Signaling in large-scale neural networks

We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons.     

Artificial neural networks in Neurosciences

This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.     

Applications of neural networks in training science

Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming.     

Kohonen neural networks and language.

Kohonen neural networks are a type of self-organizing network that recognizes the statistical characteristics of input datasets. The application of this type of neural network to language theory is demonstrated in the present note by showing three brief applications: recognizing word borders, learning the limited phonemes of one's native tongue, and category-specific naming impairments.     

Statistical inference: learning in artificial neural networks

Artificial neural networks (ANNs) are widely used to model low-level neural activities and high-level cognitive functions. In this article, we review the applications of statistical inference for learning in ANNs. Statistical inference provides an objective way to derive learning algorithms both for training and for evaluation of the performance of trained ANNs. Solutions to the over-fitting problem by model-selection methods, based on either conventional statistical approaches or on a Bayesian approach, are discussed. The use of supervised and unsupervised learning algorithms for ANNs are reviewed. Training a multilayer ANN by supervised learning is equivalent to nonlinear regression. The ensemble methods, bagging and arching, described here, can be applied to combine ANNs to form a new predictor with improved performance. Unsupervised learning algorithms that are derived either by the Hebbian law for bottom-up self-organization, or by global objective functions for top-down self-organization are also discussed.     

阿尔兹海默症及轻度认知障碍静息态大尺度脑网络功能连接的改变

静息态功能磁共振成像作为非侵入性可视化成像方法, 且数据采集简便易行, 已成为探索阿尔兹海默症及轻度认知障碍脑功能变异的主要成像手段。近年来静息态研究显示在其前驱症状期轻度认知障碍阶段患者已显现出静息态脑网络的变异, 而阿尔兹海默症患者的网络改变更加弥散。研究发现随着病程推进, 患者显示出默认网络连接逐渐减弱以及额叶认知网络连接先增强后减弱的整体趋势。此外, 脑结构和功能网络的改变并非单向因果关系, 二者在病程进展中存在交互作用。未来研究可以从诊断的标志性神经通路、疗效的大尺度脑网络标记, 以及疾病的异质性等角度入手, 进一步探索静息态脑网络作为阿尔兹海默症诊断和病程监控指标的可能性。     

Hinton diagrams: Viewing connection strengths in neural networks

A graphical analysis similar to that used by Hinton and Shallice (1991) was applied to the hair cells of a simulated auditory transducer. The graphical analysis made it apparent that there were hair cells that responded to a narrow range of frequencies, as would be predicted by the tonotopic organization of the real physiology. In short, this study demonstrates the efficacy of using graphic techniques to examine the nature of the autopoetic organization of the hidden layer of back-propagation networks.     

Learning and encoding higher order rules in neural networks

Some researchers state that whereas neural networks are fine for pattern recognition and categorization, complex rule formation requires a separate “symbolic” level. However, the human brain is a connectionist system and, however imperfectly, does complex reasoning and inference. Familiar modeling principles (e.g., Hebbian or associative learning, lateral inhibition, opponent processing, neuromodulation) could recur, in different combinations, in architectures that can learn diverse rules. These rules include, for example, “go to the most novel object,” “alternate between two given objects,” and “touch three given objects, without repeats, in any order.” Frontal lobe damage interferes with learning all three of those rules. Hence, network models of rule learning and encoding should include a module analogous to the prefrontal cortex. They should also include modules analogous to the hippocampus for episode setting and analogous to the amygdala for emotional evaluation.     

Altered topological patterns of large-scale brain functional networks during passive hyperthermia

In this study, we simulated environmental heat exposure to 18 participants, and obtained functional magnetic resonance image (fMRI) data during resting state. Brain functional networks were constructed over a wide range of sparsity threshold according to a prior atlas dividing the whole cerebrum into 90 regions. Results of graph theoretical approaches showed that although brain networks in both normal and hyperthermia conditions exhibited economical small-world property, significant alterations in both global and nodal network metrics were demonstrated during hyperthermia. Specifically, a lower clustering coefficient, maintained shortest path length, a lower small-worldness, a lower mean local efficiency were found, indicating a tendency shift to a randomized network. Additionally, significant alterations in nodal efficiency were found in bilateral gyrus rectus, bilateral parahippocampal gyrus, bilateral insula, right caudate nucleus, bilateral putamen, left temporal pole of middle temporal gyrus, right inferior temporal gyrus. In consideration of physiological system changes, we found that the alterations of normalized clustering coefficient, small-worldness, mean normalized local efficiency were significantly correlated with the rectal temperature alteration, but failed to obtain significant correlations with the weight loss. More importantly, behavioral attention network test (ANT) after MRI scanning showed that the ANT effects were altered and correlated with the alterations of some global metrics (normalized shortest path length and normalized global efficiency) and prefrontal nodal efficiency (right dorsolateral superior frontal gyrus, right middle frontal gyrus and left orbital inferior frontal gyrus), implying behavioral deficits in executive control effects and maintained alerting and orienting effects during passive hyperthermia. The present study provided the first evidence for human brain functional disorder during passive hyperthermia according to graph theoretical analysis using resting-state fMRI.     

