Plausible Model Research Articles

Extracting individual characteristics from population data reveals a negative social effect during honeybee defence.

Honeybees protect their colony against vertebrates by mass stinging and they coordinate their actions during this crucial event thanks to an alarm pheromone carried directly on the stinger, which is therefore released upon stinging. The pheromone then recruits nearby bees so that more and more bees participate in the defence. However, a quantitative understanding of how an individual bee adapts its stinging response during the course of an attack is still a challenge: Typically, only the group behaviour is effectively measurable in experiment; Further, linking the observed group behaviour with individual responses requires a probabilistic model enumerating a combinatorial number of possible group contexts during the defence; Finally, extracting the individual characteristics from group observations requires novel methods for parameter inference. We first experimentally observed the behaviour of groups of bees confronted with a fake predator inside an arena and quantified their defensive reaction by counting the number of stingers embedded in the dummy at the end of a trial. We propose a biologically plausible model of this phenomenon, which transparently links the choice of each individual bee to sting or not, to its group context at the time of the decision. Then, we propose an efficient method for inferring the parameters of the model from the experimental data. Finally, we use this methodology to investigate the effect of group size on stinging initiation and alarm pheromone recruitment. Our findings shed light on how the social context influences stinging behaviour, by quantifying how the alarm pheromone concentration level affects the decision of each bee to sting or not in a given group size. We show that recruitment is curbed as group size grows, thus suggesting that the presence of nestmates is integrated as a negative cue by individual bees. Moreover, the unique integration of exact and statistical methods provides a quantitative characterisation of uncertainty associated to each of the inferred parameters.

The Shape of Phylogenies Under Phase-Type Distributed Times to Speciation and Extinction

Phylogenetic trees describe relationships between extant species, but beyond that their shape and their relative branch lengths can provide information on broader evolutionary processes of speciation and extinction. However, currently many of the most widely used macro-evolutionary models make predictions about the shapes of phylogenetic trees that differ considerably from what is observed in empirical phylogenies. Here, we propose a flexible and biologically plausible macroevolutionary model for phylogenetic trees where times to speciation or extinction events are drawn from a Coxian phase-type (PH) distribution. First, we show that different choices of parameters in our model lead to a range of tree balances as measured by Aldous’ beta statistic. In particular, we demonstrate that it is possible to find parameters that correspond well to empirical tree balance. Next, we provide a natural extension of the beta statistic to sets of trees. This extension produces less biased estimates of beta compared to using the median beta values from individual trees. Furthermore, we derive a likelihood expression for the probability of observing an edge-weighted tree under a model with speciation but no extinction. Finally, we illustrate the application of our model by performing both absolute and relative goodness-of-fit tests for two large empirical phylogenies (squamates and angiosperms) that compare models with Coxian PH distributed times to speciation with models that assume exponential or Weibull distributed waiting times. In our numerical analysis, we found that, in most cases, models assuming a Coxian PH distribution provided the best fit.

Astrocytes Learn to Detect and Signal Deviations From Critical Brain Dynamics.

Astrocytes are nonneuronal brain cells that were recently shown to actively communicate with neurons and are implicated in memory, learning, and regulation of cognitive states. Interestingly, these information processing functions are also closely linked to the brain's ability to self-organize at a critical phase transition. Investigating the mechanistic link between astrocytes and critical brain dynamics remains beyond the reach of cellular experiments, but it becomes increasingly approachable through computational studies. We developed a biologically plausible computational model of astrocytes to analyze how astrocyte calcium waves can respond to changes in underlying network dynamics. Our results suggest that astrocytes detect synaptic activity and signal directional changes in neuronal network dynamics using the frequency of their calcium waves. We show that this function may be facilitated by receptor scaling plasticity by enabling astrocytes to learn the approximate information content of input synaptic activity. This resulted in a computationally simple, information-theoretic model, which we demonstrate replicating the signaling functionality of the biophysical astrocyte model with receptor scaling. Our findings provide several experimentally testable hypotheses that offer insight into the regulatory role of astrocytes in brain information processing.

