Computational Neuroscience Approach Research Articles

Automating literature screening and curation with applications to computational neuroscience.

ModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1850 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. For example, we estimate we have captured only around one-third of NEURON models, the most common type of models in ModelDB. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (eg, cell types, research topics). Known computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2 (a free document embedding method), GPT-3.5, and GPT-4 were used to identify likely computational neuroscience work and relevant metadata. SPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. Accuracy in identification and extraction might further be improved by dealing with ambiguity of what are computational elements, including more information from papers (eg, Methods section), improving prompts, etc. Natural language processing and large language model techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.

Bridging the Gap Between Biological Plausibility and Practicality in Neuron Modeling: Challenges and Perspectives

Neuron modeling has been a fundamental approach in computational neuroscience for understanding the dynamics and function of the central nervous system (CNS). Over the past decades, a plethora of mathematical models, ranging from the seminal Hodgkin-Huxley model to more complex multicompartmental and stochastic models, have been developed to capture the intricate behavior of neurons and their interactions. However, despite the significant advances in modeling, there remains a substantial disconnect between the complexity of the models and their practical applicability in day-to-day medical and laboratory settings. This article explores the challenges and limitations of current neuron modeling approaches, particularly focusing on the trade-off between biological realism and computational tractability. I discuss the variability of neuronal properties within and across different regions of the brain, which poses a significant challenge for developing models that can accurately represent the full complexity of the CNS. I also highlight the computational bottlenecks associated with simulating highly detailed, which often require extensive computational resources and specialized software. Considering these challenges, we emphasize the need for a balance between biological plausibility and practicality in neuron modeling. We discuss the role of simplified models, such as the leaky integrate-and-fire (LIF) and Izhikevich models, which sacrifice some biological detail in favor of computational efficiency and ease of use. These models have proven valuable for large-scale simulations of neural networks and have provided important insights into the collective behavior of neuronal populations. Furthermore, I explore the potential of data-driven approaches, such as machine learning and artificial neural networks, in bridging the gap between biological realism and practical applicability. These approaches can learn the dynamics of neuronal systems directly from experimental data, bypassing the need for explicit mathematical models. I present a simple example using linear regression to predict neuronal firing rates based on input features, showcasing the potential of machine learning techniques in capturing the complex relationships between neuronal variables. I also highlight the need for more extensive validation of models against experimental data and the development of standardized benchmarks for assessing model performance. In conclusion, while the quest for biologically plausible neuron models has driven significant advances in our understanding of the CNS, the disconnect between model complexity and practical applicability remains a major challenge. By embracing simplified models, data-driven approaches, and interdisciplinary collaborations, we can work towards the development of more efficient and practical neuron models that can bridge the gap between biological realism and computational feasibility, ultimately facilitating the translation of theoretical insights into clinical and experimental applications.

Decoding context memories for threat in large-scale neural networks.

Humans are often tasked with determining the degree to which a given situation poses threat. Salient cues present during prior events help bring online memories for context, which plays an informative role in this process. However, it is relatively unknown whether and how individuals use features of the environment to retrieve context memories for threat, enabling accurate inferences about the current level of danger/threat (i.e. retrieve appropriate memory) when there is a degree of ambiguity surrounding the present context. We leveraged computational neuroscience approaches (i.e. independent component analysis and multivariate pattern analyses) to decode large-scale neural network activity patterns engaged during learning and inferring threat context during a novel functional magnetic resonance imaging task. Here, we report that individuals accurately infer threat contexts under ambiguous conditions through neural reinstatement of large-scale network activity patterns (specifically striatum, salience, and frontoparietal networks) that track the signal value of environmental cues, which, in turn, allows reinstatement of a mental representation, primarily within a ventral visual network, of the previously learned threat context. These results provide novel insight into distinct, but overlapping, neural mechanisms by which individuals may utilize prior learning to effectively make decisions about ambiguous threat-related contexts as they navigate the environment.

AngoraPy: A Python toolkit for modeling anthropomorphic goal-driven sensorimotor systems.

