Minimal Computational Model Research Articles

Formation of Opioid-Induced Memory and Its Prevention: A Computational Study.

There are several experimental studies which suggest opioids consumption forms pathological memories in different brain regions. For example it has been empirically demonstrated that the theta rhythm which appears during chronic opioid consumption is correlated with the addiction memory formation. In this paper, we present a minimal computational model that shows how opioids can change firing patterns of the neurons during acute and chronic opioid consumption and also during withdrawal periods. The model consists of a pre- and post-synaptic neuronal circuits and the astrocyte that monitors the synapses. The output circuitry consists of inhibitory interneurons and excitatory pyramidal neurons. Our simulation results demonstrate that acute opioid consumption induces synchronous patterns in the beta frequency range, while, chronic opioid consumption provokes theta frequency oscillations. This allows us to infer that the theta rhythm appeared during chronic treatment can be an indication of brain engagement in opioid-induced memory formation. Our results also suggest that changing the inputs of the interneurons and the inhibitory neuronal network is not an appropriate method for preventing the formation of pathological memory. However, the same results suggest that prevention of pathological memory formation is possible by manipulating the input of the stimulatory network and the excitatory connections in the neuronal network. They also show that during withdrawal periods, firing rate is reduced and random fluctuations are generated in the modeled neural network. The random fluctuations disappear and synchronized patterns emerge when the activities of the astrocytic transporters are decreased. These results suggest that formation of the synchronized activities can be correlated with the relapse. Our model also predicts that reduction in gliotransmitter release can eliminate the synchrony and thereby it can reduce the likelihood of the relapse occurrence.

Criticality predicts maximum irregularity in recurrent networks of excitatory nodes.

A rigorous understanding of brain dynamics and function requires a conceptual bridge between multiple levels of organization, including neural spiking and network-level population activity. Mounting evidence suggests that neural networks of cerebral cortex operate at a critical regime, which is defined as a transition point between two phases of short lasting and chaotic activity. However, despite the fact that criticality brings about certain functional advantages for information processing, its supporting evidence is still far from conclusive, as it has been mostly based on power law scaling of size and durations of cascades of activity. Moreover, to what degree such hypothesis could explain some fundamental features of neural activity is still largely unknown. One of the most prevalent features of cortical activity in vivo is known to be spike irregularity of spike trains, which is measured in terms of the coefficient of variation (CV) larger than one. Here, using a minimal computational model of excitatory nodes, we show that irregular spiking (CV > 1) naturally emerges in a recurrent network operating at criticality. More importantly, we show that even at the presence of other sources of spike irregularity, being at criticality maximizes the mean coefficient of variation of neurons, thereby maximizing their spike irregularity. Furthermore, we also show that such a maximized irregularity results in maximum correlation between neuronal firing rates and their corresponding spike irregularity (measured in terms of CV). On the one hand, using a model in the universality class of directed percolation, we propose new hallmarks of criticality at single-unit level, which could be applicable to any network of excitable nodes. On the other hand, given the controversy of the neural criticality hypothesis, we discuss the limitation of this approach to neural systems and to what degree they support the criticality hypothesis in real neural networks. Finally, we discuss the limitations of applying our results to real networks and to what degree they support the criticality hypothesis.

Programming the mechanics of cohesive fiber networks by compression.

Fibrous networks are ideal functional materials since they provide mechanical rigidity at low weight. Here, we demonstrate that fibrous networks of the blood clotting protein fibrin undergo a strong and irreversible increase in their mechanical rigidity in response to uniaxial compression. This rigidification can be precisely controlled by the level of applied compressive strain, providing a means to program the network rigidity without having to change its composition. To identify the underlying mechanism we measure single fiber-fiber interactions using optical tweezers. We further develop a minimal computational model of cohesive fiber networks that shows that stiffening arises due to the formation of new bonds in the compressed state, which develop tensile stress when the network is re-expanded. The model predicts that the network stiffness after a compression cycle obeys a power-law dependence on tensile stress, which we confirm experimentally. This finding provides new insights into how biological tissues can adapt themselves independently of any cellular processes, offering new perspectives to inspire the design of reprogrammable materials.

Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling.

