Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the “where and when”) and then allow for empirical testing of alternative network models of brain function that link information to behavior (the “how”). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach—dynamic activity flow modeling—then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory–motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

How is cognitive task behavior generated by brain network interactions? This study describes a novel network modeling approach and applies it to source electroencephalography data. The model accurately predicts future information dynamics underlying behavior and (via simulated lesioning) suggests a role for cognitive control networks as key drivers of response information flow.     

Adaptive timing in neural networks: The conditioned response

A conditioned response not only reflects knowledge of an association between two events, a CS and a US, it also reflects knowledge about the timing of these events. A neural network and set of learning rules that generates appropriately timed conditioned response waveforms is presented. The model is capable of simulating some of the basic temporal properties of conditioned responses exhibited in biological systems, including (1) decreasing onset latency during acquisition training, (2) peak amplitude accurring at the temporal locus of the US, (3) inhibition of delay, and (4) trace conditioning. The model is also capable of simulating complex CR waveforms under certain conditions, and these simulations are compared with the results of behavioral experiments. The temporally adaptive responses are achieved by virtue of stimulus trace processes that are built into the network architecture.     

An artificial hysteresis binary neuron: a model suppressing the oscillatory behaviors of neural dynamics

A hysteresis binary McCulloch-Pitts neuron model is proposed in order to suppress the complicated oscillatory behaviors of neural dynamics. The artificial hysteresis binary neural network is used for scheduling time-multiplex crossbar switches in order to demonstrate the effects of hysteresis. Time-multiplex crossbar switching systems must control traffic on demand such that packet blocking probability and packet waiting time are minimized. The system using n×n processing elements solves an n×n crossbar-control problem with O(1) time, while the best existing parallel algorithm requires O(n) time. The hysteresis binary neural network maximizes the throughput of packets through a crossbar switch. The solution quality of our system does not degrade with the problem size.     

Genomically and biochemically accurate metabolic reconstruction of Methanosarcina barkeri Fusaro,iMG746

Methanosarcina barkeri is an Archaeon that produces methane anaerobically as the primary byproduct of its metabolism. M. barkeri can utilize several substrates for ATP and biomass production including methanol, acetate, methyl amines, and a combination of hydrogen and carbon dioxide. In 2006, a metabolic reconstruction of M. barkeri, iAF692, was generated based on a draft genome annotation. The iAF692 reconstruction enabled the first genome-Scale simulations for Archaea. Since the publication of the first metabolic reconstruction of M. barkeri, additional genomic, biochemical, and phenotypic data have clarified several metabolic pathways. We have used this newly available data to improve the M. barkeri metabolic reconstruction. Modeling simulations using the updated model, iMG746, have led to increased accuracy in predicting gene knockout phenotypes and simulations of batch growth behavior. We used the model to examine knockout lethality data and make predictions about metabolic regulation under different growth conditions. Thus, the updated metabolic reconstruction of M. barkeri metabolism is a useful tool for predicting cellular behavior, studying the methanogenic lifestyle, guiding experimental studies, and making predictions relevant to metabolic engineering applications.     

A maximum neural network approach for N-queens problems

 A novel neural network approach using the maximum neuron model is presented for N-queens problems. The goal of the N-queens problem is to find a set of locations of N queens on an N×N chessboard such that no pair of queens commands each other. The maximum neuron model proposed by Takefuji et al. has been applied to two optimization problems where the optimization of objective functions is requested without constraints. This paper demonstrates the effectiveness of the maximum neuron model for constraint satisfaction problems through the N-queens problem. The performance is verified through simulations in up to 500-queens problems on the sequential mode, the N-parallel mode, and the N ²-parallel mode, where our maximum neural network shows the far better performance than the existing neural networks. Received: 4 June 1996/Accepted in revised form: 13 November 1996     

Proteome map of the neural complex of the tunicate Ciona intestinalis,the closest living relative to vertebrates

Ciona intestinalis (the common sea squirt) is the closest living chordate relative to vertebrates with cosmopolitan presence worldwide. It has a relatively simple nervous system and development, making it a widely studied alternative model system in neuroscience and developmental biology. The use of Ciona as a model organism has increased significantly after the draft genome was published. In this study, we describe the first proteome map of the neural complex of C. intestinalis. A total of 544 proteins were identified based on 1DE and 2DE FTMS/ITMSMS analyses. Proteins were annotated against the Ciona database and analyzed to predict their molecular functions, roles in biological processes, and position in constructed network pathways. The identified Ciona neural complex proteome was found to map onto vertebrate nervous system pathways, including cytoskeleton remodeling neurofilaments, cell adhesion through the histamine receptor signaling pathway, γ‐aminobutyric acid‐A receptor life cycle neurophysiological process, glycolysis, and amino acid metabolism. The proteome map of the Ciona neural complex is the first step toward a better understanding of several important processes, including the evolution and regeneration capacity of the Ciona nervous system.     

