Computational Neuroscience Research Articles

MI-DAGSC: A domain adaptation approach incorporating comprehensive information from MI-EEG signals

Non-stationarity of EEG signals leads to high variability between subjects, making it challenging to directly use data from other subjects (source domain) for the classifier in the current subject (target domain). In this study, we propose MI-DAGSC to address domain adaptation challenges in EEG-based motor imagery (MI) decoding. By combining domain-level information, class-level information, and inter-sample structure information, our model effectively aligns the feature distributions of source and target domains. This work is an extension of our previous domain adaptation work MI-DABAN (Li et al., 2023). Based on MI-DABAN, MI-DAGSC designs Sample-Feature Blocks (SFBs) and Graph Convolution Blocks (GCBs) to focus on intra-sample and inter-sample information. The synergistic integration of SFBs and GCBs enable the model to capture comprehensive information and understand the relationship between samples, thus improving representation learning. Furthermore, we introduce a triplet loss to enhance the alignment and compactness of feature representations. Extensive experiments on real EEG datasets demonstrate the effectiveness of MI-DAGSC, confirming that our method makes a valuable contribution to the MI-EEG decoding. Moreover, it holds great potential for various applications in brain–computer interface systems and neuroscience research. And the code of the proposed architecture in this study is available under https://github.com/zhangdx21/MI-DAGSC.

Toward a computational neuroscience of visual cortex without deep learning

Toward a computational neuroscience of visual cortex without deep learning

Computational neurosciences and quantitative systems pharmacology: a powerful combination for supporting drug development in neurodegenerative diseases.

Successful clinical development of new therapeutic interventions is notoriously difficult, especially in neurodegenerative diseases, where predictive biomarkers are scarce and functional improvement is often based on patient's perception, captured by structured interviews. As a consequence, mechanistic modeling of the processes relevant to therapeutic interventions in CNS disorders has been lagging behind other disease indications, probably because of the perceived complexity of the brain. However in this report, we develop the argument that a combination of Computational Neurosciences and Quantitative Systems Pharmacology (QSP) modeling of molecular pathways is a powerful simulation tool to enhance the probability of successful drug development for neurodegenerative diseases. Computational Neurosciences aims to predict action potential dynamics and neuronal circuit activation that are ultimately linked to behavioral changes and clinically relevant functional outcomes. These processes can not only be affected by the disease state, but also by common genotype variants on neurotransmitter-related proteins and the psycho-active medications often prescribed in these patient populations. Quantitative Systems Pharmacology (QSP) modeling of molecular pathways allows to simulate key pathological drivers of dementia, such as protein aggregation and neuroinflammatory responses. They often impact neurotransmitter homeostasis and voltage-gated ion-channels or lead to mitochondrial dysfunction, ultimately leading to changes in action potential dynamics and clinical readouts. Combining these two modeling approaches can lead to better actionable understanding of the many non-linear pharmacodynamic processes active in the human diseased brain. Practical applications include a rational selection of the optimal doses in combination therapies, identification of subjects more likely to respond to treatment, a more balanced stratification of treatment arms in terms of comedications, disease status and common genotype variants and re-analysis of small clinical trials to uncover a possible clinical signal. Ultimately this will lead to a higher success rate of bringing new therapeutics to the right patient populations.

Qualitatively Different Delay-Dependent Working Memory Distortions in People With Schizophrenia and Healthy Control Participants

BackgroundImpairments in working memory (WM) have been well documented in people with schizophrenia (PSZ). However, these quantitative WM impairments can often be explained by nonspecific factors, such as impaired goal maintenance. Here, we used a spatial orientation delayed response task to explore a qualitative difference in WM dynamics between PSZ and healthy control participants (HCs). More specifically, we took advantage of the discovery that WM representations may drift either toward or away from previous trial targets (serial dependence). We tested the hypothesis that WM representations would drift toward the previous trial target in HCs but away from the previous trial target in PSZ. MethodsWe assessed serial dependence in PSZ (n = 31) and HCs (n = 25) using orientation as the to-be-remembered feature and memory delays lasting from 0 to 8 seconds. Participants were asked to remember the orientation of a teardrop-shaped object and reproduce the orientation after a delay period of varying length. ResultsConsistent with prior studies, we found that current trial memory representations were less precise in PSZ than in HCs. We also found that WM for the current trial orientation drifted toward the previous trial orientation in HCs (representational attraction) but drifted away from the previous trial orientation in PSZ (representational repulsion). ConclusionsThese results demonstrate a qualitative difference in WM dynamics between PSZ and HCs that cannot be easily explained by nuisance factors such as reduced effort. Most computational neuroscience models also fail to explain these results because they maintain information solely by means of sustained neural firing, which does not extend across trials. The results suggest a fundamental difference between PSZ and HCs in longer-term memory mechanisms that persist across trials, such as short-term potentiation and neuronal adaptation.