The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioral metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioral preference task. Our hypothesis is that, under some conditions, the metastability of the system is not restricted to the brain but extends to the system composed by the interaction of brain, body and environment. We test this idea, comparing an agent in continuous interaction with its environment in a task demanding behavioral flexibility with an equivalent model from the point of view of “internalist neuroscience.” A statistical characterization of our model and tools from information theory allow us to show how (1) the bidirectional coupling between agent and environment brings the system closer to a regime of criticality and triggers the emergence of additional metastable states which are not found in the brain in isolation but extended to the whole system of sensorimotor interaction, (2) the synaptic plasticity of the agent is fundamental to sustain open structures in the neural controller of the agent flexibly engaging and disengaging different behavioral patterns that sustain sensorimotor metastable states, and (3) these extended metastable states emerge when the agent generates an asymmetrical circular loop of causal interaction with its environment, in which the agent responds to variability of the environment at fast timescales while acting over the environment at slow timescales, suggesting the constitution of the agent as an autonomous entity actively modulating its sensorimotor coupling with the world. We conclude with a reflection about how our results contribute in a more general way to current progress in neuroscientific research.

Control centric framework for model continuity in time‐dependent multi‐agent systems

SummaryThis work centres on a control framework for general multi‐agent systems, which separates the design of agents behaviours (the application layer) from crosscutting control concerns (the control layer) regulating message exchange and processing. The goal is to support modelling and execution of a multi‐agent system whose evolution is transparently governed by a pluggable control structure. A library of different control structures, including pure‐concurrent and time‐sensitive (real‐time and simulation) strategies, was developed. The paper describes the developed control framework and focusses on the achievement of control strategies compliant with agent mobility and resource availability. The control design rests on a minimal actor computational model extended with actions, which are independent computational tasks able to execute in parallel. The approach enables model continuity, that is, the same model is used from analysis by simulation to implementation and real‐time execution. The framework is prototyped on top of the JADE agent infrastructure. Usability and effectiveness of the resultant approach are demonstrated by a case study based on a complex closed queue network of services. On‐going and future work is finally pointed out in the conclusions. Copyright © 2016 John Wiley & Sons, Ltd.

A comprehensive model to predict mitotic division in budding yeasts.

High-fidelity chromosome segregation during cell division depends on a series of concerted interdependent interactions. Using a systems biology approach, we built a robust minimal computational model to comprehend mitotic events in dividing budding yeasts of two major phyla: Ascomycota and Basidiomycota. This model accurately reproduces experimental observations related to spindle alignment, nuclear migration, and microtubule (MT) dynamics during cell division in these yeasts. The model converges to the conclusion that biased nucleation of cytoplasmic microtubules (cMTs) is essential for directional nuclear migration. Two distinct pathways, based on the population of cMTs and cortical dyneins, differentiate nuclear migration and spindle orientation in these two phyla. In addition, the model accurately predicts the contribution of specific classes of MTs in chromosome segregation. Thus we present a model that offers a wider applicability to simulate the effects of perturbation of an event on the concerted process of the mitotic cell division.

Generalized periodic discharges: Pathophysiology and clinical considerations.

Generalized periodic discharges (GPDs) are commonly encountered in metabolic encephalopathy and cerebral hypoxia/ischemia. The clinical significance of this EEG pattern is indistinct, and it is unclear whether treatment with antiepileptic drugs is beneficial. In this study, we discuss potential pathophysiological mechanisms. Based on the literature, supplemented with simulations in a minimal computational model, we conclude that selective synaptic failure or neuronal damage of inhibitory interneurons, leading to disinhibition of excitatory pyramidal cells, presumably plays a critical role. Reversibility probably depends on the potential for functional recovery of these interneurons. Whether antiepileptic drugs are helpful for regaining function is unclear. This article is part of a Special Issue entitled "Status Epilepticus".

Infraslow EEG activity modulates cortical excitability in postanoxic encephalopathy.

Infraslow activity represents an important component of physiological and pathological brain function. We study infraslow activity (<0.1 Hz) in 41 patients with postanoxic coma after cardiac arrest, including the relationship between infraslow activity and EEG power in the 3-30 Hz range, using continuous full-band scalp EEG. In all patients, infraslow activity (0.015-0.06 Hz) was present, irrespective of neurological outcome or EEG activity in the conventional frequency bands. In two patients, low-amplitude (10-30 μV) infraslow activity was present while the EEG showed no rhythmic activity above 0.5 Hz. In 13/15 patients with a good outcome and 20/26 patients with a poor one, EEG power in the 3-30 Hz frequency range was correlated with the phase of infraslow activity, quantified by the modulation index. In 9/14 patients with burst-suppression with identical bursts, bursts appeared in clusters, phase-locked to the infraslow oscillations. This is substantiated by a simulation of burst-suppression in a minimal computational model. Infraslow activity is preserved in postanoxic encephalopathy and modulates cortical excitability. The strongest modulation is observed in patients with severe postanoxic encephalopathy and burst-suppression with identical bursts.

