Stepping out of the box: information processing in the neural networks of the basal ganglia

The Albin-DeLong 'box and arrow' model has long been the accepted standard model for the basal ganglia network. However, advances in physiological and anatomical research have enabled a more detailed neural network approach. Recent computational models hold that the basal ganglia use reinforcement signals and local competitive learning rules to reduce the dimensionality of sparse cortical information. These models predict a steady-state situation with diminished efficacy of lateral inhibition and low synchronization. In this framework, Parkinson's disease can be characterized as a persistent state of negative reinforcement, inefficient dimensionality reduction, and abnormally synchronized basal ganglia activity.     

Synchronization of neural activity is a promising mechanism of memory information processing in networks of columns

Synchronization of the oscillatory discharge of cortical neurons could be a part of the mechanism that is involved in cortical information processing. On the assumption that the basic functional unit is the column composed of local excitatory and inhibitory cells and generating oscillatory neural activity, a network model that attains associative memory function is proposed. The synchronization of oscillation in the model is studied analytically using a sublattice analysis. In particular, the retrieval of a single memory pattern can be studied in the system, which can be derived from the original network model of interacting columns and is formally equivalent to a system of an isolated column. The network model simulated numerically shows a remarkable performance in which retrieval is achieved simultaneously for more than one memory pattern. The manifestations of this simultaneous retrieval in the network dynamics are successive transitions of the network state from a synchronized oscillation for a memory pattern to that for another memory pattern.     

Hemispheric specialization for global and local processing: the effect of stimulus category.

Neuropsychological evidence indicates that the global aspect of complex visual scenes is preferentially processed by the right hemisphere, and local aspects are preferentially processed by the left hemisphere. Using letter-based hierarchical stimuli (Navon figures), we recently demonstrated, in a directed-attention task, lateralized neural activity (assessed by positron emission tomography) in the left prestriate cortex during local processing, and in the right prestriate cortex during global processing. Furthermore, temporal-parietal cortex was critically activated bilaterally in a divided-attention task that involved varying the number of target switches between local and global levels of letter-based hierarchical stimuli. Little is known about whether such stimulus categories influence such hemispheric lateralization. We now present data on brain activity, derived from positron emission tomography, in normal subjects scanned during either local or global processing of object-based hierarchical stimuli. We again observe attentional modulation of neural activity in prestriate cortex. There is now greater right-sided activation for local processing and greater left-sided activation for global processing, which is the opposite of that seen with letter-based stimuli. The results suggest that the relative differential hemispheric activations in the prestriate areas during global and local processing are modified by stimulus category.     

A local training and pruning approach for neural networks

The training of neural networks using the extended Kalman filter (EKF) algorithm is plagued by the drawback of high computational complexity and storage requirement that may become prohibitive even for networks of moderate size. In this paper, we present a local EKF training and pruning approach that can solve this problem. In particular, the by-products obtained along with the local EKF training can be utilized to measure the importance of the network weights. Comparing with the original global approach, the proposed local EKF training and pruning approach results in a much lower computational complexity and storage requirement. Hence, it is more practical in solving real world problems. The performance of the proposed algorithm is demonstrated on one medium- and one large-scale problems, namely, sunspot data prediction and handwritten digit recognition.     

Spatial constancy and the brain: insights from neural networks

To form an accurate internal representation of visual space, the brain must accurately account for movements of the eyes, head or body. Updating of internal representations in response to these movements is especially important when remembering spatial information, such as the location of an object, since the brain must rely on non-visual extra-retinal signals to compensate for self-generated movements. We investigated the computations underlying spatial updating by constructing a recurrent neural network model to store and update a spatial location based on a gaze shift signal, and to do so flexibly based on a contextual cue. We observed a striking similarity between the patterns of behaviour produced by the model and monkeys trained to perform the same task, as well as between the hidden units of the model and neurons in the lateral intraparietal area (LIP). In this report, we describe the similarities between the model and single unit physiology to illustrate the usefulness of neural networks as a tool for understanding specific computations performed by the brain.     

