A neural circuit model for changing the amount of information maintained in short-term memory depending on stimuli relationships

We propose a neural circuit model of changes in amount of information maintained in short-term memory depending on stimuli relationships. The relationships between stimuli are represented by the synchronous firings of overlapping neuronal groups for semantically related stimuli and the excitatory mutual connections for semantically unrelated but simultaneously presented stimuli. We conduct computer simulations to confirm our proposed neural circuit model. The resultant numbers of stored informational input patterns are almost consistent with the maximum numbers in the psychological experiments for both semantically related and unrelated stimuli. This agreement with the psychological experiments suggests that the structure and informational representation of the proposed model are appropriate.     

History-dependent excitability as a single-cell substrate of transient memory for information discrimination

Neurons react differently to incoming stimuli depending upon their previous history of stimulation. This property can be considered as a single-cell substrate for transient memory, or context-dependent information processing: depending upon the current context that the neuron "sees" through the subset of the network impinging on it in the immediate past, the same synaptic event can evoke a postsynaptic spike or just a subthreshold depolarization. We propose a formal definition of History-Dependent Excitability (HDE) as a measure of the propensity to firing in any moment in time, linking the subthreshold history-dependent dynamics with spike generation. This definition allows the quantitative assessment of the intrinsic memory for different single-neuron dynamics and input statistics. We illustrate the concept of HDE by considering two general dynamical mechanisms: the passive behavior of an Integrate and Fire (IF) neuron, and the inductive behavior of a Generalized Integrate and Fire (GIF) neuron with subthreshold damped oscillations. This framework allows us to characterize the sensitivity of different model neurons to the detailed temporal structure of incoming stimuli. While a neuron with intrinsic oscillations discriminates equally well between input trains with the same or different frequency, a passive neuron discriminates better between inputs with different frequencies. This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous in a temporal coding scheme. We also address the influence of intrinsic properties in single-cell processing as a function of input statistics, and show that intrinsic oscillations enhance discrimination sensitivity at high input rates. Finally, we discuss how the recognition of these cell-specific discrimination properties might further our understanding of neuronal network computations and their relationships to the distribution and functional connectivity of different neuronal types.     

Detecting distant homologs using phylogenetic tree-based HMMs

It is often desired to identify further homologs of a family of biological sequences from the ever-growing sequence databases. Profile hidden Markov models excel at capturing the common statistical features of a group of biological sequences. With these common features, we can search the biological database and find new homologous sequences. Most general profile hidden Markov model methods, however, treat the evolutionary relationships between the sequences in a homologous group in an ad-hoc manner. We hereby introduce a method to incorporate phylogenetic information directly into hidden Markov models, and demonstrate that the resulting model performs better than most of the current multiple sequence-based methods for finding distant homologs.     

Analysis of knowledge-based protein-ligand potentials using a self-consistent method

We propose a self-consistent approach to analyze knowledge-based atom-atom potentials used to calculate protein-ligand binding energies. Ligands complexed to actual protein structures were first built using the SMoG growth procedure (DeWitte & Shakhnovich, 1996) with a chosen input potential. These model protein-ligand complexes were used to construct databases from which knowledge-based protein-ligand potentials were derived. We then tested several different modifications to such potentials and evaluated their performance on their ability to reconstruct the input potential using the statistical information available from a database composed of model complexes. Our data indicate that the most significant improvement resulted from properly accounting for the following key issues when estimating the reference state: (1) the presence of significant nonenergetic effects that influence the contact frequencies and (2) the presence of correlations in contact patterns due to chemical structure. The most successful procedure was applied to derive an atom-atom potential for real protein-ligand complexes. Despite the simplicity of the model (pairwise contact potential with a single interaction distance), the derived binding free energies showed a statistically significant correlation (approximately 0.65) with experimental binding scores for a diverse set of complexes.     