A neural network for computing eigenvectors and eigenvalues

A dynamic method which produces estimates of real eigenvectors and eigenvalues is presented. More generally, the technique can be applied to estimate eigenspectra of real n-dimensional k-forms. The proposed approach is based on a spectral splicing property of the line manifolds often found in solutions of polynomial differential equations. As such, it defines an artificial continuous time neural network with stored memories determined by the eigenspectra locations. This paradigm provides a good insight into an analog behavior of large scale neural structures which provide auto- or hetero-associative memories. Consequently, it has applications not only in computational sciences but also as an information processor.     

STORE working memory networks for storage and recall of arbitrary temporal sequences

Neural network models of working memory, called “sustained temporal order recurrent” (STORE) models, are described. They encode the invariant temporal order of sequential events in short-term memory (STM) in a way that mimics cognitive data about working memory, including primacy, recency, and bowed order and error gradients. As new items are presented, the pattern of previously stored items remains invariant in the sense that relative activations remain constant through time. This invariant temporal order code enables all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such competence is needed to design self-organizing temporal recognition and planning systems in which any subsequence of events may need to be categorized in order to control and predict future behavior or external events. STORE models show how arbitrary event sequences may be invariantly stored, including repeated events. A preprocessor interacts with the working memory to represent event repeats in spatially separate locations. It is shown why at least two processing levels are needed to invariantly store events presented with variable durations and interstimulus intervals. It is also shown how network parameters control the type and shape of primacy, recency, or bowed temporal order gradients that will be stored. Received: 3 November 1992/Accepted in revised form: 2 May 1994     

Ordering process of self-organizing maps improved by asymmetric neighborhood function

The Self-organizing map (SOM) is an unsupervised learning method based on the neural computation, which has found wide applications. However, the learning process sometime takes multi-stable states, within which the map is trapped to an undesirable disordered state including topological defects on the map. These topological defects critically aggravate the performance of the SOM. In order to overcome this problem, we propose to introduce an asymmetric neighborhood function for the SOM algorithm. Compared with the conventional symmetric one, the asymmetric neighborhood function accelerates the ordering process even in the presence of the defect. However, this asymmetry tends to generate a distorted map. This can be suppressed by an improved method of the asymmetric neighborhood function. In the case of one-dimensional SOM, it is found that the required steps for perfect ordering is numerically shown to be reduced from O(N ³) to O(N ²). We also discuss the ordering process of a twisted state in two-dimensional SOM, which can not be rectified by the ordinary symmetric neighborhood function.     

Learning-Induced Synchronization and Plasticity of a Developing Neural Network

Learning-induced synchronization of a neural network at various developing stages is studied by computer simulations using a pulse-coupled neural network model in which the neuronal activity is simulated by a one-dimensional map. Two types of Hebbian plasticity rules are investigated and their differences are compared. For both models, our simulations show a logarithmic increase in the synchronous firing frequency of the network with the culturing time of the neural network. This result is consistent with recent experimental observations. To investigate how to control the synchronization behavior of a neural network after learning, we compare the occurrence of synchronization for four networks with different designed patterns under the influence of an external signal. The effect of such a signal on the network activity highly depends on the number of connections between neurons. We discuss the synaptic plasticity and enhancement effects for a random network after learning at various developing stages.     

AUTOMATIC FEATURE EXTRACTION AND CLASSIFICATION OF CROSSBILL (LOXIA SPP.) FLIGHT CALLS

ABSTRACT

In this paper a new method for the automatic classification of bird sounds is presented. Our method is based on acoustic parameters (features) taken from the first harmonic component computed from the sound spectrogram. The features are based on a line segment approximation of the first harmonic component. The final feature vectors, consisting of 16 real numbers, are then classified using a self-organizing map (SOM) neural network. Flight calls of four crossbill species (Loxia spp.) are used as a test example. In the first phase, an unsupervised network was trained and tested using common crossbill L. curvirostra flight calls recorded mainly in the Netherlands. The network was tested using two-barred L. leucoptera, Scottish L. scotica and parrot L. pytyopsittacus crossbill flight calls in the second phase. Finally, the results were validated applying the same network to flight calls of common crossbills and parrot crossbills recorded in Finland. The method automatically separated common crossbill flight calls from those of parrot crossbills. The classification accuracy of the Dutch recordings was 58% in the first phase and 54% in the second phase. The Finnish recordings were classified with 54% accuracy.     

