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PubMed2004

Generalization as a behavioral window to the neural mechanisms of learning internal models.

In generating motor commands, the brain seems to rely on internal models that predict physical dynamics of the limb and the external world. How does the brain compute an internal model? Which neural structures are involved? We consider a task where a force field is applied to the hand, altering the physical dynamics of reaching. Behavioral measures suggest that as the brain adapts to the field, it maps desired sensory states of the arm into estimates of force. If this neural computation is performed via a population code, i.e., via a set of bases, then activity fields of the bases dictate a generalization function that uses errors experienced in a given state to influence performance in any other state. The patterns of generalization suggest that the bases have activity fields that are directionally tuned, but directional tuning may be bimodal. Limb positions as well as contextual cues multiplicatively modulate the gain of tuning. These properties are consistent with the activity fields of cells in the motor cortex and the cerebellum. We suggest that activity fields of cells in these motor regions dictate the way we represent internal models of limb dynamics.

Biomechanical Phenomena↗

PubMedDate not supplied

A spike based learning rule for generation of invariant representations.

For biological realism, models of learning in neuronal networks often assume that synaptic plasticity solely depends on locally available signals, in particular only on the activity of the pre- and post-synaptic cells. As a consequence, synapses influence the plasticity of other synapses exclusively via the post-synaptic activity. Inspired by recent research on the properties of apical dendrites it has been suggested, that a second integration site in the apical dendrite may mediate specific global information. Here we explore this issue considering the example of learning invariant responses by examining a network of spiking neurones with two sites of synaptic integration. We demonstrate that results obtained in networks of units with continuous outputs transfer to the more realistic neuronal model. This allows a number of more specific experimental predictions, and is a necessary step to unified description of learning rules exploiting timing of action potentials.

Action Potentials↗

PubMed1999

The effect of multiple binding modes on empirical modeling of ligand docking to proteins.

A popular approach to the computational modeling of ligand/receptor interactions is to use an empirical free energy like model with adjustable parameters. Parameters are learned from one set of complexes, then used to predict another set. To improve these empirical methods requires an independent way to study their inherent errors. We introduce a toy model of ligand/receptor binding as a workbench for testing such errors. We study the errors incurred from the two state binding assumption--the assumption that a ligand is either bound in one orientation, or unbound. We find that the two state assumption can cause large errors in free energy predictions, but it does not affect rank order predictions significantly. We show that fitting parameters using data from high affinity ligands can reduce two state errors; so can using more physical models that do not use the two state assumption. We also find that when using two state models to predict free energies, errors are more severe on high affinity ligands than low affinity ligands. And we show that two state errors can be diagnosed by systematically adding new binding modes when predicting free energies: if predictions worsen as the modes are added, then the two state assumption in the fitting step may be at fault.

Computer Simulation↗

PubMed2004

Cognitive processes in stereotype formation: the role of correct contingency learning for biased group judgments.

Three studies investigated contingency learning and stereotype formation in a scenario about group membership and behavior with a confounding context factor. The studies tested predictions from theoretical accounts of biased group judgments in terms of simplistic reasoning, parallel distributed memory, and pseudocontingencies. Study 1 revealed a positive correlation between erroneous stereotype formation and learning of the true contingencies with the confounding factor. Study 2 showed that a focus manipulation during encoding moderated the correlation between stereotype formation and contingency learning but not the strength of the erroneous stereotype. Study 3 used a quasiexperimental comparison between participants with biased versus unbiased group judgments and extended the findings of a positive relation between stereotype formation and contingency learning. The results support an explanation of biased group judgments by pseudocontingencies; that is, unwarranted inferences from accurately perceived bivariate correlations in complex environments.

Adult↗

PubMedDate not supplied

New approaches to the study of amnesic patients: what can a neurofunctional philosophy and neural network methods offer?