Goal-driven deep learning increasingly supplements classical modeling approaches in computational neuroscience. The strength of deep neural networks as models of the brain lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models can generate hypotheses about the neurocomputations underlying cortical processing that are grounded in macro- and mesoscopic anatomical properties of the network's biological counterpart. Whereas, goal-driven modeling is already becoming prevalent in the neuroscience of perception, its application to the sensorimotor domain is currently hampered by the complexity of the methods required to train models comprising the closed sensation-action loop. This paper describes AngoraPy, a Python library that mitigates this obstacle by providing researchers with the tools necessary to train complex recurrent convolutional neural networks that model the human sensorimotor system. To make the technical details of this toolkit more approachable, an illustrative example that trains a recurrent toy model on in-hand object manipulation accompanies the theoretical remarks. An extensive benchmark on various classical, 3D robotic, and anthropomorphic control tasks demonstrates AngoraPy's general applicability to a wide range of tasks. Together with its ability to adaptively handle custom architectures, the flexibility of this toolkit demonstrates its power for goal-driven sensorimotor modeling.

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qualitative performance, and it is also robust against adversative noise. The method is robust, based on formally correct functions, and does not suffer from having to be tuned on specific data sets. Results: This work demonstrates the robustness of the method against variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) in segmenting cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.

Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches.

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.

Sensation to navigation: a computational neuroscience approach to magnetic field navigation.

Diverse taxa use Earth's magnetic field (i.e., magnetoreception) as a guide during long-distance navigation. However, despite decades of research, specific sensory mechanisms of magnetoreception remain unconfirmed. Necessarily, this has led to theoretical and computational work developing hypotheses of how animals may navigate using magnetoreception. One hypothesized strategy relies on an animal using combinations of magnetic intensity and inclination as a kind of signature to identify a specific region or location. Using these signatures, animals could use a waypoint-based navigation strategy. We show that this navigation strategy is biologically plausible using a close approximation of neural processing to successfully guide an agent in a simulated magnetic field. Moreover, we accomplish this strategy using a processing approach previously utilized for mechanoreception, suggesting processing of Earth's magnetic field may share features with the processing of other, more well-understood sensory systems. Taken together, our results suggest that both for the engineering of novel navigation systems and the study of animal magnetoreception, we should take lessons from other sensory systems.

Mind Causality: A Computational Neuroscience Approach.

A neuroscience-based approach has recently been proposed for the relation between the mind and the brain. The proposal is that events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. Here, some implications are developed. It is proposed that causality, at least as it applies to the brain, should satisfy three conditions. First, interventionist tests for causality must be satisfied. Second, the causally related events should be at the same level of explanation. Third, a temporal order condition must be satisfied, with a suitable time scale in the order of 10 ms (to exclude application to quantum physics; and a cause cannot follow an effect). Next, although it may be useful for different purposes to describe causality involving the mind and brain at the mental level, or at the brain level, it is argued that the brain level may sometimes be more accurate, for sometimes causal accounts at the mental level may arise from confabulation by the mentalee, whereas understanding exactly what computations have occurred in the brain that result in a choice or action will provide the correct causal account for why a choice or action was made. Next, it is argued that possible cases of “downward causation” can be accounted for by a within-levels-of-explanation account of causality. This computational neuroscience approach provides an opportunity to proceed beyond Cartesian dualism and physical reductionism in considering the relations between the mind and the brain.

A convolutional neural-network framework for modelling auditory sensory cells and synapses

In classical computational neuroscience, analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system. These neuronal models are typically slow to compute and cannot be integrated within large-scale neuronal simulation frameworks. We present a hybrid, machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Our DNN-model architecture comprises parallel and differentiable equations that can be used for backpropagation in neuro-engineering applications, and offers a simulation run-time improvement factor of 70 and 280 on CPU or GPU systems respectively. We focussed our development on auditory neurons and synapses, and show that our DNN-model architecture can be extended to a variety of existing analytical models. We describe how our approach for auditory models can be applied to other neuron and synapse types to help accelerate the development of large-scale brain networks and DNN-based treatments of the pathological system.

An Overview of the NIDA Research Portfolio of Computational Neuroscience Approaches to Studying Sleep, Cognition, and Substance Abuse Disorders: Areas of Interest and Funding Announcements

The purpose of this talk is to provide an overview of The NIDA Research Portfolio of Computational Neuroscience Approaches to Studying sleep, cognition, and Substance Abuse disorders; Areas of Interest and Funding Announcements

A memory neural system built based on spiking neural network

Memory’s mechanism has always been the most tempting treasure for researchers. Many contributions have been delivered to unearth the mystery of memory. In this paper, we present our effort at attempting to reveal the mechanism of memory through computational neuroscience approach. We have constructed a structural efficient memory neural system with three modules, which could simulate the process where new memory is generated and kept and could be extracted. We have proved that new connections grow during the memory forming phase are vital for the keeping of memory. We propose that neurons in the memory layer could be divided into two kinds of neurons: neurons serve as interfaces for memory, and neurons serve as the main body for the keeping of memory. We also provide a method to regulate the memory layer to avoid epileptic states and work properly. The result shows our method could generate memory neural system with reasonably high memory extraction accuracy, high energy efficiency, and high robustness for different input stimulations.

