Computational Neuroscience Research Articles

Decision-making and attention deficit hyperactivity disorder: neuroeconomic perspective

The decision-making process involves various cognitive procedures influenced by the interplay between cognition, motivation, and attention, forming a complex neural framework. Attention is a fundamental cognitive element within decision-making mechanisms, and one of the conditions affecting the attentional system is attention deficit hyperactivity disorder (ADHD). Decision-making impairments in ADHD have significant economic consequences, necessitating effective policies and interventions to address this critical issue. Research from computational models and neuroscience suggests how cognitive functions’ workings and problems affect decision-making and provide insights into the neural implications of decision-making. This article explores the intersection of decision-making, ADHD, and neuroeconomics, highlighting research gaps, potential contributions, and implications for future policies.

Guest Editorial. Interdisciplinary Approaches for Human Cognition: Expanding Perspectives on the Mind

The quest to decipher the intricate workings of the human brain continues to captivate scientists and researchers worldwide. In this endeavor, the Latin American Workshop on Computational Neuroscience (LAWCN) plays a pivotal role by fostering a vibrant community committed to understanding the intersection of neuroscience, computational approaches, and cutting-edge technological innovations. LAWCN was born in 2012 at the Federal University of Rio Grande do Sul (UFRGS) where it later formally became a biannual event in 2017. Since its beginning, its objective has been to support collaborative learning and knowledge sharing through events hosted in unique Brazilian cities such as Porto Alegre (2017), São João del-Rei (2019), and São Luís de Maranhão (2021).

Computational Fuzzy Modelling Approach to Analyze Neuronal Calcium Dynamics With Intracellular Fluxes.

Mathematical neuroscience investigates how calcium distribution in nerve cells affects the neurological system. The interaction of numerous systems is necessary for the operation of several cellular processes in neuron cells, such as calcium, buffer, ER etc. The dynamics of interacting parameters give useful information on neural cell function. This work uses a mathematical model to analyze the dynamic interactions of buffer and ER inside neurons, considering their spatial properties. While buffers bind to calcium ions and lower their concentration, the endoplasmic reticulum (ER) serves as a reservoir, holding a significant number of free calcium ions. The uncertainty of initial values of calcium concentration poses challenges for researchers to develop calcium signaling models. In this article, we examined the exact solution and approximate solution of the mathematical model that was analyzed using the fuzzy undetermined coefficient approach. MATLAB is being used to perform the simulation. Endoplasmic reticulum and buffer have been found to have a substantial impact on calcium signaling. Fuzzy differential equation Provides a useful tool for evaluating complicated processes with imprecise values when ordinary differential equations perform not precisely. They allow for the examination of dynamic processes under fuzzy settings, which contributes to advances research.

Bilateral Turing Test: Assessing machine consciousness simulations

Recent advancements in Artificial Intelligence (AI) have brought discussions on machine consciousness back to the forefront. However, methodologies for assessing machine consciousness simulations remain highly constrained and lack consensus. This study introduces the Bilateral Turing Test, a thought experiment to assess machine consciousness simulations based on a functionalist perspective, focusing on external behavior and cognitive characteristics associated with consciousness. The test involves both machines and humans participating as judges in an enhanced Turing Test, and compares the external behavior of machine-simulated consciousness with the behavior of human consciousness. We derive the experimental principles via three theoretical propositions reflecting the human–machine cognitive relationship and elucidate the basic conditions and procedures of the thought experiment. A systematic measure is employed to evaluate emulation against human benchmarks, and we establish a statistical threshold for successful consciousness simulations. Our study provides a comprehensive framework for redefining machine consciousness simulations and enhancing interdisciplinary conversations across computer science, neuroscience, and philosophy. This framework also advances bottom-up approaches for assessing machine consciousness simulations and provides pivotal insights into human–machine interaction dynamics.

A computational approach to selective attention in embodied approaches to cognitive archaeology.