Radial basis function neural networks for modeling growth rates of the basidiomycetes Physisporinus vitreus and Neolentinus lepideus

A radial basis function (RBF) neural network was developed and compared against a quadratic response surface (RS) model for predicting the specific growth rates of the biotechnologically important basidiomycetous fungi, Physisporinus vitreus and Neolentinus lepideus, under three environmental conditions: temperature (10–30 °C), water activity (0.950–9.998), and pH (4–6). Both the RBF network and polynomial RS model were mathematically evaluated against experimental data using graphical plots and several statistical indices. The evaluation showed that both models gave reasonably good predictions, but the performance of the RBF neural network was superior to that of the classical statistical method for all three data sets used (training, testing, full). Sensitivity analysis revealed that of the three experimental factors the most influential on the growth rate of P. vitreus was water activity, followed by temperature and pH to a lesser extent. In contrast, temperature in particular and then water activity were the key determinants of the development of N. lepideus. RBF neural networks could be a powerful technique for modeling fungal growth behavior under certain parameters and an alternative to time-consuming, traditional microbiological techniques.     

What does the fruitless gene tell us about nature vs. nurture in the sex life of Drosophila?

The fruitless (fru) gene in Drosophila has been proposed to play a master regulator role in the formation of neural circuitries for male courtship behavior, which is typically considered to be an innate behavior composed of a fixed action pattern as generated by the central pattern generator. However, recent studies have shed light on experience-dependent changes and sensory-input-guided plasticity in courtship behavior. For example, enhanced male-male courtship, a fru mutant “hallmark,” disappears when fru-mutant males are raised in isolation. The fact that neural fru expression is induced by neural activities in the adult invites the supposition that Fru as a chromatin regulator mediates experience-dependent epigenetic modification, which underlies the neural and behavioral plasticity.     

Role of sensory cues on food searching behavior of a captive Manta birostris (Chondrichtyes,Mobulidae)

This study reports on the first experimental research designed specifically for Manta birostris behavior. The authors attempted to learn about the feeding behavior and environmental cues influencing this behavior, as well as general cognitive ability. The preconditioned Manta's ability to identify food, on the basis of a fraction of the ordinary food signal complex, was tested. The opening of cephalic fins was considered a good indicator of feeding motivation level. The study subject animal used its biological clock to predict time and also associated a specific location with food, suggesting an ability to build up a cognitive map of its environment. Both underwater visual stimuli and olfactory stimuli had a very intense effect on food searching behavior over a 30 m distance, in contrast to visual signs from above the water surface. In addition, although an underwater visual signal resulted in a more intense response than from an olfactory signal, the specimen did not discriminate between different objects tested on the basis of visual sensation. It could therefore be suggested that food searching behavior of Mantas are governed by triggering stimuli, including smell or visual recognition, and modulated by the cognitive spatial map stored in their long‐term memory. These findings will hopefully prove useful while devising protecting policies in the natural environment and/or while keeping these animals in captivity. Zoo Biol 27:294–304, 2008. © 2008 Wiley‐Liss, Inc.     

A Digital Network Approach to Infer Sex Behavior in Emerging HIV Epidemics

Purpose

Improve the ability to infer sex behaviors more accurately using network data.

Methods

A hybrid network analytic approach was utilized to integrate: (1) the plurality of reports from others tied to individual(s) of interest; and (2) structural features of the network generated from those ties. Network data was generated from digitally extracted cell-phone contact lists of a purposeful sample of 241 high-risk men in India. These data were integrated with interview responses to describe the corresponding individuals in the contact lists and the ties between them. HIV serostatus was collected for each respondent and served as an internal validation of the model’s predictions of sex behavior.

Results

We found that network-based model predictions of sex behavior and self-reported sex behavior had limited correlation (54% agreement). Additionally, when respondent sex behaviors were re-classified to network model predictions from self-reported data, there was a 30.7% decrease in HIV seroprevalence among groups of men with lower risk behavior, which is consistent with HIV transmission biology.