Trench retreat recorded by a subduction zone metamorphic history

Abstract Upper amphibolite-facies metamorphism in subduction zone rocks may occur under exceptional tectonic settings. Differentiating competing mechanisms for its occurrence requires carefully integrated, high-resolution thermobarometric and geochronologic studies of mélange rocks with well-defined field relationships. We present new pressure, temperature, and age data from the classic Cretaceous Catalina Schist in southern California (USA) that allow us to establish a plausible model for its high-temperature metamorphic history. Our results indicate that garnet-amphibolite blocks in the structurally highest amphibolite-facies mélange preserve evidence of three stages of tectonic evolution: (1) prograde lawsonite eclogite-facies metamorphism that peaked at 2.4–2.7 GPa with temperatures >580 °C during fixed-trench subduction (120–115 Ma); (2) post-peak epidote eclogite-facies metamorphism followed by amphibolite-facies metamorphism at 1.4–1.3 GPa with temperatures of 740–790 °C during trench retreat (115–105 Ma); and (3) isothermal decompression (1.3 GPa to <1.0 GPa at temperatures of ~780 °C) and cooling during trench advance and slab-flattening subduction (ca. 105–100 Ma). Our model implies the presence of a continuous Cordilleran subduction system in the Cretaceous, which had varying tectonic regimes through episodes of trench retreat/advance and slab shallowing/steepening that, in turn, dictated the development of the Cordilleran arc system.

A hybrid approach to study large conformational transitions of biomolecules from single particle XFEL diffraction data.

X-ray free-electron laser (XFEL) is the latest generation of the X-ray source that could become an invaluable technique in structural biology. XFEL has ultrashort pulse duration, extreme peak brilliance, and high spatial coherence, which could enable the observation of the biological molecules in near nature state at room temperature without crystallization. However, for biological systems, due to their low diffraction power and complexity of sample delivery, experiments and data analysis are not straightforward, making it extremely challenging to reconstruct three-dimensional (3D) structures from single particle XFEL data. Given the current limitations to the amount and resolution of the data from such XFEL experiments, we propose a new hybrid approach for characterizing biomolecular conformational transitions by using a single 2D low-resolution XFEL diffraction pattern in combination with another known conformation. In our method, we represent the molecular structure with a coarse-grained model, the Gaussian mixture model, to describe large conformational transitions from low-resolution XFEL data. We obtain plausible 3D structural models that are consistent with the XFEL diffraction pattern by deforming an initial structural model to maximize the similarity between the target pattern and the simulated diffraction patterns from the candidate models. We tested the proposed algorithm on two biomolecules of different sizes with different complexities of conformational transitions, adenylate kinase, and elongation factor 2, using synthetic XFEL data. The results show that, with the proposed algorithm, we can successfully describe the conformational transitions by flexibly fitting the coarse-grained model of one conformation to become consistent with an XFEL diffraction pattern simulated from another conformation. In addition, we showed that the incident beam orientation has some effect on the accuracy of the 3D structure modeling and discussed the reasons for the inaccuracies for certain orientations. The proposed method could serve as an alternative approach for retrieving information on 3D conformational transitions from the XFEL diffraction patterns to interpret experimental data. Since the molecules are represented by Gaussian kernels and no atomic structure is needed in principle, such a method could also be used as a tool to seek initial models for 3D reconstruction algorithms.

RL-MLZerD: Multimeric protein docking using reinforcement learning.

Numerous biological processes in a cell are carried out by protein complexes. To understand the molecular mechanisms of such processes, it is crucial to know the quaternary structures of the complexes. Although the structures of protein complexes have been determined by biophysical experiments at a rapid pace, there are still many important complex structures that are yet to be determined. To supplement experimental structure determination of complexes, many computational protein docking methods have been developed; however, most of these docking methods are designed only for docking with two chains. Here, we introduce a novel method, RL-MLZerD, which builds multiple protein complexes using reinforcement learning (RL). In RL-MLZerD a multi-chain assembly process is considered as a series of episodes of selecting and integrating pre-computed pairwise docking models in a RL framework. RL is effective in correctly selecting plausible pairwise models that fit well with other subunits in a complex. When tested on a benchmark dataset of protein complexes with three to five chains, RL-MLZerD showed better modeling performance than other existing multiple docking methods under different evaluation criteria, except against AlphaFold-Multimer in unbound docking. Also, it emerged that the docking order of multi-chain complexes can be naturally predicted by examining preferred paths of episodes in the RL computation.