Development of a record-keeping strategy for improvement of information retention in microbiological processing

The improvement of microbiological information processing in clinical laboratories depends on retention of information concerning who, what, when, how, and why each process was performed, the implementation of quality control procedures, and finally, its evaluation. The four objectives to be addressed are as follows: (1) to improve the collection of information concerned with microbiological processes, (2) to evaluate results of implemented strategies, (3) to offer a model data base to be used in research projects, and (4) to propose an evaluation model for comparative studies. To do this, microbiological cultures were collected from hospitalized patients from June 1997 to June 2003. Data for the analytical matrix were obtained from lab requests, medical history and the microbiological data. Statistical analyses were performed in Epi-Info 6. The laboratory records for 46,072 microbiological cultures were analyzed. Completion levels in data collection were compared between years 1997 and 2003. Samples from 1997 and 2003 showed 11% and 99% of the request forms specifically requesting microbiological culture, 11% and 99% were completed in 1997 and 2003, respectively. For the same years, 9% and 85% specifically stated the time of the request. Ten percent and 68%, respectively, provided complete information. Zero and 83% respectively stated who had collected the sample. Zero and 77%, respectively, specified the time of sample collection. Forms containing all relevent microbiological data were most complete with 78% and 96%, respectively. A database with 44 variables related to microbiological processes was created. In conclusion, improvement of microbiological data processing depends not only on the method of collection and completion of recorded information, but also on constant quality control and evaluation.     

Sequential configuration model for firing patterns in local neural networks

This paper presents a sequential configuration model to represent the coordinated firing patterns of memory traces in groups of neurons in local networks. Computer simulations are used to study the dynamic properties of memory traces selectively retrieved from networks in which multiple memory traces have been embedded according to the sequential configuration model. Distinct memory traces which utilize the same neurons, but differ only in temporal sequencing are selectively retrievable. Firing patterns of constituent neurons of retrieved memory traces exhibit the main properties of neurons observed in multi microelectrode recordings. The paper shows how to adjust relative synaptic weightings so as to control the disruptive influences of cross-talk in multipy-embedded networks. The theoretical distinction between (primarily anatomical) beds and (primarily physiological) realizations underlines the fundamentally stochastic nature of network firing patterns, and allows the definition of 4 degrees of clarity of retrieved memory traces.     

Population oscillations in a discrete model of neural networks of the brain

A discrete model of biological neural networks is used to find out how synchronized firing of neurons emerges in a randomly connected neural population. The objective is to understand the mechanisms underlying brain waves and to find and characterize conditions which support spontaneous switching from disordered to rhythmic population activity as in case of an epileptic seizure. The model is kept as simple as possible to achieve on one hand a fast performance of computer simulations of networks with up to 10,000 neurons and to keep on the other hand an overview of parameter dependences. Dynamics of the model can be classified into different regimes: random fluctuations, rhythmic oscillations and silence. When the ratio of the inhibitory/excitatory connectivity is raised the system crosses from the fluctuating regime through the rhythmic oscillating region to the silence regime. Close to the boundary between the fluctuating and the oscillating regimes the network shows spontaneous bursting of high amplitude rhythmic oscillations, which is characteristic of epileptiform behavior. The simulation results are in agreement with recent theories saying that focal epilepsy after injury of the brain could result from axonal sprouting of GABAergic neurons in the injured region.     

Optimal parameter settings for information processing in gene regulatory networks

Gene networks can often be interpreted as computational circuits. This article investigates the computational properties of gene regulatory networks defined in terms of the speed and the accuracy of the output of a gene network. It will be shown that there is no single optimal set of parameters, but instead, there is a trade-off between speed and accuracy. Using the trade-off it will also be shown how systems with various parameters can be ranked with respect to their computational efficiency. Numerical analysis suggests that the trade-off can be improved when the output gene is repressing itself, even though the accuracy or the speed of the auto-regulated system may be worse than the unregulated system.     