In this paper I first consider a neurofunctional approach to the study of amnesic patients. This approach stresses the need for theorising about the processing operations of brain regions and circuits rather than for theorising about neuropsychological syndromes. A syndrome such as amnesia-may not exist, in any meaningful sense, if there is marked heterogeneity within the patients grouped together in this way. Powerful neuroimaging techniques may now allow a more useful basis for grouping patients in terms of lesion location rather than aetiology. In turn this will allow an evaluation of the information processing functions subserved by the lesioned structures. The second strand to the present paper stresses the weakness in the specification of current theories. This has made it difficult to select experimental tasks that decisively measure the key components of those theories. The paper makes the case that explicit neural network models are a useful way to try to overcome this problem. In line with these ideas, the paper begins to build a model of how the brain may achieve useful kinds of stimulus representations. Considerations of human behaviour in category learning tasks have emphasised parallel and interacting roles for both exemplar- and element-based stimulus representations. It is suggested that the hippocampus itself may encode exemplar representations, and these may provide a basis for episodic memory as well as some types of category learning. It is further suggested that the ventral striatum may encode the element-based representations. The model allows some new and detailed predictions for the performance of amnesic subjects related to lesion location.

Amnesia↗

PubMed2004

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water.

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

Automation↗

PubMedDate not supplied

Actor-critic models of the basal ganglia: new anatomical and computational perspectives.

A large number of computational models of information processing in the basal ganglia have been developed in recent years. Prominent in these are actor-critic models of basal ganglia functioning, which build on the strong resemblance between dopamine neuron activity and the temporal difference prediction error signal in the critic, and between dopamine-dependent long-term synaptic plasticity in the striatum and learning guided by a prediction error signal in the actor. We selectively review several actor-critic models of the basal ganglia with an emphasis on two important aspects: the way in which models of the critic reproduce the temporal dynamics of dopamine firing, and the extent to which models of the actor take into account known basal ganglia anatomy and physiology. To complement the efforts to relate basal ganglia mechanisms to reinforcement learning (RL), we introduce an alternative approach to modeling a critic network, which uses Evolutionary Computation techniques to 'evolve' an optimal RL mechanism, and relate the evolved mechanism to the basic model of the critic. We conclude our discussion of models of the critic by a critical discussion of the anatomical plausibility of implementations of a critic in basal ganglia circuitry, and conclude that such implementations build on assumptions that are inconsistent with the known anatomy of the basal ganglia. We return to the actor component of the actor-critic model, which is usually modeled at the striatal level with very little detail. We describe an alternative model of the basal ganglia which takes into account several important, and previously neglected, anatomical and physiological characteristics of basal ganglia-thalamocortical connectivity and suggests that the basal ganglia performs reinforcement-biased dimensionality reduction of cortical inputs. We further suggest that since such selective encoding may bias the representation at the level of the frontal cortex towards the selection of rewarded plans and actions, the reinforcement-driven dimensionality reduction framework may serve as a basis for basal ganglia actor models. We conclude with a short discussion of the dual role of the dopamine signal in RL and in behavioral switching.

Animals↗

PubMed2005

Assessment of hepatotoxic liabilities by transcript profiling.

Male Wistar rats were treated with various model compounds or the appropriate vehicle controls in order to create a reference database for toxicogenomics assessment of novel compounds. Hepatotoxic compounds in the database were either known hepatotoxicants or showed hepatotoxicity during preclinical testing. Histopathology and clinical chemistry data were used to anchor the transcript profiles to an established endpoint (steatosis, cholestasis, direct acting, peroxisomal proliferation or nontoxic/control). These reference data were analyzed using a supervised learning method (support vector machines, SVM) to generate classification rules. This predictive model was subsequently used to assess compounds with regard to a potential hepatotoxic liability. A steatotic and a non-hepatotoxic 5HT(6) receptor antagonist compound from the same series were successfully discriminated by this toxicogenomics model. Additionally, an example is shown where a hepatotoxic liability was correctly recognized in the absence of pathological findings. In vitro experiments and a dog study confirmed the correctness of the toxicogenomics alert. Another interesting observation was that transcript profiles indicate toxicologically relevant changes at an earlier timepoint than routinely used methods. Together, these results support the useful application of toxicogenomics in raising alerts for adverse effects and generating mechanistic hypotheses that can be followed up by confirmatory experiments.