A Motor Adaptation Model Assuming Update of Internal Model in the Motor Cortex

When considering the human motor-adaptation mechanism from the perspective of the motor control theory, updating the internal model constitutes a critical component. The learning curve at each trial of motion can be explained by a state-space model; however, the model cannot reproduce the time-series data for the hand’s position, velocity, and acceleration (motion profiles). There is no internal model-updating rule for optimal feedback control, a plausible model for reproducing motion profiles. In this paper, we propose an adaptation model that incorporates an internal model-updating rule which modeled after Hebb’s rule into optimal feedback control. Also, we examine the neural substrate of the internal model. To validate the proposed adaptation model, we conducted behavioral experiments with humans that reflected changes in the internal model and reproduced the changes in the internal model as well as the motion profiles using the proposed adaptation model. In addition, we analyzed the data for a visuomotor rotation task performed by a monkey and checked for changes in the output characteristics of neurons in the motor cortex before and after adaptation. According to the above-mentioned validation and analysis results, the motor cortex constitutes the neural substrate of the internal model.

A Guide to Selecting Flexible Survival Models to Inform Economic Evaluations of Cancer Immunotherapies

ObjectivesParametric models are routinely used to estimate the benefit of cancer drugs beyond trial follow-up. The advent of immune checkpoint inhibitors has challenged this paradigm, and emerging evidence suggests that more flexible survival models, which can better capture the shapes of complex hazard functions, might be needed for these interventions. Nevertheless, there is a need for an algorithm to help analysts decide whether flexible models are required and, if so, which should be chosen for testing. This position article has been produced to bridge this gap. MethodsA virtual advisory board comprising 7 international experts with in-depth knowledge of survival analysis and health technology assessment was held in summer 2021. The experts discussed 24 questions across 6 topics: the current survival model selection procedure, data maturity, heterogeneity of treatment effect, cure and mortality, external evidence, and additions to existing guidelines. Their responses culminated in an algorithm to inform selection of flexible survival models. ResultsThe algorithm consists of 8 steps and 4 questions. Key elements include the systematic identification of relevant external data, using clinical expert input at multiple points in the selection process, considering the future and the observed hazard functions, assessing the potential for long-term survivorship, and presenting results from all plausible models. ConclusionsThis algorithm provides a systematic, evidence-based approach to justify the selection of survival extrapolation models for cancer immunotherapies. If followed, it should reduce the risk of selecting inappropriate models, partially addressing a key area of uncertainty in the economic evaluation of these agents.

Statistical quality assessment of Ising-based annealer outputs

The ability to evaluate the outcomes of quantum annealers is essential for such devices to be used in complex computational tasks. We introduce a statistical test of the quality of Ising-based annealers’ output based on the data only, assessing the ground state’s probability of being sampled. A higher probability value implies that at least the lower part of the spectrum is a part of the sample. Assuming a plausible model of the univariate energy distribution of the sample, we express the ground-state energy and temperature as a function of cumulants up to the third order. Using the annealer samples, we evaluate this multiple times using Bootstrap resampling, resulting in an estimated histogram of ground-state energies and deduce the desired parameter on this basis. The approach provides an easily implementable method for the primary validation of Ising-based annealers’ output. We demonstrate its behavior through experiments made with actual samples originating from quantum annealer devices.