Spinal circuits can accommodate interaction torques during multijoint limb movements.

The dynamic interaction of limb segments during movements that involve multiple joints creates torques in one joint due to motion about another. Evidence shows that such interaction torques are taken into account during the planning or control of movement in humans. Two alternative hypotheses could explain the compensation of these dynamic torques. One involves the use of internal models to centrally compute predicted interaction torques and their explicit compensation through anticipatory adjustment of descending motor commands. The alternative, based on the equilibrium-point hypothesis, claims that descending signals can be simple and related to the desired movement kinematics only, while spinal feedback mechanisms are responsible for the appropriate creation and coordination of dynamic muscle forces. Partial supporting evidence exists in each case. However, until now no model has explicitly shown, in the case of the second hypothesis, whether peripheral feedback is really sufficient on its own for coordinating the motion of several joints while at the same time accommodating intersegmental interaction torques. Here we propose a minimal computational model to examine this question. Using a biomechanics simulation of a two-joint arm controlled by spinal neural circuitry, we show for the first time that it is indeed possible for the neuromusculoskeletal system to transform simple descending control signals into muscle activation patterns that accommodate interaction forces depending on their direction and magnitude. This is achieved without the aid of any central predictive signal. Even though the model makes various simplifications and abstractions compared to the complexities involved in the control of human arm movements, the finding lends plausibility to the hypothesis that some multijoint movements can in principle be controlled even in the absence of internal models of intersegmental dynamics or learned compensatory motor signals.

A complex immunological idiotypic network for maintenance of tolerance.

A complex immunological idiotypic network for maintenance of tolerance.

Interference and Shaping in Sensorimotor Adaptations with Rewards

When a perturbation is applied in a sensorimotor transformation task, subjects can adapt and maintain performance by either relying on sensory feedback, or, in the absence of such feedback, on information provided by rewards. For example, in a classical rotation task where movement endpoints must be rotated to reach a fixed target, human subjects can successfully adapt their reaching movements solely on the basis of binary rewards, although this proves much more difficult than with visual feedback. Here, we investigate such a reward-driven sensorimotor adaptation process in a minimal computational model of the task. The key assumption of the model is that synaptic plasticity is gated by the reward. We study how the learning dynamics depend on the target size, the movement variability, the rotation angle and the number of targets. We show that when the movement is perturbed for multiple targets, the adaptation process for the different targets can interfere destructively or constructively depending on the similarities between the sensory stimuli (the targets) and the overlap in their neuronal representations. Destructive interferences can result in a drastic slowdown of the adaptation. As a result of interference, the time to adapt varies non-linearly with the number of targets. Our analysis shows that these interferences are weaker if the reward varies smoothly with the subject's performance instead of being binary. We demonstrate how shaping the reward or shaping the task can accelerate the adaptation dramatically by reducing the destructive interferences. We argue that experimentally investigating the dynamics of reward-driven sensorimotor adaptation for more than one sensory stimulus can shed light on the underlying learning rules.

A Model-Based Approach to Phenotyping Sleep-Related Breathing Disorders in Pediatric Obesity

The prevalence of overweight and obesity in children has increased dramatically over the past two decades. A significant fraction of these obese children also have sleep-related breathing disorders. The ongoing study described in this talk combines overnight physiological measurements with a minimal closed-loop computational model of ventilatory control to investigate why this subject population exhibits large variations in sleep and clinical characteristics. The knowledge derived from this study may lead to a better understanding of the mechanisms through which the different phenotypes of sleep-related breathing disorders (SRBD) occur in obese children, and could be useful in providing better guidelines for customizing therapeutic strategies to individual patients. Obstructive sleep apnea (OSA) has been reported to occur at prevalence rates on the order of 50% in morbidly obese children, compared to only a few percent of the general pediatric population. This marked difference in prevalence rates becomes even larger if one were to include other forms of sleep-related breathing disorders (SRBD), such as hypoventilation and hypercapnia or hypoxemia without frank obstructions. Although 45% of obese children have adenotonsillar hypertrophy, adenotonsillectomy eliminates OSA in less than half of the obese subjects who elect to undergo this form of treatment. These data along with other recent findings suggest that, although upper airway anatomy is a key mediator in OSA, other factors that affect dynamic changes in upper airway patency, such as chemoreflex control and state dependence, also play important roles. Moreover, the pool of subjects with obesity-related SRDB display a large variation of polysomnographic and clinical characteristics. These may be classified broadly into the following 4 phenotypic categories: (a) primary snorers with no abnormalities of gas exchange, respiratory pattern or sleep disruption; (b) obstructive hypoventilation with hypercapnia or hypoxemia with near-normal respiratory or sleep patterns; (c) high arousal frequency without prominent gas exchange abnormality, obstructive apneas or hypopneas (includes upper airway resistance syndrome); (d) traditional OSA with recurrent episodes of obstructive hypopnea and apnea. We hypothesize that this diversity in phenotypic behavior results from the existence of different underlying physiological mechanisms, and that a quantitative dynamic model would allow us to better delineate the mechanistic differences.