Functional anthology of intrinsic disorder. 1. Biological processes and functions of proteins with long disordered regions

Identifying relationships between function, amino acid sequence, and protein structure represents a major challenge. In this study, we propose a bioinformatics approach that identifies functional keywords in the Swiss-Prot database that correlate with intrinsic disorder. A statistical evaluation is employed to rank the significance of these correlations. Protein sequence data redundancy and the relationship between protein length and protein structure were taken into consideration to ensure the quality of the statistical inferences. Over 200,000 proteins from the Swiss-Prot database were analyzed using this approach. The predictions of intrinsic disorder were carried out using PONDR VL3E predictor of long disordered regions that achieves an accuracy of above 86%. Overall, out of the 710 Swiss-Prot functional keywords that were each associated with at least 20 proteins, 238 were found to be strongly positively correlated with predicted long intrinsically disordered regions, whereas 302 were strongly negatively correlated with such regions. The remaining 170 keywords were ambiguous without strong positive or negative correlation with the disorder predictions. These functions cover a large variety of biological activities and imply that disordered regions are characterized by a wide functional repertoire. Our results agree well with literature findings, as we were able to find at least one illustrative example of functional disorder or order shown experimentally for the vast majority of keywords showing the strongest positive or negative correlation with intrinsic disorder. This work opens a series of three papers, which enriches the current view of protein structure-function relationships, especially with regards to functionalities of intrinsically disordered proteins, and provides researchers with a novel tool that could be used to improve the understanding of the relationships between protein structure and function. The first paper of the series describes our statistical approach, outlines the major findings, and provides illustrative examples of biological processes and functions positively and negatively correlated with intrinsic disorder.     

主动联想记忆神经网络模型

在信息编码能提高联想记忆的存贮能力和脑内存在主动活动机制的启发下,提出一个主动联想记忆模型。模型包括两个神经网络,其一为输入和输出网络,另一个为在学习时期能自主产生兴奋模式的主动网络。两个网络的神经元之间有突触联系。由于自主产生的兴奋模式与输入无关,并可能接近于相互正交,因此,本模型有较高的存贮能力。初步分析和计算机仿真证明：本模型确有比通常联想记忆模型高的存贮能力,特别是在输入模式间有高度相关情况下、最后,对提出的模型与双向自联想记忆和光学全息存贮机制的关系作了讨论。     

Functional evidence for a dual route to amygdala

The amygdala plays a central role in evaluating the behavioral importance of sensory information. Anatomical subcortical pathways provide direct input to the amygdala from early sensory systems and may support an adaptively valuable rapid appraisal of salient information. However, the functional significance of these subcortical inputs remains controversial. We recorded magnetoencephalographic activity evoked by tones in the context of emotionally valent faces and tested two competing biologically motivated dynamic causal models against these data: the dual and cortical models. The dual model comprised two parallel (cortical and subcortical) routes to the amygdala, whereas the cortical model excluded the subcortical path. We found that neuronal responses elicited by salient information were better explained when a subcortical pathway was included. In keeping with its putative functional role of rapid stimulus appraisal, the subcortical pathway was most important early in stimulus processing. However, as often assumed, its action was not limited to the context of fear, pointing to a more widespread information processing role. Thus, our data supports the idea that an expedited evaluation of sensory input is best explained by an architecture that involves a subcortical path to the amygdala.     

The role of prefrontal cortex in working memory: examining the contents of consciousness.

Working memory enables us to hold in our ''mind''s eye'' the contents of our conscious awareness, even in the absence of sensory input, by maintaining an active representation of information for a brief period of time. In this review we consider the functional organization of the prefrontal cortex and its role in this cognitive process. First, we present evidence from brain-imaging studies that prefrontal cortex shows sustained activity during the delay period of visual working memory tasks, indicating that this cortex maintains on-line representations of stimuli after they are removed from view. We then present evidence for domain specificity within frontal cortex based on the type of information, with object working memory mediated by more ventral frontal regions and spatial working memory mediated by more dorsal frontal regions. We also propose that a second dimension for domain specificity within prefrontal cortex might exist for object working memory on the basis of the type of representation, with analytic representations maintained preferentially in the left hemisphere and image-based representations maintained preferentially in the right hemisphere. Furthermore, we discuss the possibility that there are prefrontal areas brought into play during the monitoring and manipulation of information in working memory in addition to those engaged during the maintenance of this information. Finally, we consider the relationship of prefrontal areas important for working memory, both to posterior visual processing areas and to prefrontal areas associated with long-term memory.     