Classification of anomalous diffusion in animal movement data using power spectral analysis

The field of movement ecology has seen a rapid increase in high-resolution data in recent years, leading to the development of numerous statistical and numerical methods to analyse relocation trajectories. Data are often collected at the level of the individual and for long periods that may encompass a range of behaviours. Here, we use the power spectral density (PSD) to characterise the random movement patterns of a black-winged kite (Elanus caeruleus) and a white stork (Ciconia ciconia). The tracks are first segmented and clustered into different behaviours (movement modes), and for each mode we measure the PSD and the ageing properties of the process. For the foraging kite we find 1/f noise, previously reported in ecological systems mainly in the context of population dynamics, but not for movement data. We further suggest plausible models for each of the behavioural modes by comparing both the measured PSD exponents and the distribution of the single-trajectory PSD to known theoretical results and simulations.

Homodimeric complexes of the 90–231 human prion: a multilayered computational study based on FMO/GRID-DRY approach

The molecular interaction properties and aggregation capabilities disclosed by PrP-E200K, a pathogenic mutant of the human prion protein, were investigated in detail using multilayered computational approaches. In a previous work, we reported that the electrostatic complementarity between region1 (negative) and region3 (positive) has been assumed to lead to a head-to tail interaction between 120 and 231 PrP-E200K units and to initiation of the aggregation process. In this work, we extended the PrP-E200K structure by including the unstructured 90–120 segment which was found to assume different conformations. Plausible models of 90–231 PrP-E200K dimers were calculated and analyzed in depth to identify the nature of the involved protein–protein interactions. The unstructured 90–120 segment was found to extend the positively charged region3 involved in the association of PrP-E200K units which resulted to be driven by hydrophobic interactions. The combination of molecular dynamics, protein–protein docking, grid-based mapping, and fragment molecular orbital approaches allowed us to provide a plausible mechanism of the early state of 90–231 PrP-E200K aggregation, considered a preliminary step of amyloid conversion.

Primitive nucleobases @ sodium 2-Ketooctanoate vesicles with high salt resistance

• PNB@SKO is the strongest salt resistance P-SCA vesicle system to the best of our knowledge. • The rigid planar PNB molecules orientate via hydrogen bonding in the tilted bilayers of SKO. • The morphology, size, and membrane thickness of SKO vesicles are not affected by the PNB combination significantly. Proto-single chain amphiphiles (P-SCAs) vesicles are considered to be a plausible model of the protocell on early Earth due to their primordial relevancy and membrane structure. In this work, we selected P-SCAs sodium 2-ketooctanate (SKO) and proto-nucleobases (PNBs, including cytosine, uracil, adenine, and guanine) as the building constituents of the vesicles. The PNB molecules incorporate into SKO bilayers to form PNB@SKO vesicles with a binding rate of 7% − 12%. The interactions between them increase the solubility of PNB in water obviously while decreasing the critical vesicle concentration of SKO. The morphology, size, and membrane thickness of SKO PRB@SKO vesicles are not affected by the PNB combination significantly. A binding mechanism is proposed based on the results of Fourier transform infrared and small angle X-ray scattering. The rigid planar PNB molecules orientate via hydrogen bonding in the tilted bilayers of SKO, without excessive disruption to the membrane structure. Both ribozyme-catalyzed and non-enzymatic RNA copying reactions require high concentrations of divalent ions, fortunately, the salt resistance of these PNB@SKO vesicles is surprisingly remarkable, one or two orders of magnitude greater than the other typical P-SCAs vesicles systems. We expect that this primitively relevant vesicle system can exert its potential advantages in the proto-membrane mimicry of the origin of life.