Linear Classification of Neural Manifolds with Correlated Variability.

Understanding how the statistical and geometric properties of neural activity relate to performance is a key problem in theoretical neuroscience and deep learning. Here, we calculate how correlations between object representations affect the capacity, a measure of linear separability. We show that for spherical object manifolds, introducing correlations between centroids effectively pushes the spheres closer together, while introducing correlations between the axes effectively shrinks their radii, revealing a duality between correlations and geometry with respect to the problem of classification. We then apply our results to accurately estimate the capacity of deep network data.

Recent Developments in Neuroinformatics and Computational Neuroscience

In comparison to other natural systems, the temporal dynamics of the human brain's growth, structure, and function are notably intricate. The human brain is comprised of an estimated 86.1 to 8.0 billion neurons and a comparable non-neural glial cells number. Additionally, the brain contains neuronal systems with over 100 trillion connections. The modeling, analysis, and comprehension of these complex structures require the use of code and automation. Neuroinformatics methodologies are employed to manage, retrieve, and integrate the copious quantities of data produced through clinical documentation, scientific literature, and specialized databases. Conversely, computational neuroscience, which draws heavily upon the fields of biology, physics, mathematics, and computation, tackles these issues. Neuroinformatics is the interdisciplinary field that integrates computational neuroscience and neuroscientific experimentation. This paper functions as an introductory guide for individuals who lack familiarity with the domains of neuroinformatics and computational neuroscience, along with their consistentsophisticated software, resources, and tools.

Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body.

The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.

Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data

Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.

Astromorphic Self-Repair of Neuromorphic Hardware Systems

While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention is still focused on computational units like the neuron and synapse. Shifting from this neuro-synaptic perspective, this paper attempts to explore the self-repair role of glial cells, in particular, astrocytes. The work investigates stronger correlations with astrocyte computational neuroscience models to develop macro-models with a higher degree of bio-fidelity that accurately captures the dynamic behavior of the self-repair process. Hardware-software co-design analysis reveals that bio-morphic astrocytic regulation has the potential to self-repair hardware realistic faults in neuromorphic hardware systems with significantly better accuracy and repair convergence for unsupervised learning tasks on the MNIST and F-MNIST datasets. Our implementation source code and trained models are available at https://github.com/NeuroCompLab-psu/Astromorphic_Self_Repair.

Brain works principle followed by neural information processing: a review of novel brain theory

The way the brain work and its principle of work has long been a big scientific question that scientists have dreamed of solving. However, as is known to all, the brain works at different levels, and the operation at different levels is interactional and mutually coupled. Unfortunately, until now, we still do not know how the nervous system at different levels is interacting and coupling with each other. This review provides some preliminary discussions on how to address these scientific questions, for which we propose a novel theory of the brain called neural energy. Such a theoretical and research approach can couple neural information with neural energy to address the interactions of the nervous system at various levels. Therefore, this review systematically summarizes the neural energy theories and methods proposed by our research in the field of brain science, as well as the internal relationship between mechanics and neural energy theory. Focuses on how to construct a Wang–Zhang (W–Z) neuron model equivalent to Hodgkin–Huxley (H–H) model by using the idea of analytical dynamics. Then, based on this model, we proposed a large-scale neural model and a theoretical framework of global neural coding of the brain in the field of neuroscience. It includes information processing of multiple sensory and perceptual nervous systems such as visual perception, neural mechanism of coupling between default mode network and functional network of brain, memory switching and brain state switching, brain navigation, prediction of new working mechanism of neurons, and interpretation of experimental phenomena that are difficult to be explained by neuroscience. It is proved that the new W–Z neuron model and neural energy theory have unique functions and advantages in neural modeling, neural information processing and methodology. The idea of large-scale neuroscience research with neural energy as the core will provide a potentially powerful research method for promoting the fusion of experimental neuroscience and theoretical neuroscience in the future, and propose a widely accepted brain theory system between experimental neuroscience and theoretical neuroscience. It is of great scientific significance to abandon the shortcomings of reductive and holism research methods in the field of neuroscience, and effectively integrate their respective advantages in methodology.