The Princeton Protein Orthology Database (P-POD): a comparative genomics analysis tool for biologists

Many biological databases that provide comparative genomics information and tools are now available on the internet. While certainly quite useful, to our knowledge none of the existing databases combine results from multiple comparative genomics methods with manually curated information from the literature. Here we describe the Princeton Protein Orthology Database (P-POD, http://ortholog.princeton.edu), a user-friendly database system that allows users to find and visualize the phylogenetic relationships among predicted orthologs (based on the OrthoMCL method) to a query gene from any of eight eukaryotic organisms, and to see the orthologs in a wider evolutionary context (based on the Jaccard clustering method). In addition to the phylogenetic information, the database contains experimental results manually collected from the literature that can be compared to the computational analyses, as well as links to relevant human disease and gene information via the OMIM, model organism, and sequence databases. Our aim is for the P-POD resource to be extremely useful to typical experimental biologists wanting to learn more about the evolutionary context of their favorite genes. P-POD is based on the commonly used Generic Model Organism Database (GMOD) schema and can be downloaded in its entirety for installation on one's own system. Thus, bioinformaticians and software developers may also find P-POD useful because they can use the P-POD database infrastructure when developing their own comparative genomics resources and database tools.     

Aging,Culture, and Memory for Socially Meaningful Item-Context Associations: An East-West Cross-Cultural Comparison Study

Research suggests that people in Eastern interdependent cultures process information more holistically and attend more to contextual information than do people in Western independent cultures. The current study examined the effects of culture and age on memory for socially meaningful item-context associations in 71 Canadians of Western European descent (35 young and 36 older) and 72 native Chinese citizens (36 young and 36 older). All participants completed two blocks of context memory tasks. During encoding, participants rated pictures of familiar objects. In one block, objects were rated either for their meaningfulness in the independent living context or their typicality in daily life. In the other block, objects were rated for their meaningfulness in the context of fostering relationships with others or for their typicality in daily life. The encoding in each block was followed by a recognition test in which participants identified pictures and their associated contexts. The results showed that Chinese outperformed Canadians in context memory, though both culture groups showed similar age-related deficits in item and context memory. The results suggest that Chinese are at an advantage in memory for socially meaningful item-context associations, an advantage that continues from young adulthood into old age.     

Cluster-based network model for time-course gene expression data

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.     

Representing word context effect in Chinese character perception — an application of information theory

The perception of a letter in the context of a word is easier than in the context of a random letter sequence. It appears that our knowledge about words can influence our perception process. McClelland and Rumelhart (1981) propose an interactive activation model to account for the interaction between our knowledge about words and our visual input. They use their model to explain how these interactions facilitate perception. In their account, word context effect is a constant independent of the identity of the words. In this paper, we propose the use of informatin theory to quantify word context effect. In this way, the strength of word context effect will depend on the identity of the words. We apply the method to quantify word context effect in Chinese words. This knowledge is encoded in an artificial neural network using the interactive activation and competition model. The network is used to recognize Chinese characters and we are able to achieve a high recognition rate.     

A Model of the Hippocampal–cortical Memory System

Based on physiological evidence, we propose a theoretical model of the hippocampal–cortical memory system. The model consists of the following components: the sensory system, the hippocampus (short-term memory), and the association cortex (long-term memory). A series of key codes (local information) is supplied from the sensory system, while context (global information) is inputted from the hippocampus. The two inputs interact dynamically in the association cortex. The interactive neurons work as a detector of coincidence. The cortical network learns the memory information through the coincidence window and, finally, stores it in the form of attractors. This local–global information works as an addressor to designate the stored location of the memory in the association cortex and accelerates the process of storing and retrieving memory information.     

A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language

Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Although considerable work has been done on modeling human language abilities, it has been difficult to bring them together to a comprehensive tabula rasa system compatible with current knowledge of how verbal information is processed in the brain. This work presents a cognitive system, entirely based on a large-scale neural architecture, which was developed to shed light on the procedural knowledge involved in language elaboration. The main component of this system is the central executive, which is a supervising system that coordinates the other components of the working memory. In our model, the central executive is a neural network that takes as input the neural activation states of the short-term memory and yields as output mental actions, which control the flow of information among the working memory components through neural gating mechanisms. The proposed system is capable of learning to communicate through natural language starting from tabula rasa, without any a priori knowledge of the structure of phrases, meaning of words, role of the different classes of words, only by interacting with a human through a text-based interface, using an open-ended incremental learning process. It is able to learn nouns, verbs, adjectives, pronouns and other word classes, and to use them in expressive language. The model was validated on a corpus of 1587 input sentences, based on literature on early language assessment, at the level of about 4-years old child, and produced 521 output sentences, expressing a broad range of language processing functionalities.     