Animals↗

PubMed2001

Sibling influences on gender development in middle childhood and early adolescence: a longitudinal study.

The development of gender role qualities (attitudes, personality, leisure activities) from middle childhood to early adolescence was studied to determine whether siblings' gender role qualities predicted those of their sisters and brothers. Participants were 198 firstborn and second-born siblings (Ms = 10 years 9 months and 8 years 3 months, respectively, in Year 1) and their parents. Families were interviewed annually for 3 years. Firstborn siblings' qualities in Year 1 predicted second-born children's qualities in Year 3 when both parent and child qualities in Year 1 were controlled, a pattern consistent with a social learning model of sibling influence. Parental influence was more evident and sibling influence less evident in predicting firstborns' qualities; for firstborns, sibling influences suggested a de-identification process.

Adolescent↗

PubMed2003

CS-dependent response probability in an auditory masked-detection task: considerations based on models of Pavlovian conditioning.

Experimental studies were performed using a Pavlovian-conditioned eyeblink response to measure detection of a variable-sound-level tone (T) in a fixed-sound-level masking noise (N) in rabbits. Results showed an increase in the asymptotic probability of conditioned responses (CRs) to the reinforced TN trials and a decrease in the asymptotic rate of eyeblink responses to the non-reinforced N presentations as a function of the sound level of the T. These observations are consistent with expected behaviour in an auditory masked detection task, but they are not consistent with predictions from a traditional application of the Rescorla-Wagner or Pearce models of associative learning. To implement these models, one typically considers only the actual stimuli and reinforcement on each trial. We found that by considering perceptual interactions and concepts from signal detection theory, these models could predict the CS dependence on the sound level of the T. In these alternative implementations, the animals response probabilities were used as a guide in making assumptions about the "effective stimuli".

Animals↗

PubMed2004

Learning and strain among newcomers: a three-wave study on the effects of job demands and job control.

The present 3-wave longitudinal study was an examination of job-related learning and strain as a function of job demand and job control. The participants were 311 newcomers to their jobs. On the basis of R. A. Karasek and T. Theorell's (1990) demand-control model, the authors predicted that high demand and high job control would lead to high levels of learning; low demand and low job control should lead to low levels of learning; high demand and low job control should lead to high levels of strain; and low demand and high job control should lead to low levels of strain. The relation between strain and learning was also examined. The authors tested the hypotheses using ANCOVA and structural equation modeling. The results revealed that high levels of strain have an adverse effect on learning; the reverse effect was not confirmed. It appears that Karasek and Theorell's model is very relevant when examining work socialization processes.

Adult↗

PubMed1998

A model of activity-dependent formation of cerebellar microzones.

According to modern views of the cerebellum in motor control, each cerebellar functional unit, or microzone, learns how to execute predictive and coordinative control, based on long-term depression of the granule cell-Purkinje cell synapses. In the present paper, in light of recent experimental and theoretical studies on synaptic elimination and cerebellar motor learning, a model of the formation of cerebellar microzones by climbing fiber synaptic elimination is proposed. It is shown that competition for an activity-dependent supply of neurotrophic factor can reproduce the spatio-temporal characteristics of climbing fiber synaptic elimination. It is further shown that when this elimination is accurate, motor coordination can be acquired in an arm reaching task. In view of the results of the present study, several predictions are proposed.

Adult↗

PubMed1999

Empirical versus mechanistic modelling: comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates.

The aim of the current study was to compare the predictive performance of a mechanistically based model and an empirical artificial neural network (ANN) model to describe the relationship between the tissue-to-unbound plasma concentration ratios (Kpu's) of 14 rat tissues and the lipophilicity (LogP) of a series of nine 5-n-alkyl-5-ethyl barbituric acids. The mechanistic model comprised the water content, binding capacity, number of the binding sites, and binding association constant of each tissue. A backpropagation ANN with 2 hidden layers (33 neurons in the first layer, 9 neurons in the second) was used for the comparison. The network was trained by an algorithm with adaptive momentum and learning rate, programmed using the ANN Toolbox of MATLAB. The predictive performance of both models was evaluated using a leave-one-out procedure and computation of both the mean prediction error (ME, showing the prediction bias) and the mean squared prediction error (MSE, showing the prediction accuracy). The ME of the mechanistic model was 18% (range, 20 to 57%), indicating a tendency for overprediction; the MSE is 32% (range, 6 to 104%). The ANN had almost no bias: the ME was 2% (range, 36 to 64%) and had greater precision than the mechanistic model, MSE 18% (range, 4 to 70%). Generally, neither model appeared to be a significantly better predictor of the Kpu's in the rat.