Combined Computational Systems Biology and Computational Neuroscience Approaches Help Develop of Future "Cognitive Developmental Robotics".

OPINION article Front. Neurorobot., 15 November 2017 Volume 11 - 2017 | https://doi.org/10.3389/fnbot.2017.00063

Detection of magnetic field properties using distributed sensing: a computational neuroscience approach

Diverse taxa use Earth’s magnetic field to aid both short- and long-distance navigation. Study of these behaviors has led to a variety of postulated sensory and processing mechanisms that remain unconfirmed. Although several models have been proposed to explain and understand these mechanisms’ underpinnings, they have not necessarily connected a putative sensory signal to the nervous system. Using mathematical software simulation, hardware testing and the computational neuroscience tool of dynamic neural fields, the present work implements a previously developed conceptual model for processing magnetite-based magnetosensory data. Results show that the conceptual model, originally constructed to stimulate thought and generate insights into future physiological experiments, may provide a valid approach to encoding magnetic field information. Specifically, magnetoreceptors that are each individually capable of sensing directional information can, as a population, encode magnetic intensity and direction. The findings hold promise both as a biological magnetoreception concept and for generating engineering innovations in sensing and processing.

Computational neuroscience approach to biomarkers and treatments for mental disorders.

Psychiatry research has long experienced a stagnation stemming from a lack of understanding of the neurobiological underpinnings of phenomenologically defined mental disorders. Recently, the application of computational neuroscience to psychiatry research has shown great promise in establishing a link between phenomenological and pathophysiological aspects of mental disorders, thereby recasting current nosology in more biologically meaningful dimensions. In this review, we highlight recent investigations into computational neuroscience that have undertaken either theory- or data-driven approaches to quantitatively delineate the mechanisms of mental disorders. The theory-driven approach, including reinforcement learning models, plays an integrative role in this process by enabling correspondence between behavior and disorder-specific alterations at multiple levels of brain organization, ranging from molecules to cells to circuits. Previous studies have explicated a plethora of defining symptoms of mental disorders, including anhedonia, inattention, and poor executive function. The data-driven approach, on the other hand, is an emerging field in computational neuroscience seeking to identify disorder-specific features among high-dimensional big data. Remarkably, various machine-learning techniques have been applied to neuroimaging data, and the extracted disorder-specific features have been used for automatic case-control classification. For many disorders, the reported accuracies have reached 90% or more. However, we note that rigorous tests on independent cohorts are critically required to translate this research into clinical applications. Finally, we discuss the utility of the disorder-specific features found by the data-driven approach to psychiatric therapies, including neurofeedback. Such developments will allow simultaneous diagnosis and treatment of mental disorders using neuroimaging, thereby establishing 'theranostics' for the first time in clinical psychiatry.

ATP1A2 Mutations in Migraine: Seeing through the Facets of an Ion Pump onto the Neurobiology of Disease.

Mutations in four genes have been identified in familial hemiplegic migraine (FHM), from which CACNA1A (FHM type 1) and SCN1A (FHM type 3) code for neuronal voltage-gated calcium or sodium channels, respectively, while ATP1A2 (FHM type 2) encodes the α2 isoform of the Na+,K+-ATPase's catalytic subunit, thus classifying FHM primarily as an ion channel/ion transporter pathology. FHM type 4 is attributed to mutations in the PRRT2 gene, which encodes a proline-rich transmembrane protein of as yet unknown function. The Na+,K+-ATPase maintains the physiological gradients for Na+ and K+ ions and is, therefore, critical for the activity of ion channels and transporters involved neuronal excitability, neurotransmitter uptake or Ca2+ signaling. Strikingly diverse functional abnormalities have been identified for disease-linked ATP1A2 mutations which frequently lead to changes in the enzyme's voltage-dependent properties, kinetics, or apparent cation affinities, but some mutations are truly deleterious for enzyme function and thus cause full haploinsufficiency. Here, we summarize structural and functional data about the Na+,K+-ATPase available to date and an overview is provided about the particular properties of the α2 isoform that explain its physiological relevance in electrically excitable tissues. In addition, current concepts about the neurobiology of migraine, the correlations between primary brain dysfunction and mechanisms of headache pain generation are described, together with insights gained recently from modeling approaches in computational neuroscience. Then, a survey is given about ATP1A2 mutations implicated in migraine cases as documented in the literature with focus on mutations that were described to completely destroy enzyme function, or lead to misfolded or mistargeted protein in particular model cell lines. We also discuss whether or not there are correlations between these most severe mutational effects and clinical phenotypes. Finally, perspectives for future research on the implications of Na+,K+-ATPase mutations in human pathologies are presented.

LibNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience.

NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment.

Computational neuroscience approach to biomarkers and treatments for psychiatric disorders

Computational neuroscience approach to biomarkers and treatments for psychiatric disorders

Inferring effective computational connectivity using incrementally conditioned multivariate transfer entropy

Effective connectivity analysis is a popular approach in computational neuroscience, whereby one seeks to infer a network of directed edges between neural variables (e.g. voxels or regions in fMRI, or reconstructed source time series in MEG data) which can explain their observed time-series dynamics. This is an important approach in understanding brain function, contrasting with functional connectivity analysis in being directed and dynamic, and with structural connectivity analysis in not requiring interventions and in being task-modulated. In particular, effective connectivity analysis seeks to find a minimal circuit model that can reconstruct the activity patterns contained in the given data. Ideally, such inference would be: made using model-free techniques; capture non-linear, multivariate, directional relationships; handle small amounts of data, and be statistically robust. The information-theoretic measure transfer entropy (TE) [1] (a non-linear Granger causality) is becoming

Electronic circuit implementation of the chaotic Rulkov neuron model

Numerical integration is the most common and straightforward approach in computational neuroscience for the study of biological neuron models based on ordinary differential equations. For some purposes, numerical simulations are not enough due to the multiple bottlenecks in computer architectures. However, when electronic circuits are used to simulate in real time large arrays of coupled neurons, the simulations are much faster than the computer simulations. We present here an electronic implementation of a map-based neuron model, a chaotic Rulkov neuron model, that can be easily transferred on a large scale integration circuit and thus provide a framework for the simulation of large networks of neurons. The Rulkov model is a map-based neuron model that has a surprising abundance of features, such as periodic and chaotic spiking and bursting. The discrete time dynamics allows to tune the time scale of the circuit to the needs of the specific application. Since the circuit described here only uses 18 MOS transistors, it offers new perspectives for building large networks of neurons in a single device. This is very relevant for the analysis of large networks of coupled neurons in order to investigate its dynamics over the network and its synchronization properties.

Computational Neuroscience Ontology: a new tool to provide semantic meaning to your models

The diversity of modeling approaches in computational neuroscience makes model sharing, retrieval, reuse and reproducibility difficult and even sometimes impossible. To address this problem, standardized languages have been developed by and for the community, such as NeuroML[1], PyNN [2] and NineML (http://software.incf.org/software/nineml). Although these languages enable software interoperability and therefore model reuse and reproducibility, they lack semantic information that would facilitate efficient model sharing and retrieval. In the context of the INCF Multi-Scale Modeling (MSM) program, we have developed an ontology to annotate spiking network models described with NineML and other structured model description languages. Ontologies are formal models of knowledge in a particular domain and composed of classes that represent concepts defining the field as well as the logical relations that link these concepts together [3]. These classes and relations have unique identifiers and definitions that allow unambiguous annotation of digital resources such as web pages or model source code. Implemented in a machine-readable format, these knowledge models can be used to design more efficient and intuitive information retrieval systems for experts in the field. We are proposing the first version of the Computational Neuroscience Ontology or CNO. This ontology is composed of 207 classes representing general concepts related to computational neuroscience organized in a hierarchy of concepts. CNO is currently available on Bioportal (http://bioportal.bioontology.org/ontologies/3003). The design of CNO follows some of the recommendations of the Open Biological and Biomedical Ontologies (OBO) community and is compatible with the ontologies developed and maintained within the Neuroscience Information Framework (NIF, [4]http://www.neuinfo.org). Integration with this large federation of neuroscience ontologies has two main advantages: (1) it allows the linking of models with biological information, creating a bridge between computational and experimental knowledge bases; (2) as ontology development is an iterative process that relies on inputs from the community, NIF has developed NeuroLex (http://neurolex.org), an effective collaborative platform, available for community inputs on the content in CNO. With the further development of CNO based on inputs from the community, we hope CNO will provide a useful framework to federate digital resources in the field of computational neuroscience.