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At least 1,549 records · Page 86Linked to original sources
PubMed2006

Short-term memory for serial order: a recurrent neural network model.

Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order.

Artificial Intelligence↗
PubMed2026

GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.

Genetic interactions (GIs) underpin the functional connectivity of genes and pathways, and are important for dissecting genotype-phenotype relationships and identifying therapeutic targets for diseases. However, the scale of the human genome restricts systematic experimental interrogation of GIs. Existing computational tools focus on predicting synthetic lethality (SL) and synthetic viability (SV), the two primary forms of GIs, yet their accuracy and biological interpretability are compromised by inadequate modeling of the molecular mechanisms behind positive and negative interactions, as well as the limitation of negative samples. To overcome these challenges, we developed Genetic Interaction Graph Convolutional Network (GiGCN), a signed network modeling framework for the joint identification of gene pairs with SL and SV. We built a high-confidence signed genetic network by integrating verified GIs, and non-interacting gene pairs, together with gene semantic similarity derived from biological processes. By leveraging disentangled subspace decomposition, this framework separately models distinct functional dimensions within gene networks, enabling robust representation of context-dependent regulatory relationships and accurate discrimination of SL and SV events. Benchmark experiments demonstrate that GiGCN outperforms state-of-the-art approaches (area under receiver operating-characteristic curve: 0.978, and area under precision-recall curve: 0.944). Further analyses reveal biologically meaningful insights, including known and novel SL interactions centered on the oncogene MYC Proto-Oncogene (MYC), as well as SV interactions linked to autophagy and mitophagy pathways. This study provides a robust and interpretable network-based strategy for systematically exploring GIs. The GiGCN framework not only improves the precision of SL and SV prediction, but also offers mechanistic insights into gene functional relationships, thereby supporting the discovery of actionable therapeutic targets for cancer and other human diseases.

Humans↗
PubMed2006

A recurrent network mechanism of time integration in perceptual decisions.

Recent physiological studies using behaving monkeys revealed that, in a two-alternative forced-choice visual motion discrimination task, reaction time was correlated with ramping of spike activity of lateral intraparietal cortical neurons. The ramping activity appears to reflect temporal accumulation, on a timescale of hundreds of milliseconds, of sensory evidence before a decision is reached. To elucidate the cellular and circuit basis of such integration times, we developed and investigated a simplified two-variable version of a biophysically realistic cortical network model of decision making. In this model, slow time integration can be achieved robustly if excitatory reverberation is primarily mediated by NMDA receptors; our model with only fast AMPA receptors at recurrent synapses produces decision times that are not comparable with experimental observations. Moreover, we found two distinct modes of network behavior, in which decision computation by winner-take-all competition is instantiated with or without attractor states for working memory. Decision process is closely linked to the local dynamics, in the "decision space" of the system, in the vicinity of an unstable saddle steady state that separates the basins of attraction for the two alternative choices. This picture provides a rigorous and quantitative explanation for the dependence of performance and response time on the degree of task difficulty, and the reason for which reaction times are longer in error trials than in correct trials as observed in the monkey experiment. Our reduced two-variable neural model offers a simple yet biophysically plausible framework for studying perceptual decision making in general.

Animals↗
PubMedDate not supplied

The lipid world.

The continuity of abiotically formed bilayer membranes with similar structures in contemporary cellular life, and the requirement for microenvironments in which large and small molecules could be compartmentalized, support the idea that amphiphilic boundary structures contributed to the emergence of life. As an extension of this notion, we propose here a 'Lipid World' scenario as an early evolutionary step in the emergence of cellular life on Earth. This concept combines the potential chemical activities of lipids and other amphiphiles, with their capacity to undergo spontaneous self-organization into supramolecular structures such as micelles and bilayers. In particular, the documented chemical rate enhancements within lipid assemblies suggest that energy-dependent synthetic reactions could lead to the growth and increased abundance of certain amphiphilic assemblies. We further propose that selective processes might act on such assemblies, as suggested by our computer simulations of mutual catalysis among amphiphiles. As demonstrated also by other researchers, such mutual catalysis within random molecular assemblies could have led to a primordial homeostatic system displaying rudimentary life-like properties. Taken together, these concepts provide a theoretical framework, and suggest experimental tests for a Lipid World model for the origin of life.