Melting Phase Equilibria from 4 to 7 GPa in the System CaO-MgO-Al2O3-SiO2-CO2, the Persistence of the “Ledge” in Carbonated Basalt with Excess Silica, and the Most Likely Limits on the Depths of Termination of Carbon Cycle at Subduction Zones

Abstract Melting phase relations involving model carbonated basalt with excess silica were studied in experiments over the pressure range of 4–7 GPa in the system CaO-MgO-Al2O3-SiO2-CO2 to determine if there is a sharp decrease in the melting temperatures along the transition from carbon dioxide vapor (vapor) to dolomite. The phase assemblages of clinopyroxene + garnet + coesite + vapor + carbon dioxide-bearing silicate liquid (silicate liquid) and clinopyroxene + garnet + coesite + dolomite + carbonate liquid, exist over 4–5 and 5.8–7 GPa, respectively. These two distinct phase assemblages form the two, vapor + silicate liquid and dolomite + carbonate liquid-bearing divariant surfaces. The dissolved carbon dioxide and the molar calcium number [Ca# 100*(Ca/Ca + Mg)] of the silicate and carbonate liquids are approximately 4–8 wt% and between 50–55 and 35–40 wt% and 69–71, respectively. The compositions of phases vary little, implying minimal topography along the two surfaces, and the temperatures rise linearly along the silicate liquid-bearing divariant surface over 4–5 GPa. Between 5.2 and 5.6 GPa, however, the temperatures decrease precipitously by ~200–250°C and, along with this steep decline, the liquid changes from silicate to carbonate, with the rest of the phase assemblage of clinopyroxene + garnet + coesite + vapor, persisting. Hence, and this is important to emphasize, this liquid, coexisting with vapor, is carbonate in composition in the absence of dolomite. Isobaric invariance, at 5.4 GPa/1250°C, 5.6 GPa/1150°C, and 5.8 GPa/1100°C, consists of the six-phase assemblage of clinopyroxene + garnet + coesite + vapor + dolomite + carbonate liquid. Melting phase relations are thus univariant, and correspond to that of a solidus ‘ledge’, i.e. with a negative Clapeyron slope, in this part of the composition space. The melting reaction along the ledge is clinopyroxene + vapor = garnet + coesite + dolomite + carbonate liquid, and the ledge separates the two divariant surfaces. The Ca# of the coexisting carbonate liquid and dolomite here are opposite to those of the carbonate liquid and dolomite on the calcite-magnesite join at similar pressures as in this study. This is most likely a consequence of the combined effects of (a) observations from experiments and theory that the fusion curve of calcite starts to diverge from that of magnesite toward lower temperatures at pressures in excess of ~5 GPa, and (b) the pressure, where ultrabasic silicate–carbonate (~2.5–3 GPa) and excess-silica carbonate-basalt (>4 GPa, as inhere) systems undergo carbonation. These, in turn, cause the liquid and dolomite in experiments here to become more calcic and more magnesian than observed in experiments on the calcite-magnesite join. The solidus ledge, here, has a profound effect because the most plausible modern-day model ocean crust subduction zone geotherms in Earth will, in all likelihood, intersect it and cause fusion of dolomite, thereby, in effect, liberating all carbon from what once was a carbonate-basalt mixture. Thereafter, little exists to suggest that there is anything ‘deep’ to the carbon cycle, through recycling, with most of it likely confined to less than ~200 km in Earth.

Oxidative Stress: Glutathione and Its Potential to Protect Methionine-35 of Aβ Peptide from Oxidation.

Alzheimer’s disease (AD) is the most common neurodegenerative disorder with heterogeneous etiology. Intracellular neurofibrillary tangles caused by tau (τ) protein phosphorylation and extracellular senile plaques caused by aggregation of amyloid-beta (Aβ) peptide are characteristic histopathological hallmarks of AD. Oxidative stress (OS) is also suggested to play a role in the pathophysiology of AD. The antioxidant glutathione (GSH) is able to mitigate OS through the detoxification of free radicals. The clearance of these free radicals is reported to be affected when there is a decline in GSH levels in AD. These radicals further react with the methionine-35 (M-35) residue of Aβ and facilitate its subsequent oligomerization. This review presents a plausible model indicating the role of master antioxidant GSH to protect M35 of Aβ1–40/Aβ1–42 from oxidation in pathological conditions as compared to healthy controls.

Bacterial infection-driven lymphomagenesis.