Algorithms↗

PubMed2024

Major depletion of insulin sensitivity-associated taxa in the gut microbiome of persons living with HIV controlled by antiretroviral drugs.

BACKGROUND: Persons living with HIV (PWH) harbor an altered gut microbiome (higher abundance of Prevotella and lower abundance of Bacillota and Ruminococcus lineages) compared to non-infected individuals. Some of these alterations are linked to sexual preference and others to the HIV infection. The relationship between these lineages and metabolic alterations, often present in aging PWH, has been poorly investigated. METHODS: In this study, we compared fecal metagenomes of 25 antiretroviral-treatment (ART)-controlled PWH to three independent control groups of 25 non-infected matched individuals by means of univariate analyses and machine learning methods. Moreover, we used two external datasets to validate predictive models of PWH classification. Next, we searched for associations between clinical and biological metabolic parameters with taxonomic and functional microbiome profiles. Finally, we compare the gut microbiome in 7 PWH after a 17-week ART switch to raltegravir/maraviroc. RESULTS: Three major enterotypes (Prevotella, Bacteroides and Ruminococcaceae) were present in all groups. The first Prevotella enterotype was enriched in PWH, with several of characteristic lineages associated with poor metabolic profiles (low HDL and adiponectin, high insulin resistance (HOMA-IR)). Conversely butyrate-producing lineages were markedly depleted in PWH independently of sexual preference and were associated with a better metabolic profile (higher HDL and adiponectin and lower HOMA-IR). Accordingly with the worst metabolic status of PWH, butyrate production and amino-acid degradation modules were associated with high HDL and adiponectin and low HOMA-IR. Random Forest models trained to classify PWH vs. control on taxonomic abundances displayed high generalization performance on two external holdout datasets (ROC AUC of 80-82%). Finally, no significant alterations in microbiome composition were observed after switching to raltegravir/maraviroc. CONCLUSION: High resolution metagenomic analyses revealed major differences in the gut microbiome of ART-controlled PWH when compared with three independent matched cohorts of controls. The observed marked insulin resistance could result both from enrichment in Prevotella lineages, and from the depletion in species producing butyrate and involved into amino-acid degradation, which depletion is linked with the HIV infection.

Humans↗

PubMed2026

Toward AI Virtual Cells for Hepatology: Representation, Generation, Dynamics, and Intervention in Single-Cell Models.

``Single-cell and spatial atlases describe the healthy and diseased liver at high resolution, including lobular hepatocyte zonation, fibrotic macrophage-stellate niches, cholangiocyte reactions, immune remodeling, and hepatocellular carcinoma ecosystems. These maps show where cell states occur but do not, by themselves, predict whether liver injury will progress or how the liver will respond to an untested drug, toxicant, or genetic perturbation. In this review, we organize current approaches toward an AI Virtual Cell (AIVC) for the liver into three complementary modeling routes. Generative models represent cell states, dynamics and transport models infer state transitions, and pretrained or foundation models test whether learned representations transfer across donors, etiologies, disease stages, and platforms. Perturbation-response prediction serves as a cross-cutting assessment of whether these layers can predict responses to untested genetic, chemical, inflammatory, or metabolic interventions. Available evidence can be categorized as direct liver validation, liver-included benchmarks, general single-cell evidence, and conceptual applications. Published models demonstrate individual components, including atlas integration, inferred trajectories, transferable representations, and retrospective response programs. However, these models do not constitute a prospectively validated liver simulator. At minimum, evaluation should include donor-, etiology-, stage-, platform-, and perturbation-level hold-outs. Model performance should be reported using response direction, recovery of differentially expressed genes and rare states, and calibrated uncertainty. Claims about tissue- or function-level prediction additionally require independent spatial, histologic, metabolic, and functional readouts. Near-term use should prioritize experiment selection and hypothesis generation, whereas clinical decision support remains a longer-term objective.