Catalysis↗
PubMed1999

A unified reconstruction framework for both parallel-beam and variable focal-length fan-beam collimators by a Cormack-type inversion of exponential radon transform.

A variety of inversions of exponential Radon transform has been derived based on the circular harmonic transform in Fourier space by several research groups. However, these inversions cannot be directly applied to deal with the reconstruction for fan-beam or variable-focal-length fan-beam collimator geometries in single photon emission computed tomography (SPECT). In this paper, we derived a Cormack-type inversion of the exponential Radon transform by employing the circular harmonic transform directly in the projection space and the image space instead of the Fourier space. Thus, a unified reconstruction framework is established for parallel-, fan-, and variable-focal-length fan-beam collimator geometries. Compared to many existing algorithms, the presented one greatly mitigates the difficulty of image reconstruction due to the complicated collimator geometry and significantly reduces the computational burden of the special functions, such as Chebyshev or Bessel functions. By the well-established fast-Fourier transform (FFT), our algorithm is very efficient, as demonstrated by several numerical simulations.

Algorithms↗
PubMed1989

Modeling the olfactory bulb and its neural oscillatory processings.

The olfactory bulb of mammals aids in the discrimination of odors. A mathematical model based on the bulbar anatomy and electrophysiology is described. Simulations of the highly non-linear model produce a 35-60 Hz modulated activity which is coherent across the bulb. The decision states (for the odor information) in this system can be thought of as stable cycles, rather than point stable states typical of simpler neuro-computing models. Analysis shows that a group of coupled non-linear oscillators are responsible for the oscillatory activities. The output oscillation pattern of the bulb is determined by the odor input. The model provides a framework in which to understand the transform between odor input and the bulbar output to olfactory cortex. There is significant correspondence between the model behavior and observed electrophysiology.

Computer Simulation↗
PubMedDate not supplied

Computed tomography in the measurement of femoral anteversion.

A new technique using computed tomography for measuring femoral anteversion is presented, with advantages over prior techniques. Computed tomography allows the visual portrayal of the cervical axis to be superimposed upon the diacondylar axis for direct goniometric measurement of the anteversion angle. Beyond its accuracy, the procedure requires no complicated positioning framework and can be done in a brief span of time.

Child, Preschool↗
PubMed1998

A computational model of how the basal ganglia produce sequences.

We propose a systems-level computational model of the basal ganglia based closely on known anatomy and physiology. First, we assume that the thalamic targets, which relay ascending information to cortical action and planning areas, are tonically inhibited by the basal ganglia. Second, we assume that the output stage of the basal ganglia, the internal segment of the globus pallidus (Gpi), selects a single action from several competing actions via lateral interactions. Third, we propose that a form of local working memory exists in the form of reciprocal connections between the external globus pallidus (Gpe) and the subthalamic nucleus (STN). As a test of the model, the system was trained to learn a sequence of states that required the context of previous actions. The striatum, which was assumed to represent a conjunction of cortical states, directly selected the action in the GP during training. The STN-to-GP connection strengths were modified by an associative learning rule and came to encode the sequence after 20 to 40 iterations through the sequence. Subsequently, the system automatically reproduced the sequence when cued to the first action. The behavior of the model was found to be sensitive to the ratio of the striatal-nigral learning rate to the STN-GP learning rate. Additionally, the degree of striatal inhibition of the globus pallidus had a significant influence on both learning and the ability to select an action. Low learning rates, which would be hypothesized to reflect low levels of dopamine, as in Parkinson's disease, led to slow acquisition of contextual information. However, this could be partially offset by modeling a lesion of the globus pallidus that resulted in an increase in the gain of the STN units. The parameter sensitivity of the model is discussed within the framework of existing behavioral and lesion data.

Basal Ganglia↗
PubMed2006

Toward a prefrontal microcircuit model for cognitive deficits in schizophrenia.