The first convincing evidence for a causal relationship between bacterial infection and lymphomagenesis came from the link between gastric lymphoma and chronic Helicobacter pylori gastritis. This review will summarize the current epidemiological, clinical, and biological evidence of a causative role of bacteria in the development of malignant lymphomas, particularly, the extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue type. Other microorganisms have been associated with specific extranodal lymphoma sites with variable and not always definitive, evidence, including Chlamydia psittaci , Borrelia burgdorferi , Campylobacter jejuni and, most recently, Coxiella Burnetii . According to most plausible models, lymphoma growth is a consequence of continuous antigenic stimulation induced by chronic infection. However, some evidence of a direct oncogenic role of H. pylori has been provided, too. Lymphomas are not the result of a single cause but multifactorial diseases, influenced by a variety of genetic and environmental elements. Hence, ascertaining the specific contribution of bacterial infections is not always easy. Nevertheless, the eradication of the associated chronic infection may result in sustained lymphoma regression. Moreover, the association between infections and lymphoma may offer opportunities for reducing lymphoma incidence by preventing the predisposing infections or treating them early.

Child Behavior Problems and Parenting Stress in Underserved Families of Children with ASD: Investigation of Family Resources and Parenting Self-efficacy.

Behavior problems in children with autism spectrum disorder (ASD) may exacerbate parenting stress. Parenting self-efficacy and family resources may influence this association. We examined cross-sectional statistical mediation effects of parenting self-efficacy on the relationship between child behavior problems and parenting stress and hypothesized that family-level resources moderated this indirect effect. Participants included 132 underserved (Medicaid-eligible) children with ASD (ages 3-13) with racial/ethnic diversity; many (63%) had intellectual disability. Greater externalizing problems were linked with lower parenting self-efficacy, which in turn was associated with increased parenting stress. A larger mediation effect was observed for families with fewer resources. A plausible alternative model (parenting stress mediating parenting self-efficacy) exhibited poorer fit. Implications for family supports and benefits of longitudinal follow-up are discussed.

Mineralogic controls are harbingers of hydrological controls on soil organic matter content in warmer boreal forests

Boreal forests contain ∼ 30 % of the global forest soil organic carbon (SOC) stock within a region vulnerable to climate change, yet regional studies on the climate relevant controls of this large reservoir are lacking. The Newfoundland and Labrador Boreal Ecosystem Latitudinal Transect represents a boreal climate gradient of sites ranging in temperature and precipitation similar in extent to changes expected within the region over the next century. Though forest and soil type are similar across the transect, geological parent material upon which soils have developed vary across the region. Despite a > 50 % increase in litterfall and a 3-fold increase in the dissolved organic matter inputs to the mineral soil with decreasing latitude, average surface horizon mineral SOC and N content are similar among regions. To explain this, the relationships between mineral soil characteristics and other ecosystem parameters with SOC and N content were assessed using an information theoretic approach to rank models. Rankings were used to determine the most relevant variables explaining regional variance in surface mineral SOC. Unsurprisingly, organo-metal complexes (OMCs), a weathering product, were a major component of all plausible models. However, Al complexes ranked higher than Fe, likely because of the greater solubility of reduced Fe in reducing microsites, particularly beneath the winter snowpack or within the saturated soils during the spring melt. These results highlight the potential role of seasonal hydrology on the formation of metal-stabilized C and N in boreal forests and suggest that the role of Fe OMC in C sequestration may be enhanced with warmer winters. The realization and effect of these seasonally controlled OMC mechanisms require further understanding of mineral soil characteristics and hydrology and their response to climate warming. Such efforts will improve predictions of the impact of climate change on soil C and N content in these forests.

Physically constrained neural networks for inferring physiological system models.

