the Perceptron algorithm, namely 100%. In comparison, Madaline is 62.5%, and the smallest epoch is the Madaline algorithm, namely 4 epochs, while the smallest Perceptron epoch is 317. This research significantly contributes to developing computing and neural networks, with potential applications extending from data processing to more accurate modelling in artificial intelligence, data analysis and understanding complex patterns.

Cell-type specific inhibitory plasticity in subicular pyramidal cells.

The balance between excitation and inhibition is essential to the proper function of cortical circuits. To maintain this balance during dynamic network activity, modulation of the strength of inhibitory synapses is a central requirement. In this study, we aimed to characterize perisomatic inhibition and its plasticity onto pyramidal cells (PCs) in the subiculum, the main output region of the hippocampus. We performed whole-cell patch-clamp recordings from the two main functional PC types, burst (BS) and regular spiking (RS) neurons in acute rat hippocampal slices and applied two different extracellular high-frequency stimulation paradigms: non-associative (presynaptic stimulation only) and associative stimulation (concurrent pre-and postsynaptic stimulation) to induce plasticity. Our results revealed cell type-specific differences in the expression of inhibitory plasticity depending on the induction paradigm: While associative stimulation caused robust inhibitory plasticity in both cell types, non-associative stimulation produced long-term potentiation in RS, but not in BS PCs. Analysis of paired-pulse ratio, variance of IPSPs, and postsynaptic Ca2+ buffering indicated a dominant postsynaptic calcium-dependent signaling and expression of inhibitory plasticity in both PC types. This divergence in inhibitory plasticity complements a stronger inhibition and a higher intrinsic excitability in RS as compared to BS neurons, suggesting differential involvement of the two PC types during network activation and information processing in the subiculum.

Complex chemical reaction networks for future information processing.

Tackling the increasing energy demand of our society is one of the key challenges today. With the rise of artificial intelligence, information and communication technologies started to substantially contribute to this alarming trend and therefore necessitate more sustainable approaches for the future. Brain-inspired computing paradigms represent a radically new and potentially more energy-efficient approach for computing that may complement or even replace CMOS in the long term. In this perspective, we elaborate on the concepts and properties of complex chemical reaction networks (CRNs) that may serve as information-processing units based on chemical reactions. The computational capabilities of simpler, oscillatory chemical reactions have already been demonstrated in scenarios ranging from the emulation of Boolean gates to image-processing tasks. CRNs offer higher complexity and larger non-linearity, potentially at lower energy consumption. Key challenges for the successful development of CRN-based computers are associated with their specific physical implementations, operability, and readout modalities. CRNs are sensible to various reaction triggers, and provide multiple and interlinked reaction pathways and a diverse compound space. This bears a high potential to build radically new hardware and software concepts for energy-efficient computing based on neuromorphic architectures-with computing capabilities in real-world applications yet to be demonstrated.

The effects of chronic morphine administration on spatial memory and microtubule dynamicity in male mice's brain

The examination of the influence of morphine on behavioral processes, specifically learning and memory, holds significant importance. Additionally, microtubule proteins play a pivotal role in cellular functions, and the dynamics of microtubules contribute to neural network connectivity, information processing, and memory storage. however, the molecular mechanism of morphine on microtubule dynamics, learning, and memory remains uncovered. In the present study, we examined the effects of chronic morphine administration on memory formation impairment and the kinetic alterations in microtubule proteins induced by morphine in mice. Chronic morphine administration at doses of 5 and 10 mg/kg dose-dependently decreased subjects' performance in spatial memory tasks, such as the Morris Water Maze and Y-maze spontaneous alternation behavior. Furthermore, morphine was found to stabilize microtubule structure, and increase polymerization, and total polymer mass. However, it simultaneously impaired microtubule dynamicity, stemming from structural changes in tubulin dimer structure. These findings emphasize the need for careful consideration of different doses when using morphine, urging a more cautious approach in the administration of this opioid medication.

Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification.

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.

Coincidence detection and integration behavior in spiking neural networks

AbstractRecently, the interest in spiking neural networks (SNNs) remarkably increased, as up to now some key advances of biological neural networks are still out of reach. Thus, the energy efficiency and the ability to dynamically react and adapt to input stimuli as observed in biological neurons is still difficult to achieve. One neuron model commonly used in SNNs is the leaky-integrate-and-fire (LIF) neuron. LIF neurons already show interesting dynamics and can be run in two operation modes: coincidence detectors for low and integrators for high membrane decay times, respectively. However, the emergence of these modes in SNNs and the consequence on network performance and information processing ability is still elusive. In this study, we examine the effect of different decay times in SNNs trained with a surrogate-gradient-based approach. We propose two measures that allow to determine the operation mode of LIF neurons: the number of contributing input spikes and the effective integration interval. We show that coincidence detection is characterized by a low number of input spikes as well as short integration intervals, whereas integration behavior is related to many input spikes over long integration intervals. We find the two measures to linearly correlate via a correlation factor that depends on the decay time. Thus, the correlation factor as function of the decay time shows a powerlaw behavior, which could be an intrinsic property of LIF networks. We argue that our work could be a starting point to further explore the operation modes in SNNs to boost efficiency and biological plausibility.