Population networks: a large-scale framework for modelling cortical neural networks

Artificial neural networks are usually built on rather few elements such as activation functions, learning rules, and the network topology. When modelling the more complex properties of realistic networks, however, a number of higher-level structural principles become important. In this paper we present a theoretical framework for modelling cortical networks at a high level of abstraction. Based on the notion of a population of neurons, this framework can accommodate the common features of cortical architecture, such as lamination, multiple areas and topographic maps, input segregation, and local variations of the frequency of different cell types (e.g., cytochrome oxidase blobs). The framework is meant primarily for the simulation of activation dynamics; it can also be used to model the neural environment of single cells in a multiscale approach. Received: 9 January 1996 / Accepted in revised form: 24 July 1996     

A structural and a functional aspect of stable information processing by the brain

Brain is an expert in producing the same output from a particular set of inputs, even from a very noisy environment. In this article a model of neural circuit in the brain has been proposed which is composed of cyclic sub-circuits. A big loop has been defined to be consisting of a feed forward path from the sensory neurons to the highest processing area of the brain and feed back paths from that region back up to close to the same sensory neurons. It has been mathematically shown how some smaller cycles can amplify signal. A big loop processes information by contrast and amplify principle. How a pair of presynaptic and postsynaptic neurons can be identified by an exact synchronization detection method has also been mentioned. It has been assumed that the spike train coming out of a firing neuron encodes all the information produced by it as output. It is possible to extract this information over a period of time by Fourier transforms. The Fourier coefficients arranged in a vector form will uniquely represent the neural spike train over a period of time. The information emanating out of all the neurons in a given neural circuit over a period of time can be represented by a collection of points in a multidimensional vector space. This cluster of points represents the functional or behavioral form of the neural circuit. It has been proposed that a particular cluster of vectors as the representation of a new behavior is chosen by the brain interactively with respect to the memory stored in that circuit and the amount of emotion involved. It has been proposed that in this situation a Coulomb force like expression governs the dynamics of functioning of the circuit and stability of the system is reached at the minimum of all the minima of a potential function derived from the force like expression. The calculations have been done with respect to a pseudometric defined in a multidimensional vector space.     

Olfactory information processing in the brain: Encoding chemical and temporal features of odors

A fundamental problem in studying the neural mechanisms of odor recognition and discrimination in the olfactory system lies in determining the features or “primitives” of an odor stimulus that are analyzed by glomerular circuits at the first level of processing in the brain. Several recent studies support the idea that it is not simply the molecular features of odors that contain important information, but also the intermittent pattern of their presentation to the olfactory epithelium that helps determine the behavioral response to odor. © 1996 John Wiley & Sons, Inc.     

Institutions,information exchange,and migrant social networks in Rome

While a considerable body of work examines immigrant networks, inadequate attention has been devoted to understanding how networks regulate the relationship between immigrants and host institutions. A rich immigrant process may reinforce current power structures by providing a convenient buffer between the elite and challengers. Conversely, immigration may challenge the status quo. I employ social network analysis to examine three understudied immigrant groups in Rome, Bangladeshis, Filipinos, and Peruvians. I find that they have developed systems of problem-solving and sense-making that often interrupt their interactions with host institutions. The state tolerates this because it situates immigrants’ concerns outside its sphere of responsibility.     

EEG correlates of the change in information processing strategy in visual imagery

Cnanger of the spatial organization of biopotentials (spatial synchronization and disorder, spectral power and coherence) were analyzed during mental creating of visual images from two simple elements: the angle the oblique line. With the transition from the first to the fourth task, the total number of used elements increased from the number suitable for simultaneous presentation and conscious processing (less than 7 +/- 2) to a much higher number. Changes in the characteristics of the spatial organization of biopotentials associated with the increase in the number of the elements can be explained by a change in the information processing strategy, transformation of information processing strategy, i.e. the transition from the left-hemispheric successive (conscious) analyses to the right-hemispheric simultaneous (unconscious) processing. It was shown that this change in the information processing strategy was accompanied by an increase in the index of spatial disorder sensitive to complicated nonlinear processes. Subjects were divided in two groups with different forms of the reorganization of interhemispheric and fronto-occipital relationships of biopotentials. These data are interpreted in terms of different involvement of the unconscious intellectual processes of different layers (subconsciousness and superconsciousness) in the change in information processing strategy.     