Causal Bayesian network for tagging syntactical structure of Croatian sentences

Paper describes tagging syntactical structure of Croatian language sentences using causal Bayesian network. In the first part of the paper we describe Bayesian model for tagging sentences. Base on this idea, we will test our model on Croatian language sentences on Database of grammatical sentences of Croatian language (http://infoz.ffzg.hr / tepes /). This paper is result of our new research connected with the paper hidden Markov model for tagging of Croatian language texts for project Linguistic Analysis of The European languages and the paper Probability distribution on the parse trees for the project Annotated database and syntactic structure of Croatian languages.     

An oscillatory neural network model of sparse distributed memory and novelty detection

A model of sparse distributed memory is developed that is based on phase relations between the incoming signals and an oscillatory mechanism for information processing. This includes phase-frequency encoding of input information, natural frequency adaptation among the network oscillators for storage of input signals, and a resonance amplification mechanism that responds to familiar stimuli. Simulations of this model show different types of dynamics in response to new and familiar stimuli. The application of the model to hippocampal working memory is discussed.     

Prediction of Interacting Protein Pairs from Sequence Using a Bayesian Method

With the development of bioinformatics, more and more protein sequence information has become available. Meanwhile, the number of known protein–protein interactions (PPIs) is still very limited. In this article, we propose a new method for predicting interacting protein pairs using a Bayesian method based on a new feature representation. We trained our model using data on 6,459 PPI pairs from the yeast Saccharomyces cerevisiae core subset. Using six species of DIP database, our model demonstrates an average prediction accuracy of 93.67%. The result showed that our method is superior to other methods in both computing time and prediction accuracy.     

Representational switching by dynamical reorganization of attractor structure in a network model of the prefrontal cortex

The prefrontal cortex (PFC) plays a crucial role in flexible cognitive behavior by representing task relevant information with its working memory. The working memory with sustained neural activity is described as a neural dynamical system composed of multiple attractors, each attractor of which corresponds to an active state of a cell assembly, representing a fragment of information. Recent studies have revealed that the PFC not only represents multiple sets of information but also switches multiple representations and transforms a set of information to another set depending on a given task context. This representational switching between different sets of information is possibly generated endogenously by flexible network dynamics but details of underlying mechanisms are unclear. Here we propose a dynamically reorganizable attractor network model based on certain internal changes in synaptic connectivity, or short-term plasticity. We construct a network model based on a spiking neuron model with dynamical synapses, which can qualitatively reproduce experimentally demonstrated representational switching in the PFC when a monkey was performing a goal-oriented action-planning task. The model holds multiple sets of information that are required for action planning before and after representational switching by reconfiguration of functional cell assemblies. Furthermore, we analyzed population dynamics of this model with a mean field model and show that the changes in cell assemblies' configuration correspond to those in attractor structure that can be viewed as a bifurcation process of the dynamical system. This dynamical reorganization of a neural network could be a key to uncovering the mechanism of flexible information processing in the PFC.     

ProClass protein family database

ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PIR superfamilies and PROSITE patterns. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database currently consists of >155 000 sequence entries retrieved from both PIR-International and SWISS-PROT databases. Approximately 92 000 or 60% of the ProClass entries are classified into approximately 6000 families, including a large number of new members detected by our GeneFIND family identification system. The ProClass motif collection contains approximately 72 000 motif sequences and >1300 multiple alignments for all PROSITE patterns, including >21 000 matches not listed in PROSITE and mostly detected from unique PIR sequences. To maximize family information retrieval, the database provides links to various protein family, domain, alignment and structural class databases. With its high classification rate and comprehensive family relationships, ProClass can be used to support full-scale genomic annotation. The database, now being implemented in an object-relational database management system, is available for online sequence search and record retrieval from our WWW server at http://pir.georgetown.edu/gfserver/proclass.html