Comparing representations and computations in single neurons versus neural networks

Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function.     

Polytopes as vehicles of informational content in feedforward neural networks

Localizing content in neural networks provides a bridge to understanding the way in which the brain stores and processes information. In this paper, I propose the existence of polytopes in the state space of the hidden layer of feedforward neural networks as vehicles of content. I analyze these geometrical structures from an information-theoretic point of view, invoking mutual information to help define the content stored within them. I establish how this proposal addresses the problem of misclassification and provide a novel solution to the disjunction problem, which hinges on the precise nature of the causal-informational framework for content advocated herein.     

Towards end-to-end likelihood-free inference with convolutional neural networks

Complex simulator-based models with non-standard sampling distributions require sophisticated design choices for reliable approximate parameter inference. We introduce a fast, end-to-end approach for approximate Bayesian computation (ABC) based on fully convolutional neural networks. The method enables users of ABC to derive simultaneously the posterior mean and variance of multidimensional posterior distributions directly from raw simulated data. Once trained on simulated data, the convolutional neural network is able to map real data samples of variable size to the first two posterior moments of the relevant parameter's distributions. Thus, in contrast to other machine learning approaches to ABC, our approach allows us to generate reusable models that can be applied by different researchers employing the same model. We verify the utility of our method on two common statistical models (i.e., a multivariate normal distribution and a multiple regression scenario), for which the posterior parameter distributions can be derived analytically. We then apply our method to recover the parameters of the leaky competing accumulator (LCA) model and we reference our results to the current state-of-the-art technique, which is the probability density estimation (PDA). Results show that our method exhibits a lower approximation error compared with other machine learning approaches to ABC. It also performs similarly to PDA in recovering the parameters of the LCA model.     

On simulating one-trial learning using morphological neural networks

“Learning once, remembering forever”, this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon “one-trial learning”. The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of “one-trial learning”, therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science.     

Computer simulation of neural networks for perceptual psychology

Computer simulations of neural network processes fill an important methodological niche, permitting the investigation of questions not resolvable by physiological, behavioral, or formal approaches alone. Two types of network simulations are considered: simulations of boundary completion and simulations of segmentation. Simulations that compare properties of published models with variations of these models are presented to illustrate how parametric computer simulations have guided the development of neural models of visual perception.     

A hybrid model using neural networks and ACT-R

How do people make decisions given contradictory information? This paper presents a model of how expert DSOs (defensive system operators) on a B1 bomber examine a complex series of signals, categorize whether those signals are dangerous or not, and then make a decision on the basis of those signals. This decision is made more difficult because an automatic on-board computer sometimes identifies the signal incorrectly. Therefore, the DSO must compare the actual signal to the system ID “guess.” The proposed model is a hybrid model, combining a standard neural network and ACT-R, a production system, which achieves a high degree of success.     

Teaching students to model neural circuits and neural networks using an electronic spreadsheet simulator

An electronic spreadsheet simulator can be used to enable students to conduct simulated microelectrode recording experiments. In addition, it can be used both to let students explore the operation of models of hypothetical neural networks and to let them design and develop their own neural models.     

Empiricism without magic: transformational abstraction in deep convolutional neural networks

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain.     

Prefrontal cortex regulates inhibition and excitation in distributed neural networks.

Prefrontal cortex provides both inhibitory and excitatory input to distributed neural circuits required to support performance in diverse tasks. Neurological patients with prefrontal damage are impaired in their ability to inhibit task-irrelevant information during behavioral tasks requiring performance over a delay. The observed enhancements of primary auditory and somatosensory cortical responses to task-irrelevant distractors suggest that prefrontal damage disrupts inhibitory modulation of inputs to primary sensory cortex, perhaps through abnormalities in a prefrontal-thalamic sensory gating system. Failure to suppress irrelevant sensory information results in increased neural noise, contributing to the deficits in decision making routinely observed in these patients. In addition to a critical role in inhibitory control of sensory flow to primary cortical regions, and tertiary prefrontal cortex also exerts excitatory input to activity in multiple sub-regions of secondary association cortex. Unilateral prefrontal damage results in multi-modal decreases in neural activity in posterior association cortex in the hemisphere ipsilateral to damage. This excitatory modulation is necessary to sustain neural activity during working memory. Thus, prefrontal cortex is able to sculpt behavior through parallel inhibitory and excitatory regulation of neural activity in distributed neural networks.