This article proposes a novel computational approach to embodied approaches in cognitive archaeology called computational cognitive archaeology (CCA). We argue that cognitive archaeology, understood as the study of the human mind based on archaeological findings such as artefacts and material remains excavated and interpreted in the present, can benefit from the integration of novel methods in computational neuroscience interested in modelling the way the brain, the body and the environment are coupled and parameterized to allow for adaptive behaviour. We discuss the kind of tasks that CCA may engage in with a narrative example of how one can model the cumulative cultural evolution of the material and cognitive components of technologies, focusing on the case of knapping technology. This article thus provides a novel theoretical framework to formalize research in cognitive archaeology using recent developments in computational neuroscience.

Active Inference in Psychology and Psychiatry: Progress to Date?

The free energy principle is a formal theory of adaptive self-organising systems that emerged from statistical thermodynamics, machine learning and theoretical neuroscience and has since been translated into biologically plausible 'process theories' of cognition and behaviour, which fall under the banner of 'active inference'. Despite the promise this theory holds for theorising, research and practical applications in psychology and psychiatry, its impact on these disciplines has only now begun to bear fruit. The aim of this treatment is to consider the extent to which active inference has informed theoretical progress in psychology, before exploring its contributions to our understanding and treatment of psychopathology. Despite facing persistent translational obstacles, progress suggests that active inference has the potential to become a new paradigm that promises to unite psychology's subdisciplines, while readily incorporating the traditionally competing paradigms of evolutionary and developmental psychology. To date, however, progress towards this end has been slow. Meanwhile, the main outstanding question is whether this theory will make a positive difference through applications in clinical psychology, and its sister discipline of psychiatry.

Freud's 1926 conjecture is confirmed: evidence from the dorsal periaqueductal gray in mice that human psychological defense against internal instinctual threat evolved from animal motor defense against external predatory threat.

In 1926, Freud famously conjectured that the human ego defense of repression against an internal instinctual threat evolved from the animal motor defense of flight from an external predatory threat. Studies over the past 50 years mainly in rodents have investigated the neurobiology of the fight-or-flight reflex to external threats, which activates the emergency alarm system in the dorsal periaqueductal gray (dPAG), the malfunction of which appears likely in panic and post-traumatic stress disorders, but perhaps also in some "non-emergent" conditions like social anxiety and "hysterical" conversion disorder. Computational neuroscience studies in mice by Reis and colleagues have revealed unprecedented insights into the dPAG-related neural mechanisms underlying these evolutionarily honed emergency vertebrate defensive functions (e.g., explore, risk assessment, escape, freeze). A psychoanalytic interpretation of the Reis studies demonstrates that Freud's 1926 conjecture is confirmed, and that internal instinctual threats alone can also set off the dPAG emergency alarm system, which is regulated by 5-HT1A and CRF-1 receptors. Consistent with current psychoanalytic and neurobiologic theories of panic, several other of the primitive components of the dPAG alarm system may also have relevance for understanding of the unconscious determinants of impaired object relationships (e.g., avoidance distance). These dPAG findings reveal (1) a process of "evolution in situ," whereby a more sophisticated dPAG ego defense is seen evolving out of a more primitive dPAG motor defense, (2) a dPAG location for the phylogenetically ancient kernel of Freud's Ego and Id, and (3) a Conscious Id theory that has been conclusively invalidated.

Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals

This study introduces a novel methodology for classifying cognitive states using convolutional neural networks (CNNs) on electroencephalography (EEG) data of 41 students, aimed at streamlining the traditionally labor-intensive analysis procedures utilized in EEGLAB. Concentrating on the 30–40 Hz frequency range within the gamma band, we developed a CNN model to analyze EEG signals recorded from the inferior parietal lobule during various cognitive tasks. The model demonstrated substantial efficacy, achieving an accuracy of 91.42%, precision of 71.41%, and recall of 72.51%, effectively distinguishing between high and low gamma activity states. This performance surpasses traditional machine learning methods for EEG analysis, such as support vector machines and random forests, which typically achieve accuracies between 70–85% for similar tasks. Our approach offers significant time savings over manual EEGLAB methods. The integration of event-related spectral perturbation (ERSP) analysis with a novel CNN architecture enables capture of both fine-grained and broad spectral EEG features, advancing the field of computational neuroscience. This research has implications for brain-computer interfaces, clinical diagnostics, and cognitive monitoring, offering a more efficient and accurate alternative to current EEG analysis methods.