AI Virtual Cell↗

PubMed2006

Early childhood diarrhea predicts impaired school performance.

OBJECTIVE: Diarrhea is a leading cause of mortality worldwide; however, its long-term morbidity is poorly understood. Recently, early childhood diarrhea (ECD) has been associated with impaired physical fitness, growth and cognitive function 6 to 9 years later. We studied the effects of ECD on school functioning in a shantytown in northeastern Brazil. DESIGN: We administered 77 educational surveys. Complete diarrhea surveillance (ie, >90%) in the first 2 years of life and demographic and anthropometric information were available for 73 children. Age at starting school was calculated for 62 children, whereas age appropriateness for the current grade (AFG) was calculated for all 73 children who were >6 years old. Stepwise regression was used to examine the independent effect of ECD on school functioning after controlling for socioeconomic factors, maternal education, breast feeding, growth and cognitive functioning. RESULTS: ECD correlated with age at starting school (r = 0.55, P = 0.0005) and remained a significant predictor even after controlling for family demographics, days of breast feeding, early growth and TONI-3 test of nonverbal intelligence. This was true despite significant correlations of ECD with growth shortfalls and impaired cognitive functioning. ECD also correlated with AFG (r = 0.38, P = 0.001). Only TONI-3 test scores explained this association, suggesting that ECD may hinder school performance, but only in part school readiness, by impairing cognitive function as measured by performance on the TONI-3 nonverbal intelligence test. CONCLUSIONS: These findings document effects of early childhood diarrhea on later school readiness and performance and hence potential long-term human and economic costs of ECD, which warrant further attention and far greater investment for the control of ECD and its consequences.

Age Factors↗

PubMed2006

Linking proactive personality and the Big Five to motivation to learn and development activity.

The authors investigated links between the Big Five, proactive personality, and motivation to learn. Web-based survey data were collected at 2 points in time from 183 employees of a financial services firm. Results showed that proactive personality was, only in part, a composite of Big Five facets, which accounted for 26% of its variance. Structural equation modeling results demonstrated that proactive personality, openness, extraversion, and conscientiousness predicted motivation to learn. In addition, motivation to learn was positively related to objectively assessed development activity. Proactive personality, extraversion, and openness had significant indirect links to development activity. Hierarchical regression results suggested that proactive personality had significant incremental validity in the prediction of motivation to learn over all relevant Big Five facets.

Adult↗

PubMed2025

Sparse deconvolution of cell type medleys in spatial transcriptomics.

Mapping cell distributions across spatial locations with whole-genome coverage is essential for understanding cellular responses and signaling However, current deconvolution models aim to estimate the proportions of distinct cell types in each spatial transcriptomics spot by integrating reference single-cell data. These models often assume strong overlap between the reference and spatial datasets, neglecting biology-grounded constraints such as sparsity and cell-type variations, as well as technical sparsity. As a result, these methods rely on over-permissive algorithms that ignore given constraints leading to inaccurate predictions, particularly in heterogeneous or unmatched datasets. We introduce Weight-Induced Sparse Regression (WISpR), a machine learning algorithm that integrates spot-specific hyperparameters and sparsity-driven modeling. Unlike conventional approaches that neglect biology-grounded constraints, WISpR accurately predicts cell-type distributions while preserving biological coherence, i.e., spatially and functionally consistent cell-type localization, even in unmatched datasets. Benchmarking against five alternative methods across ten datasets, WISpR consistently outperformed competitors and predicted cellular landscapes in both normal and cancerous tissues. By leveraging sparse cell-type arrangements, WISpR provides biologically informed, high-resolution cellular maps. Its ability to decode tissue organization in both healthy and diseased states highlights WISpR's practical utility for spatial transcriptomics, particularly in challenging settings involving noise, sparsity, or reference mismatches.

Humans↗