Inspirational anchors: minimal computational models in cognitive science

In model-based science, a minimal computational model (MCM) is a computational model developed without the guidance of significant empirical evidence about the mechanism being modelled. Despite their historical and contemporary prominence in cognitive science, MCMs face serious challenges: (1) because of the lack of empirical grounding, it is hard to see how we are justified in making inferences from a model to its target and (2) if they say nothing about their targets, it seems that their utility is limited to the articulation of mere logical possibilities. In this article, I scrutinise this challenge by surveying and rejecting some alternative accounts of the epistemological role of MCMs. I argue that these models are best viewed as fulcra upon which additional research is leveraged. In support, I draw connections between cognitive and economic modelling, and sketch how a prominent account of the latter can be extended to cover the former.

Glutathione/thioredoxin systems modulate mitochondrial H2O2 emission: An experimental-computational study

The net emission of hydrogen peroxide (H2O2) from mitochondria results from the balance between reactive oxygen species (ROS) continuously generated in the respiratory chain and ROS scavenging. The relative contribution of the two major antioxidant systems in the mitochondrial matrix, glutathione (GSH) and thioredoxin (Trx), has not been assessed. In this paper, we examine this key question via combined experimental and theoretical approaches, using isolated heart mitochondria from mouse, rat, and guinea pig.As compared with untreated control mitochondria, selective inhibition of Trx reductase with auranofin along with depletion of GSH with 2,4-dinitrochlorobenzene led to a species-dependent increase in H2O2 emission flux of 17, 11, and 6 fold in state 4 and 15, 7, and 8 fold in state 3 for mouse, rat, and guinea pig mitochondria, respectively. The maximal H2O2 emission as a percentage of the total O2 consumption flux was 11%/2.3% for mouse in states 4 and 3 followed by 2%/0.25% and 0.74%/0.29% in the rat and guinea pig, respectively.A minimal computational model accounting for the kinetics of GSH/Trx systems was developed and was able to simulate increase in H2O2 emission fluxes when both scavenging systems were inhibited separately or together. Model simulations suggest that GSH/Trx systems act in concert. When the scavenging capacity of either one of them saturates during H2O2 overload, they relieve each other until complete saturation, when maximal ROS emission occurs.Quantitatively, these results converge on the idea that GSH/Trx scavenging systems in mitochondria are both essential for keeping minimal levels of H2O2 emission, especially during state 3 respiration, when the energetic output is maximal. This suggests that the very low levels of H2O2 emission observed during forward electron transport in the respiratory chain are a result of the well-orchestrated actions of the two antioxidant systems working continuously to offset ROS production.

An interlocked oscillator model for firing of the mesencephalic dopaminergic neuron.

Dopaminergic (DA) neurons display two functionally distinct modes of electrical activity: low- and high-frequency firing. We suggest a new minimal computational model that unites data on these firing modes obtained under different experimental conditions. The model reproduces the separation of maximal frequencies under NMDA synaptic stimulation vs. other treatments. In accord to recent experimental data, NMDA stimulation restricted to the soma effectively evokes high-frequency oscillations in the model. We have also reproduced low- and high-frequency oscillations under blockade of the SK current. Thus, the new model suggests a way that overcomes all major limitations of the switching dominance mechanism for controlling the frequency of the DA neuron. We explain recent experimental facts and make further predictions.