Oscillations in an artificial neural network convert competing inputs into a temporal code.

The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and complex cells in early visual cortex. However, the computational relevance of oscillatory dynamics experimentally observed in the visual system are typically not considered in artificial neural networks (ANNs). Computational models of neocortical dynamics, on the other hand, rarely take inspiration from computer vision. Here, we combine methods from computational neuroscience and machine learning to implement multiplexing in a simple ANN using oscillatory dynamics. We first trained the network to classify individually presented letters. Post-training, we added temporal dynamics to the hidden layer, introducing refraction in the hidden units as well as pulsed inhibition mimicking neuronal alpha oscillations. Without these dynamics, the trained network correctly classified individual letters but produced a mixed output when presented with two letters simultaneously, indicating a bottleneck problem. When introducing refraction and oscillatory inhibition, the output nodes corresponding to the two stimuli activate sequentially, ordered along the phase of the inhibitory oscillations. Our model implements the idea that inhibitory oscillations segregate competing inputs in time. The results of our simulations pave the way for applications in deeper network architectures and more complicated machine learning problems.

Information Thermodynamics: From Physics to Neuroscience.

This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems, information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy, and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and information storing.

The Scientific Frontiers of Neuroengineering, the Treatment of Schizophrenia and the Visionary Future

Science, civilization and mankind are today in the path of newer scientific divination as regards application of neuroscience and neuro-engineering in diverse areas of science and technology. Neuroengineering is the agglomeration of neuroscience, experimental neuroscience, computational neuroscience, neurology, electrical engineering and nanotechnology. The scientific frontier of neuro-engineering needs to be envisioned and revamped with the passage of scientific history and time. Scientific provenance, scientific divination and scientific revelation are the key highlights of neuroengineering, neuroscience and electrical engineering science today. Today the science of psychiatry and psychology are merged with neuroengineering and neuroscience. The author is a schizophrenic patient since November,2023. From October 2012, he is diagnosed with glaucoma in his both eyes. In this article, the author deeply and profoundly highlights the lucid and insightful world of neuroengineering and the science of psychiatry. This article is targeted towards a newer world in the field of neuroengineering with a deep interface with the trials and tribulations in his life as regards treatment of schizophrenia.

Adaptive structure generation and neuronal differentiation for memory encoding in SNNs

Memory is the core of cognition. The exploration of the memory encoding mechanism or the representation mechanism of information in the Spiking Neural Network (SNN) is the basis for the in-depth study of memory. In this paper, we study the memory encoding mechanism of multilayer SNN models from a biomimetic perspective and explore a method using the high biological likelihood of SNN to enable the network to effectively simulate memory effects. We proposed a series of heuristic neuron-growing connection algorithms and supervised network weight learning algorithms, which were applied to the unsupervised and supervised training process of the presentation layer. These methods optimized the structure of the representation layer, achieved functional differentiation of neurons, and enabled the network to generate differentiated representations for different data modes. Under our algorithm, the proposed model achieves stable convergence with identical pattern inputs, demonstrating distinct representations and sensitivities to different visual modalities. To achieve stable information expression within the network, we conducted various comparative experiments to determine diverse parameters of the complex network. This paper contributes to the development of Brain-inspired Intelligence by bridging the gap between computer science and neuroscience by using simulations to validate biological hypotheses and guide machine learning.

Agent-Based Model for Situational Awareness in the Workplace: Enhancing Neural Networks with Direct Feedback Alignment

The integration of direct feedback alignment (DFA) into neural networks presents a paradigm shift in computational models, enhancing situational awareness within professional settings. This paper explores the application of DFA in agent-based computational models, demonstrating its efficiency and biological plausibility over traditional backpropagation methods. The research highlights the significant impact of direct feedback alignment on reinforcing situational awareness, evidenced through comprehensive simulations that show marked improvements in agents' ability to effectively navigate and comprehend complex work environments. The study suggests that direct feedback alignment revolutionizes neural network learning processes and significantly enhances cognitive aspects critical to employee safety and operational efficiency. It lays the groundwork for future investigations into the integration of neural computation techniques with organizational psychology and behavior, offering a new perspective on fostering safer, more aware, and efficient workplace environments. The potential application of direct feedback alignment across various professional scenarios opens new avenues for research in computational neuroscience, cognitive psychology, and organizational behavior, with a focus on optimizing human-environment interactions in complex systems.