Is the mind in the brain in contemporary computational neuroscience?

According to contemporary computational neuroscience the mental is associated with computations implemented in the brain. We analyze in physical terms based on recent results in the foundations of statistical mechanics two well-known (independent) problems that arise for this approach: the problem of multiple-computations and the problem of multiple-realization. We show that within the computational theory of the mind the two problems are insoluble by the physics of the brain. We further show that attempts to solve the problems by the interactions of the systems implementing the computations with an environment (in or outside the brain) must introduce non-physical factors, and therefore fail on physical grounds. We also show that the problems are endemic and pertain to other forms of functional theories of the mind, most notably, causal functionalism. Finally, we propose a physicalist reductive identity theory, which is a generalization of statistical mechanics for all the special sciences, and show that only a theory of this kind can provide physical solutions to the above two problems in computational neuroscience. We conclude that functionalism in the theory of mind must be replaced with a reductive identity theory. This result has far-reaching implications with respect to the research programs in brain science.

PyVBMC: Efficient Bayesian inference in Python

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for black-box computational models (Acerbi, 2018, 2020). VBMC is an approximate inference method designed for efficient parameter estimation and model assessment when model evaluations are mildly-to-very expensive (e.g., a second or more) and/or noisy. Specifically, VBMC computes: - a flexible (non-Gaussian) approximate posterior distribution of the model parameters, from which statistics and posterior samples can be easily extracted; - an approximation of the model evidence or marginal likelihood, a metric used for Bayesian model selection. PyVBMC can be applied to any computational or statistical model with up to roughly 10-15 continuous parameters, with the only requirement that the user can provide a Python function that computes the target log likelihood of the model, or an approximation thereof (e.g., an estimate of the likelihood obtained via simulation or Monte Carlo methods). PyVBMC is particularly effective when the model takes more than about a second per evaluation, with dramatic speed-ups of 1-2 orders of magnitude when compared to traditional approximate inference methods. Extensive benchmarks on both artificial test problems and a large number of real models from the computational sciences, particularly computational and cognitive neuroscience, show that VBMC generally - and often vastly - outperforms alternative methods for sample-efficient Bayesian inference, and is applicable to both exact and simulator-based models (Acerbi, 2018, 2019, 2020). PyVBMC brings this state-of-the-art inference algorithm to Python, along with an easy-to-use Pythonic interface for running the algorithm and manipulating and visualizing its results.

A bimembrane neuron for computational neuroscience

A biological neuron has endomembrane and extracellular membrane, and its capacitive effect can be described in an equiavlent neural circuit composed of two capacitors. In this paper, a smooth nonlinear resistor with cubic term for the current-voltage (i-v) is introduced into a nonlinear circuit connected with two capacitors and one inductor. The characteristic of field energy is analyzed and unknown parameters are identified with adaptive law. Furthermore, a magnetic flux-controlled memristor (MFCM) is connected to this circuit to estimate its memristive effect on dynamics and energy flow. After scale transformation, the two kinds of nonlinear circuits are replaced by equivalent neuron models, which can present similar spiking, bursting patterns as those presented in biological neurons. Energy function is obtained in theoretical way for discerning the relation between mode selection and energy level. Noisy disturbance is applied to induce coherence resonance, and the involvement of memristive current can induce coherence resonance with lower noise intensity. The results confirmed that energy flow can be controlled to regulate the electric activities in neurons, and higher energy level is obtained in periodic patterns of neuron under coherence resonance. This neural circuit and the memristive model are more suitable for identifying the neural activities in nervous system composed of biological neurons.

Applications of machine learning in neuroscience and inspiration of reinforcement learning for computational neuroscience

Applications of machine learning in neuroscience and inspiration of reinforcement learning for computational neuroscience

Advances in Spinal Cord Neuromodulation: The Integration of Neuroengineering, Computational Approaches, and Innovative Conceptual Frameworks.