Systematic construction of a conceptual minimal model of plasma cholesterol levels based on knockout mouse phenotypes

Elevated plasma cholesterol, a well-known risk factor for cardiovascular diseases, is the result of the activity of many genes and their encoded proteins in a complex physiological network. We aim to develop a minimal kinetic computational model for predicting plasma cholesterol levels. To define the scope of this model, it is essential to discriminate between important and less important processes influencing plasma cholesterol levels. To this end, we performed a systematic review of mouse knockout strains and used the resulting dataset, named KOMDIP, for the identification of key genes that determine plasma cholesterol levels. Based on the described phenotype of mouse knockout models, 36 of the 120 evaluated genes were marked as key genes that have a pronounced effect on the plasma cholesterol concentration. The key genes include well-known genes, e.g., Apoe and Ldlr, as well as genes hardly linked to cholesterol metabolism so far, e.g., Plagl2 and Slc37a4. Based on the catalytic function of the genes, a minimal conceptual model was defined. A comparison with nine conceptual models from literature revealed that each of the individual published models is less complete than our model. Concluding, we have developed a conceptual model that can be used to develop a physiologically based kinetic model to quantitatively predict plasma cholesterol levels.

An experimentally constrained computational model of NMDA oscillations in lamprey CPG neurons

Rhythmicity is a characteristic of neural networks responsible for locomotion. In many organisms, activation of N-methyl-D: -aspartate (NMDA) receptors leads to generation of rhythmic locomotor patterns. In addition, single neurons can display intrinsic, NMDA-dependent membrane potential oscillations when pharmacologically isolated from each other by tetrodotoxin (TTX) application. Such NMDA-TTX oscillations have been characterized, for instance, in lamprey locomotor network neurons. Conceptual and computational models have been put forward to explain the appearance and characteristics of these oscillations. Here, we seek to refine the understanding of NMDA-TTX oscillations by combining new experimental evidence with computational modelling. We find that, in contrast to previous computational predictions, the oscillation frequency tends to increase when the NMDA concentration is increased. We develop a new, minimal computational model which can incorporate this new information. This model is further constrained by another new piece of experimental evidence: that regular-looking NMDA-TTX oscillations can be obtained even after voltage-dependent potassium and high-voltage-activated calcium channels have been pharmacologically blocked. Our model conforms to several experimentally derived criteria that we have set up and is robust to parameter changes, as evaluated through sensitivity analysis. We use the model to re-analyze an old NMDA-TTX oscillation model, and suggest an explanation of why it failed to reproduce the new experimental data that we present here.

Microsaccades are triggered by low retinal image slip.

Even during visual fixation of a stationary target, our eyes perform rather erratic miniature movements, which represent a random walk. These "fixational" eye movements counteract perceptual fading, a consequence of fast adaptation of the retinal receptor systems to constant input. The most important contribution to fixational eye movements is produced by microsaccades; however, a specific function of microsaccades only recently has been found. Here we show that the occurrence of microsaccades is correlated with low retinal image slip approximately 200 ms before microsaccade onset. This result suggests that microsaccades are triggered dynamically, in contrast to the current view that microsaccades are randomly distributed in time characterized by their rate-of-occurrence of 1 to 2 per second. As a result of the dynamic triggering mechanism, individual microsaccade rate can be predicted by the fractal dimension of trajectories. Finally, we propose a minimal computational model for the dynamic triggering of microsaccades.

Dynamic control of inhibition improves performance of a hippocampal model

Adding synaptic modification to the inhibitory interneuron in a minimal computational model of hippocampal region CA3 improves average performance of the simulations. After training on two partially overlapping sequences, simulations are tested on a sequence completion problem that can only be solved by using context dependent information. Simulations with dynamic autonomously scaling (DAS) inhibition are more robust than those without. In the DAS model, scaling factors for inhibition are adjusted gradually over time to compensate for the original model's tendency to move away from a pre-set activity level. This variable inhibition modifies more slowly than the local, associative synaptic modification of the excitatory synapses. As a result, activity fluctuations from one time-step to the next continue to occur, but average activity levels show small variability across training. These results suggest that restricting long term activity fluctuations can be beneficial to recurrent networks that must learn context dependent sequences.

Entorhinal/dentate excitation of CA3: A critical variable in hippocampal models

We investigate how the strength of entorhinal cortical inputs during training affects learned performance using computer simulations of a minimal computational model of hippocampal region CA3. After the model learns two partially overlapping sequences, it is tested on two contradictory prediction problems — disambiguation and goal-finding. Relative to total activity, the activity level of entorhinal inputs during learning profoundly affects performance on each task. The optimal input levels differ for the two sequence prediction problems, but a small region of overlap exists where both tasks can usually be performed successfully. This sensitivity to relative input activity suggests critical tests of the model.