Theoretical Physics and Theoretical Neuroscience: What Each Can Learn from the Other

Theoretical Physics and Theoretical Neuroscience: What Each Can Learn from the Other

Electrical Signaling Beyond Neurons.

Neural action potentials (APs) are difficult to interpret as signal encoders and/or computational primitives. Their relationships with stimuli and behaviors are obscured by the staggering complexity of nervous systems themselves. We can reduce this complexity by observing that "simpler" neuron-less organisms also transduce stimuli into transient electrical pulses that affect their behaviors. Without a complicated nervous system, APs are often easier to understand as signal/response mechanisms. We review examples of nonneural stimulus transductions in domains of life largely neglected by theoretical neuroscience: bacteria, protozoans, plants, fungi, and neuron-less animals. We report properties of those electrical signals-for example, amplitudes, durations, ionic bases, refractory periods, and particularly their ecological purposes. We compare those properties with those of neurons to infer the tasks and selection pressures that neurons satisfy. Throughout the tree of life, nonneural stimulus transductions time behavioral responses to environmental changes. Nonneural organisms represent the presence or absence of a stimulus with the presence or absence of an electrical signal. Their transductions usually exhibit high sensitivity and specificity to a stimulus, but are often slow compared to neurons. Neurons appear to be sacrificing the specificity of their stimulus transductions for sensitivity and speed. We interpret cellular stimulus transductions as a cell's assertion that it detected something important at that moment in time. In particular, we consider neural APs as fast but noisy detection assertions. We infer that a principal goal of nervous systems is to detect extremely weak signals from noisy sensory spikes under enormous time pressure. We discuss neural computation proposals that address this goal by casting neurons as devices that implement online, analog, probabilistic computations with their membrane potentials. Those proposals imply a measurable relationship between afferent neural spiking statistics and efferent neural membrane electrophysiology.

Maximum-correntropy-based sequential method for fast neural population activity reconstruction in the cortex from incomplete abnormally-disturbed noisy measurements

This paper continues to explore the membrane potential reconstruction and pattern recognition problem in a neural tissue modeled by Stochastic Dynamic Neural Field (SDNF) equation. Although recent research has suggested an efficient solution based on the state-space approach through nonlinear Bayesian filtering framework, it is becoming extremely difficult to ignore the existence of non-Gaussian uncertainties in the SDNFs as well as the stability problem of neuronal population dynamics to outliers. Motivated by recent events in signal processing and mathematical neuroscience, this paper explores the SDNFs in a presence of non-Gaussian uncertainties, which is the shot noise case, where the corrupted data might appear due to broken sensors. We derive the “distributionally robust” state estimator for the membrane potential reconstruction process that is the Maximum Correntropy Criterion Extended Kalman Filter (MCC-EKF) as well as its fast and numerically robust (to roundoff) implementation method by using the sequential principle of processing the measurement vectors. The numerical experiments are provided to illustrate the performance of the novel estimation methods.

Multiscale modeling of neuronal dynamics in hippocampus CA1.

The development of biologically realistic models of brain microcircuits and regions constitutes currently a very relevant topic in computational neuroscience. One of the main challenges of such models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. In this paper we introduce a multiscale modeling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modeling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. Then, we analyze the implementation of synaptic plasticity within our framework, a key aspect to study the role of hippocampus in learning and memory consolidation, and we demonstrate the capability of our framework to incorporate the variations at synaptic level. Finally, we present an example of the implementation of our model to study a stimulus propagation at the macro-scale level, and we show that the results of our framework can capture the dynamics obtained in the corresponding spiking network model of the whole CA1 area.