Spinal cord stimulation (SCS) is an approved treatment for intractable pain and has recently emerged as a promising area of research for restoring function after spinal cord lesion. This review will focus on the historical evolution of this transition and the path that remains to be taken for these methods to be rigorously evaluated for application in clinical practice. New developments in SCS are being driven by advances in the understanding of spinal cord lesions at the molecular, cellular, and neuronal levels, as well as the understanding of compensatory mechanisms. Advances in neuroengineering and the computational neurosciences have enabled the development of new conceptual SCS strategies, such as spatiotemporal neuromodulation, which allows spatially selective stimulation at precise time points during anticipated movement. It has also become increasingly clear that these methods are only effective when combined with intensive rehabilitation techniques, such as new task-oriented methods and robotic aids. The emergence of innovative approaches to spinal cord neuromodulation has sparked significant enthusiasm among patients and in the media. Non-invasive methods are perceived to offer improved safety, patient acceptance, and cost-effectiveness. There is an immediate need for well-designed clinical trials involving consumer or advocacy groups to evaluate and compare the effectiveness of various treatment modalities, assess safety considerations, and establish outcome priorities.

Availability, outage, and capacity of spatially correlated, Australasian free-space optical networks

Network capacity and reliability for free space optical communication (FSOC) is strongly driven by ground station availability, which is dominated by local cloud cover causing an outage. Here, we combine remote sensing data and novel methods to provide a generalized framework for assessing and optimizing optical ground station networks. This work is guided by an example network of eight Australian and New Zealand optical communication ground stations that span approximately 60° in longitude and 20° in latitude. Utilizing time-dependent cloud cover data from five satellites, we present a detailed analysis that determines the network availability and diversity, which showed that the Australasian region is well-suited for an optical network with a 69% average site availability and low spatial cloud cover correlations. Employing methods from computational neuroscience, we provide a Monte Carlo method for sampling the joint probability distribution of site availabilities for an arbitrarily sized and point-wise correlated network of ground stations. Furthermore, we develop a general heuristic for site selection under availability and correlation optimizations and combine it with orbital propagation simulations to compare the data capacity between optimized networks and the example network. We show that the example network may be capable of providing tens of terabits per day to a low Earth orbit satellite and up to 99.97% reliability to geostationary satellites. We therefore used the Australasian region to demonstrate, to the best of our knowledge, novel, generalized tools for assessing and optimizing FSOC ground station networks, as well as the suitability of the region for hosting such a network.

Dissociating the processing of empty categories in raising and control sentences: a self-paced reading study in Japanese

IntroductionTheoretical linguistics has proposed different types of empty categories (ECs), i.e., unpronounced words with syntactic characteristics. ECs are a key to elucidating the computational system of syntax, algorithms of language processing, and their neural implementation. Here we examined the distinction between raising and control sentences in Japanese and whether ECs are psychologically real.MethodsWe recruited 254 native speakers of Japanese in the present internet-based experiment. We used a self-paced reading and a probe recognition priming technique. To investigate whether raising and control sentences have different ECs (i.e., Copy and PRO) and whether these ECs cause a reactivation effect, behavioral data were analyzed using linear mixed-effects models.ResultsWe found two striking results. First, we demonstrate that the reading times of raising and control sentences in Japanese were better explained by the linear mixed-effects model considering the differences of ECs, i.e., Copy and PRO. Secondly, we found a significant reactivation effect for raising and control sentences, which have ECs, and reflexive sentences without ECs. These results indicate that ECs are processed similarly to reflexive pronouns (e.g., himself ).DiscussionBased on these results, we conclude that raising and control sentences in Japanese have different ECs, i.e., Copy and PRO, and that ECs have psychological reality. Our results demonstrate that behavioral experiment based on theoretical linguistics, which is the first step for developing linking hypotheses connecting theoretical linguistics and experimental neuroscience, is indeed necessary for testing hypotheses proposed in theoretical linguistics.