Systems biology and systems neurophysiology in particular have recently emerged as powerful tools for a number of key applications in the biomedical sciences. Nevertheless, such models are often based on complex combinations of multiscale (and possibly multiphysics) strategies that require ad hoc computational strategies and pose extremely high computational demands. Recent developments in the field of deep neural networks have demonstrated the possibility of formulating nonlinear, universal approximators to estimate solutions to highly nonlinear and complex problems with significant speed and accuracy advantages in comparison with traditional models. After synthetic data validation, we use so-called physically constrained neural networks (PINN) to simultaneously solve the biologically plausible Hodgkin-Huxley model and infer its parameters and hidden time-courses from real data under both variable and constant current stimulation, demonstrating extremely low variability across spikes and faithful signal reconstruction. The parameter ranges we obtain are also compatible with prior knowledge. We demonstrate that detailed biological knowledge can be provided to a neural network, making it able to fit complex dynamics over both simulated and real data.

Monkey Prefrontal Single-Unit Activity Reflecting Category-Based Logical Thinking Process and Its Neural Network Model.

Category-based thinking is a fundamental form of logical thinking. Here, we aimed to investigate its neural process at the local circuit level in the prefrontal cortex (PFC). We recorded single-unit PFC activity while male monkeys (Macaca fuscata) performed a task in which the category and rule were prerequisites of logical thinking and the outcome contingency was its consequence. Different groups of neurons coded a single type of information discretely or multiple types in a transitional form. Results of time-by-time analysis of neuronal activity suggest an information flow from category-coding and rule-coding neurons to transitional intermediate neurons, and then to contingency-coding neurons. Category-coding, rule-coding, and contingency-coding neurons showed stable coding of information, whereas intermediate neurons showed dynamic coding, as if it integrated category and rule to derive contingency. A similar process was confirmed by using a spiking neural network model that consisted of subnetworks coding category and rule on the input layer and those coding contingency on the output layer, with a subnetwork for integration in the intermediate layer. These results suggest that category-based logical thinking is realized in the PFC by separated neural populations organized for working in a feedforward manner.SIGNIFICANCE STATEMENT To elucidate the neural process for logical thinking, we combined an in-depth analysis of single-unit activity data with a biologically plausible computational model. Results of time-by-time analysis of prefrontal neuronal activity suggest an information flow from category-coding and rule-coding neurons to transitional intermediate neurons, and then to contingency-coding neurons. Category-coding, rule-coding, and contingency-coding neurons showed stable coding, whereas intermediate neurons showed dynamic coding, as if they integrated category and rule to derive contingency. A spiking neural network model reproduced similar temporal changes of information as the recorded neuronal data. Our results suggest that the prefrontal cortex (PFC) is critically involved in category-based thought process, and this process may be produced by separated neural populations organized for working in a feedforward manner.

Beyond LIF Neurons on Neuromorphic Hardware.

Neuromorphic systems aim to provide accelerated low-power simulation of Spiking Neural Networks (SNNs), typically featuring simple and efficient neuron models such as the Leaky Integrate-and-Fire (LIF) model. Biologically plausible neuron models developed by neuroscientists are largely ignored in neuromorphic computing due to their increased computational costs. This work bridges this gap through implementation and evaluation of a single compartment Hodgkin-Huxley (HH) neuron and a multi-compartment neuron incorporating dendritic computation on the SpiNNaker, and SpiNNaker2 prototype neuromorphic systems. Numerical accuracy of the model implementations is benchmarked against reference models in the NEURON simulation environment, with excellent agreement achieved by both the fixed- and floating-point SpiNNaker implementations. The computational cost is evaluated in terms of timing measurements profiling neural state updates. While the additional model complexity understandably increases computation times relative to LIF models, it was found a wallclock time increase of only 8× was observed for the HH neuron (11× for the mutlicompartment model), demonstrating the potential of hardware accelerators in the next-generation neuromorphic system to optimize implementation of complex neuron models. The benefits of models directly corresponding to biophysiological data are demonstrated: HH neurons are able to express a range of output behaviors not captured by LIF neurons; and the dendritic compartment provides the first implementation of a spiking multi-compartment neuron model with XOR-solving capabilities on neuromorphic hardware. The work paves the way for inclusion of more biologically representative neuron models in neuromorphic systems, and showcases the benefits of hardware accelerators included in the next-generation SpiNNaker2 architecture.