Conclusion

Combining the relative completeness and objectivity of digital network data with the substantive details of classical interview and HIV biomarker data permitted new analyses and insights into the accuracy of self-reported sex behavior.     

Decoding the Attentional Demands of Gait through EEG Gamma Band Features

Rehabilitation techniques are evolving focused on improving their performance in terms of duration and level of recovery. Current studies encourage the patient’s involvement in their rehabilitation. Brain-Computer Interfaces are capable of decoding the cognitive state of users to provide feedback to an external device. On this paper, cortical information obtained from the scalp is acquired with the goal of studying the cognitive mechanisms related to the users’ attention to the gait. Data from 10 healthy users and 3 incomplete Spinal Cord Injury patients are acquired during treadmill walking. During gait, users are asked to perform 4 attentional tasks. Data obtained are treated to reduce movement artifacts. Features from δ(1 − 4Hz), θ(4 − 8Hz), α(8 − 12Hz), β(12 − 30Hz), γ_low(30 − 50Hz), γ_high(50 − 90Hz) frequency bands are extracted and analyzed to find which ones provide more information related to attention. The selected bands are tested with 5 classifiers to distinguish between tasks. Classification results are also compared with chance levels to evaluate performance. Results show success rates of ∼67% for healthy users and ∼59% for patients. These values are obtained using features from γ band suggesting that the attention mechanisms are related to selective attention mechanisms, meaning that, while the attention on gait decreases the level of attention on the environment and external visual information increases. Linear Discriminant Analysis, K-Nearest Neighbors and Support Vector Machine classifiers provide the best results for all users. Results from patients are slightly lower, but significantly different, than those obtained from healthy users supporting the idea that the patients pay more attention to gait during non-attentional tasks due to the inherent difficulties they have during normal gait. This study provides evidence of the existence of classifiable cortical information related to the attention level on the gait. This fact could allow the development of a real-time system that obtains the attention level during lower limb rehabilitation. This information could be used as feedback to adapt the rehabilitation strategy.     

Adaptation supports short-term memory in a visual change detection task

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.     

Mechanisms driving neural crest induction and migration in the zebrafish and Xenopus laevis

The neural crest is an evolutionary adaptation, with roots in the formation of mesoderm. Modification of neural crest behavior has been is critical for the evolutionary diversification of the vertebrates and defects in neural crest underlie a range of human birth defects. There has been a tremendous increase in our knowledge of the molecular, cellular, and inductive interactions that converge on defining the neural crest and determining its behavior. While there is a temptation to look for simple models to explain neural crest behavior, the reality is that the system is complex in its circuitry. In this review, our goal is to identify the broad features of neural crest origins (developmentally) and migration (cellularly) using data from the zebrafish (teleost) and Xenopus laevis (tetrapod amphibian) in order to illuminate where general mechanisms appear to be in play, and equally importantly, where disparities in experimental results suggest areas of profitable study.     

Coupling isoprene and monoterpene emissions from Amazonian tree species with physiological and environmental parameters using a neural network approach

The ability to predict isoprene emissions from plants is important for predicting atmospheric chemistry. To improve the basis for prediction capability, data obtained from continuous field measurements of isoprene and monoterpene emissions from three Amazonian tree species were related to observed environmental and leaf physiological parameters using a new neural network approach. The environmental parameters included leaf temperature, light, relative humidity, water vapour pressure deficit, and the history of ambient temperature and ozone concentration, whereas the physiological parameters included stomatal conductance, assimilation and intercellular CO₂ concentration. The neural approach with 24 different combinations of these parameters was applied to predict the emission variability observed during short time periods (2–3 d) with individual tree branches and, on a longer-term scale, in aggregated data sets from different seasons, leaf developmental stage, and light environment. The results were compared to the quasi standard emission algorithm for isoprene. On the short-term scale, good agreement (r²≈ 0.9) was obtained between observations and predictions of the standard algorithm as well as predictions of the neural network using the same input parameters (leaf temperature and light). When these predictors were used to model the long-term emission variability, r² was reduced to < 0.5 for both approaches. Remarkably, for the neural technique, more than 50% of the unexplained variance could be explained by the mean temperature of the preceding 36 h. An even better network performance was obtained with physiological parameter combinations (r² > 0.9) suggesting a strong and applicable link between isoprenoid emission and leaf primary metabolism.