I present here a biophysically-based model of cortical microcircuits capable of both internal representation (memory storage) and dynamical processing (decision and action selection). The model is illustrated through computer simulations that account for neurophysiological and behavioral data from studies using nonhuman primates. This computational theory proposes that an interplay between slow reverberating excitation and competitive synaptic inhibition enables a cortical area, such as the prefrontal cortex, to subserve cognitive functions. It is argued that quantitatively accurate microcircuit models can potentially provide a framework for a systematic approach to pharmacological treatment of schizophrenia and other mental disorders.

Animals↗
PubMed2003

Towards a theory of the laminar architecture of cerebral cortex: computational clues from the visual system.

One of the most exciting and open research frontiers in neuroscience is that of seeking to understand the functional roles of the layers of cerebral cortex. New experimental techniques for probing the laminar circuitry of cortex have recently been developed, opening up novel opportunities for investigating how its six-layered architecture contributes to perception and cognition. The task of trying to interpret this complex structure can be facilitated by theoretical analyses of the types of computations that cortex is carrying out, and of how these might be implemented in specific cortical circuits. We have recently developed a detailed neural model of how the parvocellular stream of the visual cortex utilizes its feedforward, feedback and horizontal interactions for purposes of visual filtering, attention and perceptual grouping. This model, called LAMINART, shows how these perceptual processes relate to the mechanisms that ensure the stable development of cortical circuits in the infant, and to the continued stability of learning in the adult. The present article reviews this laminar theory of visual cortex, considers how it may be generalized towards a more comprehensive theory that encompasses other cortical areas and cognitive processes, and shows how its laminar framework generates a variety of testable predictions.

Adaptation, Physiological↗
PubMed1998

Sequence, structural, functional, and phylogenetic analyses of three glycosidase families.

Glycosidases, which cleave the glycosidic bond between a carbohydrate and another moiety, have been classified into over 63 families. Here, a variety of computational techniques have been employed to examine three families important in normal and abnormal pathology with the aim of developing a framework for future homology modeling, experimental and other studies. Family 1 includes bacterial and archaeal enzymes as well as lactase phlorizin-hydrolase and klotho, glycosidases implicated in disaccharide intolerance II and aging respectively. A statistical model, a hidden Markov model (HMM), for the family 1 glycosidase domain was trained and used as the basis for comparative examination of the conserved and variable sequence and structural features as well as the phylogenetic relationships between family members. Although the structures of four family 1 glycosidases have been determined, this is the first comparative examination of all these enzymes. Aspects that are unique to specific members or subfamilies (substrate binding loops) as well those common to all members (a beta/alpha)8 barrel fold) have been defined. Active site residues in some domains in klotho and lactase-phlorizin hydrolases differ from other members and in one instance may bind but not cleave substrate. The four invariant and most highly conserved residues are not residues implicated in catalysis and/or substrate binding. Of these, a histidine may be involved in transition state stabilization. Glucosylceramidase (family 30) and galactosylceramidase (family 59) are mutated in the lysosomal storage disorders Gaucher disease and Krabbe disease, respectively. HMM-based analysis, structure prediction studies and examination of disease mutations reveal a glycosidase domain common to these two families that also occurs in some bacterial glycosidases. Similarities in the reactions catalyzed by families 30 and 59 are reflected in the presence of a structurally and functionally related (beta/alpha)8 barrel fold related to that in family 1.

Amino Acid Sequence↗
PubMed2005

Stochastic correlative firing for figure-ground segregation.

Segregation of sensory inputs into separate objects is a central aspect of perception and arises in all sensory modalities. The figure-ground segregation problem requires identifying an object of interest in a complex scene, in many cases given binaural auditory or binocular visual observations. The computations required for visual and auditory figure-ground segregation share many common features and can be cast within a unified framework. Sensory perception can be viewed as a problem of optimizing information transmission. Here we suggest a stochastic correlative firing mechanism and an associative learning rule for figure-ground segregation in several classic sensory perception tasks, including the cocktail party problem in binaural hearing, binocular fusion of stereo images, and Gestalt grouping in motion perception.