MICDnet: Multimodal information processing networks for Crohn’s disease diagnosis

Crohn’s disease (CD) is a chronic inflammatory disease with increasing incidence worldwide and unclear etiology. Its clinical manifestations vary depending on location, extent, and severity of the lesions. In order to diagnose Crohn’s disease, medical professionals need to comprehensively analyze patients’ multimodal examination data, which includes medical imaging such as colonoscopy, pathological, and text information from clinical records. The processes of multimodal data analysis require collaboration among medical professionals from different departments, which wastes a lot of time and human resources. Therefore, a multimodal medical assisted diagnosis system for Crohn’s disease is particularly significant. Existing network frameworks find it hard to effectively capture multimodal patient data for diagnosis, and multimodal data for Crohn’s disease is currently lacking. In addition,a combination of data from patients with similar symptoms could serve as an effective reference for disease diagnosis. Thus, we propose a multimodal information diagnosis network (MICDnet) to learn CD feature representations by integrating colonoscopy, pathology images and clinical texts. Specifically, MICDnet first preprocesses each modality data, then uses encoders to extract image and text features separately. After that, multimodal feature fusion is performed. Finally, CD classification and diagnosis are conducted based on the fused features. Under the authorization, we build a dataset of 136 hospitalized inspectors, with colonoscopy images of seven areas, pathology images, and clinical record text for each individual. Training MICDnet on this dataset shows that multimodal diagnosis can improve the diagnostic accuracy of CD, and the diagnostic performance of MICDnet is superior to other models.

29 Smoking as a Risk Factor: Altered Brain Activity in Areas Associated with Preclinical Alzheimer's Disease

Objective:Those at genetic risk for Alzheimer's Disease (AD) because of the ApoE ε4 allele show differences in activation during olfactory information processing and memory in areas such as MTL structures, entorhinal cortex, posterior cingulate, precuneus, and inferior parietal lobule, suggesting preclinical AD neuropathology and olfactory impairment as a biomarker for predicting later AD onset (Murphy, 2019). The effects of smoking on AD have varied, with early studies suggesting either no effect or protective effects, and recent studies suggesting smoking as a risk factor for AD but with the need for further investigation in preclinical stages. Therefore, this study focused on olfaction and smoking as risk factors for preclinical AD neuropathology by studying differences in fMRI BOLD signal changes in smokers and nonsmokers during olfactory tasks.Participants and Methods:Archival data from 25 non-demented older adults recruited from the UCSD Alzheimer's Disease Research Center who completed an Assessment Scale-Cognitive Subscale (ADAS-Cog) and functional MRI scans at 3T, acquired during performance of an odor identification task. Odor Identification (OI) measured correct (hits) or incorrect (misses) identification of odors presented by an olfactometer to deliver the odor stimuli in short, controlled durations during fMRI scanning.Results:fMRI data were preprocessed using fMRIprep, smoothed at 4mm, scaled, and first level analyses were conducted using 3dDeconvolve in AFNI with time points corresponding to hits and misses as regressors. Differences between smokers and nonsmokers revealed smokers show a larger difference in BOLD signal change from hits minus misses at five significant clusters (p = 0.01 with the minimum cluster size [voxels] at 42). Peak areas of significant clusters included the right precuneus, right calcarine gyrus, left inferior parietal lobule, left superior parietal lobule, and left middle occipital gyrus. Analyses suggested a greater difference in activity between hits and misses in smokers compared to nonsmokers, with more activity during hits.Conclusions:Differences in activation between smokers and nonsmokers during an olfactory identification task, with greater activity in smokers during hits, suggests greater effort to correctly identify an odor. These findings of hyperactivation in areas (such as the precuneus and inferior parietal lobule) are similar to findings of hyperactivation during odor memory observed in studies of ε4 carriers during preclinical stages. Results provide further insight into smoking as a risk factor for AD. Moreover, results suggest the risk of smoking could potentially be reflected in altered activity in olfactory information processing networks in preclinical stages of AD. The study highlights the need for research to further understand the role smoking plays in the development of AD and the use of olfaction as a biomarker to aid in disease detection, prevention, and stage-associated treatments.

Heat Transfer Enhancement in Tree-Structured Polymer Linked Gold Nanoparticle Networks.