Learning the local landscape of protein structures with convolutional neural networks

One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received much less attention even though it has important applications in both protein engineering and evolutionary biology. Here, we ask whether 3D convolutional neural networks (3D CNNs) can learn the local fitness landscape of protein structure to reliably predict either the wild-type amino acid or the consensus in a multiple sequence alignment from the local structural context surrounding site of interest. We find that the network can predict wild type with good accuracy, and that network confidence is a reliable measure of whether a given prediction is likely going to be correct or not. Predictions of consensus are less accurate and are primarily driven by whether or not the consensus matches the wild type. Our work suggests that high-confidence mis-predictions of the wild type may identify sites that are primed for mutation and likely targets for protein engineering.

     

Observing local and global properties of metabolic pathways: 'load points' and 'choke points' in the metabolic networks

MOTIVATION: The local and global aspects of metabolic network analyses allow us to identify enzymes or reactions that are crucial for the survival of the organism(s), therefore directing us towards the discovery of potential drug targets. RESULTS: We demonstrate a new method ('load points') to rank the enzymes/metabolites in the metabolic network and propose a model to determine and rank the biochemical lethality in metabolic networks (enzymes/metabolites) through 'choke points'. Based on an extended form of the graph theory model of metabolic networks, metabolite structural information was used to calculate the k-shortest paths between metabolites (the presence of more than one competing path between substrate and product). On the basis of these paths and connectivity information, load points were calculated and used to empirically rank the importance of metabolites/enzymes in the metabolic network. The load point analysis emphasizes the role that the biochemical structure of a metabolite, rather than its connectivity (hubs), plays in the conversion pathway. In order to identify potential drug targets (based on the biochemical lethality of metabolic networks), the concept of choke points and load points was used to find enzymes (edges) which uniquely consume or produce a particular metabolite (nodes). A non-pathogenic bacterial strain Bacillus subtilis 168 (lactic acid producing bacteria) and a related pathogenic bacterial strain Bacillus anthracis Sterne (avirulent but toxigenic strain, producing the toxin Anthrax) were selected as model organisms. The choke point strategy was implemented on the pathogen bacterial network of B.anthracis Sterne. Potential drug targets are proposed based on the analysis of the top 10 choke points in the bacterial network. A comparative study between the reported top 10 bacterial choke points and the human metabolic network was performed. Further biological inferences were made on results obtained by performing a homology search against the human genome. AVAILABILITY: The load and choke point modules are introduced in the Pathway Hunter Tool (PHT), the basic version of which is available on http://www.pht.uni-koeln.de.     

NeuroPXI: A real-time multi-electrode array system for recording,processing and stimulation of neural networks and the control of high-resolution neural implants for rehabilitation

The increasing number of channels offered by new Microelectrode Array (MEA) systems raises the problem of data handling and analysis in real time. The goal of the RETINE project is to develop a new MEA system, called NeuroPXI, offering 256 channels with online features for MEA data recording, processing and stimulation. This system is a complete redesign of the ASIC-based BioMEA system developed previously. NeuroPXI is based on a front-end device featuring integrated electronics handling signal amplification and current-mode stimulation, and a back-end device handling real-time data processing and 2D spatiotemporal stimulus generation. Initial tests of this system have been performed on mouse spinal cord networks in which waves of activity were successfully recorded. Further tests of NeuroPXI, performed on rats, will consist first, in recording activity waves in the newborn retina and second, in patterned retinal stimulation combined with optical cortical recordings of the primary visual cortex. Such experiments have recently been conducted using the BioMEA system with standard pulse stimuli, and will be repeated with the NeuroPXI system to include more complex 2D-pattern stimuli.