Acoustic Stimulation↗
PubMed2006

A modular framework for development and interlaboratory sharing and validation of diffusion tensor tractography algorithms.

This Technical Note describes a novel modular framework for development and interlaboratory distribution and validation of 3D tractography algorithms based on in vivo diffusion tensor imaging (DTI) measurements. The proposed framework allows individual MRI research centers to benefit from new tractography algorithms developed at other independent centers by "plugging" new tractography modules directly into their own custom DTI software tools, such as existing graphical user interfaces (GUI) for visualizing brain white matter pathways. The proposed framework is based on the Java 3D programming platform, which provides an object-oriented programming (OOP) model and independence of computer hardware configuration and operating system. To demonstrate the utility of the proposed approach, a complete GUI for interactive DTI tractography was developed, along with two separate and interchangeable modules that implement two different tractography algorithms. Although the application discussed here relates to DTI tractography, the programming concepts presented here should be of interest to anyone who wishes to develop platform-independent GUI applications for interactive 3D visualization.

Algorithms↗
PubMed2005

Estimating immunoregulatory gene networks in human herpesvirus type 6-infected T cells.

The immune response to viral infection involves complex network of dynamic gene and protein interactions. We present here the dynamic gene network of the host immune response during human herpesvirus type 6 (HHV-6) infection in an adult T-cell leukemia cell line. Using a pathway-focused oligonucleotide DNA microarray, we found a possible association between chemokine genes regulating Th1/Th2 balance and genes regulating T-cell proliferation during HHV-6B infection. Gene network analysis using an integrated comprehensive workbench, VoyaGene, revealed that a gene encoding a TEC-family kinase, ITK, might be a putative modulator in the host immune response against HHV-6B infection. We conclude that Th2-dominated inflammatory reaction in host cells may play an important role in HHV-6B-infected T cells, thereby suggesting the possibility that ITK might be a therapeutic target in diseases related to dysregulation of Th1/Th2 balance. This study describes a novel approach to find genes related with the complex host-virus interaction using microarray data employing the Bayesian statistical framework.

Adult↗
PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗
PubMed2005

Non-clinical safety evaluation of novel vaccines and adjuvants: new products, new strategies.

Advances in molecular biology and biotechnology, coupled with an increased understanding of disease processes and mechanisms of protective immunity have facilitated the development of new rationally-designed vaccines utilising recombinant proteins, naked DNA, live vectors, genetically-modified toxins and whole dendritic and tumour cells for both prophylaxis and therapy of a wide range of indications. These new vaccine technologies coupled with novel adjuvants, delivery systems, formulations, dosing routes and regimes present many unique and difficult challenges in demonstrating product safety and efficacy to support clinical testing. This paper aims to review these novel vaccine and adjuvant technologies and to highlight the key safety issues potentially associated with them. Approaches taken to demonstrate vaccine safety by assessing systemic and local toxicity, biodistribution and persistence, immunogenicity and immunotoxicity, reproductive toxicology, safety pharmacology and genotoxicity within the current regulatory framework are presented.

Adjuvants, Immunologic↗
PubMed1997

Epidemics in a population with social structures.

We consider the long-time behavior of the simplest possible epidemic in a large homogeneous population that is not necessarily homogeneously mixing. Rather each individual is a member of a number of social structures. In a special case we compute the basic reproduction number, and we observe a threshold behavior as the population grows. The model is described in the framework of random graphs.

Communicable Diseases↗
PubMed1999

Stereoscopic surface perception.

Physiological, computational, and psychophysical studies of stereopsis have assumed that the perceived surface structure of binocularly viewed images is primarily specified by the pattern of binocular disparities in the two eyes' views. A novel set of stereoscopic phenomena are reported that demonstrate the insufficiency of this view. It is shown that the visual system computes the contrast relationships along depth discontinuities to infer the depth, lightness, and opacity of stereoscopically viewed surfaces. A novel theoretical framework is introduced to explain these results. It is argued that the visual system contains mechanisms that enforce two principles of scene interpretation: a generic view principle that determines qualitative scene geometry, and anchoring principles that determine how image data are quantitatively partitioned between different surface attributes.

Algorithms↗