Human brains use a tree-like neuron network for information processing at high efficiency and low energy consumption. Tree-like structures have also been engineered to enhance mass and heat transfer in various applications. In this work, we reveal the heat transfer mechanism in tree-structured polymer linked gold nanoparticle (AuNP) networks using atomistic simulations. We report both upward and downward heat fluxes between root and leaf nodes in tree-structured polyethylene (PE) and poly(p-phenylene) (PPP) linked AuNP networks at tree levels from 1 to 5. We found that the heat conductance increases with an increasing polymer tree level. The heat transfer enhancement is due to the resulting increase in the low-frequency vibrational modes. This and other thermal properties are affected by the location of the AuNPs in the tree. Moreover, complex tree structures with at least five levels were found to be robust in the sense that disabling half of the leaves did not change the overall heat conductance.

Research on computer network information image data processing based on machine learning

Abstract In this paper, we aim to progress the efficiency and accuracy of information processing and security detection in computer networks by introducing convolutional neural networks in machine learning algorithms that are capable of multi-scaling from the channel attention module and spatial attention module in extracting image information. Global maximum pooling and global averaging are done for the feature maps generated by both modules to get the clearest feature maps by dimensionality reduction. The loss function is used to calculate the feature maps to reduce the data loss generated during data extraction and finally complete the image data processing. To verify the effectiveness of the proposed platform, network images containing different amounts of data are input into the platform, and the accuracy and loss of data extraction are obtained. The results show that the data extraction accuracy of the reduced platform is up to 100%, which is 6% higher than other platforms. The number of data losses in other platforms is more than twice of this paper, while the number of losses in this paper can be controlled within 5. It can be seen that convolutional neural network in machine learning improves the accuracy of data extraction from computer network information images and reduces the loss in data extraction.

High-dimensional time-frequency entanglement in a singly-filtered biphoton frequency comb

High-dimensional quantum entanglement is a cornerstone for advanced technology enabling large-scale noise-tolerant quantum systems, fault-tolerant quantum computing, and distributed quantum networks. The recently developed biphoton frequency comb (BFC) provides a powerful platform for high-dimensional quantum information processing in its spectral and temporal quantum modes. Here we propose and generate a singly-filtered high-dimensional BFC via spontaneous parametric down-conversion by spectrally shaping only the signal photons with a Fabry-Pérot cavity. High-dimensional energy-time entanglement is verified through Franson-interference recurrences and temporal correlation with low-jitter detectors. Frequency- and temporal- entanglement of our singly-filtered BFC is then quantified by Schmidt mode decomposition. Subsequently, we distribute the high-dimensional singly-filtered BFC state over a 10 km fiber link with a post-distribution time-bin dimension lower bounded to be at least 168. Our demonstrations of high-dimensional entanglement and entanglement distribution show the singly-filtered quantum frequency comb’s capability for high-efficiency quantum information processing and high-capacity quantum networks.

Mapping information flow between the inferotemporal and prefrontal cortices via neural oscillations in memory retrieval and maintenance

Interaction between the inferotemporal (ITC) and prefrontal (PFC) cortices is critical for retrieving information from memory and maintaining it in working memory. Neural oscillations provide a mechanism for communication between brain regions. However, it remains unknown how information flow via neural oscillations is functionally organized in these cortices during these processes. In this study, we apply Granger causality analysis to electrocorticographic signals from both cortices of monkeys performing visual association tasks to map information flow. Our results reveal regions within the ITC where information flow to and from the PFC increases via specific frequency oscillations to form clusters during memory retrieval and maintenance. Theta-band information flow in both directions increases in similar regions in both cortices, suggesting reciprocal information exchange in those regions. These findings suggest that specific subregions function as nodes in the memory information-processing network between the ITC and the PFC.

Extracting and visualizing hidden activations and computational graphs of PyTorch models with TorchLens

Deep neural network models (DNNs) are essential to modern AI and provide powerful models of information processing in biological neural networks. Researchers in both neuroscience and engineering are pursuing a better understanding of the internal representations and operations that undergird the successes and failures of DNNs. Neuroscientists additionally evaluate DNNs as models of brain computation by comparing their internal representations to those found in brains. It is therefore essential to have a method to easily and exhaustively extract and characterize the results of the internal operations of any DNN. Many models are implemented in PyTorch, the leading framework for building DNN models. Here we introduce TorchLens, a new open-source Python package for extracting and characterizing hidden-layer activations in PyTorch models. Uniquely among existing approaches to this problem, TorchLens has the following features: (1) it exhaustively extracts the results of all intermediate operations, not just those associated with PyTorch module objects, yielding a full record of every step in the model's computational graph, (2) it provides an intuitive visualization of the model's complete computational graph along with metadata about each computational step in a model's forward pass for further analysis, (3) it contains a built-in validation procedure to algorithmically verify the accuracy of all saved hidden-layer activations, and (4) the approach it uses can be automatically applied to any PyTorch model with no modifications, including models with conditional (if–then) logic in their forward pass, recurrent models, branching models where layer outputs are fed into multiple subsequent layers in parallel, and models with internally generated tensors (e.g., injections of noise). Furthermore, using TorchLens requires minimal additional code, making it easy to incorporate into existing pipelines for model development and analysis, and useful as a pedagogical aid when teaching deep learning concepts. We hope this contribution will help researchers in AI and neuroscience understand the internal representations of DNNs.