Analysis of the tri-hybrid Maxwell nanofluid flow with temperature-dependent thermophysical characteristics on static and dynamic surfaces employing Levenberg-Marquardt artificial neural networks

Using the Levenberg-Marquardt Artificial Neural Network (LM-ANN) model, this study applies computational neuroscience to the analysis of flow, heat, and mass transport characteristics. The novel characteristics of MHD Maxwell tri-hybrid nanofluid passing through static and moving wedge with temperature-dependent thermophysical properties (thermal conductivity, viscosity, diffusivity, and electrical field) in permeable, radiative, and mixed convection processes are investigated in this paper. Nonlinear PDEs are transformed into nonlinear ODEs using similarity variables. A dataset for the LM-ANN is generated across various scenarios by varying parameters such as the temperature-dependent viscosity parameter ( 0.07 − 0.1 ) , variable diffusivity parameter ( 0.4 − 2.5 ) , thermal buoyancy parameter ( 0.1 − 0.5 ) , nonlinear thermal buoyancy parameter ( 0.2 − 0.5 ) , thermal radiation ( 1 − 1.5 ) , buoyancy ratio parameter ( 0.2 − 0.5 ) , chemical reaction parameter ( 1 − 3 ) , and Schmidt number ( 1 − 3 ) using the Bvp5c numerical algorithm. The testing, training, and validation procedure of LM-ANN is utilized to examine the estimated solutions of specific situations, and the proposed algorithm is then validated. After that, the proposed LM-ANN is validated using regression analysis, MSE, and histogram analysis. The prescribed approach impeccably mirrors the recommended and cited findings, evincing a precision level within the realm of 10−9. Results indicate that increasing the permeability parameter from 1 to 1.2 and the temperature-dependent thermal conductivity parameter from 0.4 to 2.5 enhances the Nusselt number. Additionally, increasing the chemical reaction parameter from 1 to 3 leads to an increase in the Sherwood number. The significance of this study lies in its potential applications in industries such as aerospace, chemical processing, and energy systems, where efficient heat and mass transfer processes are crucial. For instance, the findings can be applied to enhance cooling techniques in high-performance aerospace components, optimize reactor designs in chemical plants, and improve thermal management in power generation systems.

Trade-offs between Complexity and Hub Presence in Network-Based Brain Models

In the emerging field of computational neuroscience, the architecture of brain networks is a subject of intense study and debate. While models that only consider complex systems provide significant insights into neuronal interconnections, they often overlook the pivotal role of brain hubs—central nodes that manage a large number of connections. On the other hand, giving too much importance to brain hubs can lead to an oversimplification of the true complexity found in neuronal networks. This paper explores the challenges and trade-offs of incorporating both complexity and hubs in brain models. Through a custom-built model featuring five hubs with varying weights and distances, we investigate how these elements interact and influence the emergent network properties such as alpha brain wave patterns. Our findings suggest that a balanced approach that considers both complexity and the presence of hubs yields a more accurate and nuanced understanding of brain network architecture.

Structured flexibility in recurrent neural networks via neuromodulation.

The goal of theoretical neuroscience is to develop models that help us better understand biological intelligence. Such models range broadly in complexity and biological detail. For example, task-optimized recurrent neural networks (RNNs) have generated hypotheses about how the brain may perform various computations, but these models typically assume a fixed weight matrix representing the synaptic connectivity between neurons. From decades of neuroscience research, we know that synaptic weights are constantly changing, controlled in part by chemicals such as neuromodulators. In this work we explore the computational implications of synaptic gain scaling, a form of neuromodulation, using task-optimized low-rank RNNs. In our neuromodulated RNN (NM-RNN) model, a neuromodulatory subnetwork outputs a low-dimensional neuromodulatory signal that dynamically scales the low-rank recurrent weights of an output-generating RNN. In empirical experiments, we find that the structured flexibility in the NM-RNN allows it to both train and generalize with a higher degree of accuracy than low-rank RNNs on a set of canonical tasks. Additionally, via theoretical analyses we show how neuromodulatory gain scaling endows networks with gating mechanisms commonly found in artificial RNNs. We end by analyzing the low-rank dynamics of trained NM-RNNs, to show how task computations are distributed.