Cascaded Convolutional Recurrent Neural Networks for EEG Emotion Recognition Based on Temporal–Frequency–Spatial Features

Emotion recognition is a research area that spans multiple disciplines, including computational science, neuroscience, and cognitive psychology. The use of electroencephalogram (EEG) signals in emotion recognition is particularly promising due to their objective and nonartefactual nature. To effectively leverage the spatial information between electrodes, the temporal correlation of EEG sequences, and the various sub-bands of information corresponding to different emotions, we construct a 4D matrix comprising temporal–frequency–spatial features as the input to our proposed hybrid model. This model incorporates a residual network based on depthwise convolution (DC) and pointwise convolution (PC), which not only extracts the spatial–frequency information in the input signal, but also reduces the training parameters. To further improve performance, we apply frequency channel attention networks (FcaNet) to distribute weights to different channel features. Finally, we use a bidirectional long short-term memory network (Bi-LSTM) to learn the temporal information in the sequence in both directions. To highlight the temporal importance of the frame window in the sample, we choose the weighted sum of the hidden layer states at all frame moments as the input to softmax. Our experimental results demonstrate that the proposed method achieves excellent recognition performance. We experimentally validated all proposed methods on the DEAP dataset, which has authoritative status in the EEG emotion recognition domain. The average accuracy achieved was 97.84% for the four binary classifications of valence, arousal, dominance, and liking and 88.46% for the four classifications of high and low valence–arousal recognition.

Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days

The apical dendrites of pyramidal neurons in sensory cortex receive primarily top-down signals from associative and motor regions, while cell bodies and nearby dendrites are heavily targeted by locally recurrent or bottom-up inputs from the sensory periphery. Based on these differences, a number of theories in computational neuroscience postulate a unique role for apical dendrites in learning. However, due to technical challenges in data collection, little data is available for comparing the responses of apical dendrites to cell bodies over multiple days. Here we present a dataset collected through the Allen Institute Mindscope’s OpenScope program that addresses this need. This dataset comprises high-quality two-photon calcium imaging from the apical dendrites and the cell bodies of visual cortical pyramidal neurons, acquired over multiple days in awake, behaving mice that were presented with visual stimuli. Many of the cell bodies and dendrite segments were tracked over days, enabling analyses of how their responses change over time. This dataset allows neuroscientists to explore the differences between apical and somatic processing and plasticity.

Representational Geometries Reveal Differential Effects of Response Correlations on Population Codes in Neurophysiology and Functional Magnetic Resonance Imaging.

Two sensory neurons usually display trial-by-trial spike-count correlations given the repeated representations of a stimulus. The effects of such response correlations on population-level sensory coding have been the focal contention in computational neuroscience over the past few years. In the meantime, multivariate pattern analysis (MVPA) has become the leading analysis approach in functional magnetic resonance imaging (fMRI), but the effects of response correlations among voxel populations remain underexplored. Here, instead of conventional MVPA analysis, we calculate linear Fisher information of population responses in human visual cortex (five males, one female) and hypothetically remove response correlations between voxels. We found that voxelwise response correlations generally enhance stimulus information, a result standing in stark contrast to the detrimental effects of response correlations reported in empirical neurophysiological studies. By voxel-encoding modeling, we further show that these two seemingly opposite effects actually can coexist within the primate visual system. Furthermore, we use principal component analysis to decompose stimulus information in population responses onto different principal dimensions in a high-dimensional representational space. Interestingly, response correlations simultaneously reduce and enhance information on higher- and lower-variance principal dimensions, respectively. The relative strength of the two antagonistic effects within the same computational framework produces the apparent discrepancy in the effects of response correlations in neuronal and voxel populations. Our results suggest that multivariate fMRI data contain rich statistical structures that are directly related to sensory information representation, and the general computational framework to analyze neuronal and voxel population responses can be applied in many types of neural measurements.SIGNIFICANCE STATEMENT Despite the vast research interest in the effect of spike-count noise correlations on population codes in neurophysiology, it remains unclear how the response correlations between voxels influence MVPA in human imaging. We used an information-theoretic approach and showed that unlike the detrimental effects of response correlations reported in neurophysiology, voxelwise response correlations generally improve sensory coding. We conducted a series of in-depth analyses and demonstrated that neuronal and voxel response correlations can coexist within the visual system and share some common computational mechanisms. These results shed new light on how the population codes of sensory information can be evaluated via different neural measurements.