Exploring reservoir computing: Implementation via double stochastic nanowire networks

Neuromorphic computing, inspired by the human brain, uses memristor devices for complex tasks. Recent studies show that self-organizing random nanowires can implement neuromorphic information processing, enabling data analysis. This paper presents a model based on these nanowire networks, with an improved conductance variation profile. We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses. The nanowire network layer generates dynamic behaviors for pulse voltages, allowing time series prediction analysis. Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals, outperforming traditional reservoir computing in terms of fewer nodes, enriched dynamics and improved prediction accuracy. Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets, making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

Development of epistatic YES and AND protein logic gates and their assembly into signalling cascades.

The construction and assembly of artificial allosteric protein switches into information and energy processing networks connected to both biological and non-biological systems is a central goal of synthetic biology and bionanotechnology. However, designing protein switches with the desired input, output and performance parameters is challenging. Here we use a range of reporter proteins to demonstrate that their chimeras with duplicated receptor domains produce YES gate protein switches with large (up to 9,000-fold) dynamic ranges and fast (minutes) response rates. In such switches, the epistatic interactions between largely independent synthetic allosteric sites result in an OFF state with minimal background noise. We used YES gate protein switches based on β-lactamase to develop quantitative biosensors of therapeutic drugs and protein biomarkers. Furthermore, we demonstrated the reconfiguration of YES gate switches into AND gate switches controlled by two different inputs, and their assembly into signalling networks regulated at multiple nodes.

Dense 4D nanoscale reconstruction of living brain tissue.

Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure-function relationships of the brain's complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.

Electrically controlled nonreciprocity in a hybrid opto-electromechanical system

The nonreciprocity between two signal fields is regarded as a key function in future quantum networks and modern communication technologies. Here, we theoretically propose a scheme of nonreciprocal devices between signal fields in two different arbitrarily frequency domains in a hybrid cavity opto-electromechanical system. The model consists of a microwave cavity and an optical cavity, respectively, coupled with two different mechanical oscillators, which are coupled together by the tunable Coulomb interaction and driven by the external electrical fields. We study the nonreciprocal response between two different frequency fields. Nonreciprocal transmission is based on multichannel quantum interference to break time-reversal symmetry. The perfect nonreciprocity is shown in the certain conditions. By adjusting the Coulomb interaction, the phase differences, and strength of the electrically driven fields on the mechanical oscillators, we find that nonreciprocity can be modulated and even transformed into perfect nonreciprocity and reciprocity. These results provide a new insight into the design of nonreciprocal devices and present the potential applications in quantum information processing and quantum networks.

Caffeic acid recovers ischemia-induced synaptic dysfunction without direct effects on excitatory synaptic transmission and plasticity in mouse hippocampal slices

Caffeic acid is a polyphenolic compound present in a vast array of dietary components. We previously showed that caffeic acid reduces the burden of brain ischemia joining evidence by others that it can attenuate different brain diseases. However, it is unknown if caffeic acid affects information processing in neuronal networks. Thus, we now used electrophysiological recordings in mouse hippocampal slices to test if caffeic acid directly affected synaptic transmission, plasticity and dysfunction caused by oxygen-glucose deprivation (OGD), an in vitro ischemia model. Caffeic acid (1–10 μM) was devoid of effect on synaptic transmission and paired-pulse facilitation in Schaffer collaterals-CA1 pyramidal synapses. Also, the magnitude of either hippocampal long-term potentiation (LTP) or the subsequent depotentiation were not significantly modified by 10 μM caffeic acid. However, caffeic acid (10 μM) increased the recovery of synaptic transmission upon re-oxygenation following 7 min of OGD. Furthermore, caffeic acid (10 μM) also recovered plasticity after OGD, as heralded by the increased magnitude of LTP after exposure. These findings show that caffeic acid does not directly affect synaptic transmission and plasticity but can indirectly affect other cellular targets to correct synaptic dysfunction. Unraveling the molecular mechanisms of action of caffeic acid may allow the design of hitherto unrecognized novel neuroprotective strategies.