Neuron Model Research Articles

The human alpha7 nicotinic acetylcholine receptor is a host target for the rabies virus glycoprotein.

The rabies virus enters the nervous system by interacting with several molecular targets on host cells to modify behavior and trigger receptor-mediated endocytosis of the virion by poorly understood mechanisms. The rabies virus glycoprotein (RVG) interacts with the muscle acetylcholine receptor and the neuronal α4β2 subtype of the nicotinic acetylcholine receptor (nAChR) family by the putative neurotoxin-like motif. Given that the neurotoxin-like motif is highly homologous to the α7 nAChR subtype selective snake toxin α-bungarotoxin (αBTX), other nAChR subtypes are likely involved. The purpose of this study is to determine the activity of the RVG neurotoxin-like motif on nAChR subtypes that are expressed in brain regions involved in rabid animal behavior. nAChRs were expressed in Xenopus laevis oocytes, and two-electrode voltage clamp electrophysiology was used to collect concentration-response data to measure the functional effects. The RVG peptide preferentially and completely inhibits α7 nAChR ACh-induced currents by a competitive antagonist mechanism. Tested heteromeric nAChRs are also inhibited, but to a lesser extent than the α7 subtype. Residues of the RVG peptide with high sequence homology to αBTX and other neurotoxins were substituted with alanine. Altered RVG neurotoxin-like peptides showed that residues phenylalanine 192, arginine 196, and arginine 199 are important determinants of RVG peptide apparent potency on α7 nAChRs, while serine 195 is not. The evaluation of the rabies ectodomain reaffirmed the observations made with the RVG peptide, illustrating a significant inhibitory impact on α7 nAChR with potency in the nanomolar range. In a mammalian cell culture model of neurons, we confirm that the RVG peptide binds preferentially to cells expressing the α7 nAChR. Defining the activity of the RVG peptide on nAChRs expands our understanding of basic mechanisms in host-pathogen interactions that result in neurological disorders.

Machine unlearning in brain-inspired neural network paradigms.

Machine unlearning, which is crucial for data privacy and regulatory compliance, involves the selective removal of specific information from a machine learning model. This study focuses on implementing machine unlearning in Spiking Neuron Models (SNMs) that closely mimic biological neural network behaviors, aiming to enhance both flexibility and ethical compliance of AI models. We introduce a novel hybrid approach for machine unlearning in SNMs, which combines selective synaptic retraining, synaptic pruning, and adaptive neuron thresholding. This methodology is designed to effectively eliminate targeted information while preserving the overall integrity and performance of the neural network. Extensive experiments were conducted on various computer vision datasets to assess the impact of machine unlearning on critical performance metrics such as accuracy, precision, recall, and ROC AUC. Our findings indicate that the hybrid approach not only maintains but in some cases enhances the neural network's performance post-unlearning. The results confirm the practicality and efficiency of our approach, underscoring its applicability in real-world AI systems.

Neuromorphic place cells

A neuromorphic simultaneous localization and mapping (SLAM) system shows potential for more efficient implementation than its traditional counterpart. At the mean time a neuromorphic model of spatial encoding neurons in silicon could provide insights on the functionality and dynamic between each group of cells. Especially when realistic factors including variations and imperfections on the neural movement encoding are presented to challenge the existing hypothetical models for localization. We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, egocentric place cells, and the typical allocentric place cells. Together, they form a biologically plausible network that could reproduce the localization functionality of place cells observed in rodents. The system consists of a theta chip with 128 theta cell units and an FPGA implementing 4 networks for egocentric place cells formation that provides the capability for tracking on a 11 by 11 place cell grid. Experimental results validate the robustness of our model when suffering from as much as 18% deviation, induced by parameter variations in analog circuits, from the mathematical model of theta cells. We provide a model for implementing dynamic neuromorphic SLAM systems for dynamic-scale mapping of cluttered environments, even when subject to significant errors in sensory measurements and real-time analog computation. We also suggest a robust approach for the network topology of spatial cells that can mitigate neural non-uniformity and provides a hypothesis for the function of grid cells and the existence of egocentric place cells.

A global neuronal workspace model of functional neurological disorders.

We introduce here a general model of Functional Neurological Disorders based on the following hypothesis: a Functional Neurological Disorder could correspond to a consciously initiated voluntary top-down process causing involuntary lasting consequences that are consciously experienced and subjectively interpreted by the patient as involuntary. We develop this central hypothesis according to Global Neuronal Workspace theory of consciousness, that is particularly suited to describe interactions between conscious and non-conscious cognitive processes. We then present a list of predictions defining a research program aimed at empirically testing their validity. Finally, this general model leads us to reinterpret the long-debated links between hypnotic suggestion and functional neurological disorders. Driven by both scientific and therapeutic goals, this theoretical paper aims at bringing closer the psychiatric and neurological worlds of functional neurological disorders with the latest developments of cognitive neuroscience of consciousness.

Optimization research of handwritten digit recognition algorithm based on hardware neural networks

In the modern era of digitization, the widespread application of handwritten numerical data has led to an exponential increase in data volume, necessitating efficient real-time recognition and processing systems. This project is grounded in the foundational structure of neural networks, with a specific focus on integrating these networks into hardware through Very-Large-Scale Integration (VLSI). The primary objective is to achieve faster processing speeds, lower power consumption, and real-time operations. The central emphasis of this study is the design and implementation of a hardware neural network system tailored specifically for handwritten digit recognition. Drawing from neural network theory, we explore the construction of a hardware-based handwritten digit classifier. The fundamental model employed is the single-layer perceptron neuron model. Upon receiving 28x28 pixel grayscale images, the image classifier utilizes pre-trained weights, activation functions, and a maximum selector to compare and output recognition results for numerical digits. The concrete implementation is realized using the Verilog hardware description language, coupled with algorithm optimization strategies to enhance performance and efficiency. This research endeavor aims to provide an effective hardware solution for real-time handwritten digit recognition in the digital age.

Emerging 2D Ferroelectric Devices for In-Sensor and In-Memory Computing.

The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in-memory and in-sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data-intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling-bond-free surface, ultra-fast polarization flipping, and ultra-low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in-sensing and in-memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics-integrated 2D devices and active ferroelectrics-integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor-memory and computing integration application field, leading to new possibilities for modern electronics.

Comparing a polynomial DOE model and an ANN model for enhanced geranyl cinnamate biosynthesis with Novozym® 435 lipase

Central Composite Rotatable Design (CCRD), a type of factorial design of experiment is among the most traditional methods used for optimizing bioprocesses, but, in recent years artificial neural networks (ANNs) have emerged as a promising approach for data modeling in bioprocesses. A comparative study between CCRD and ANN modeling for data treatment in the optimization of geranyl cinnamate biosynthesis using Novozym® 435 lipase was conducted. The most effective ANN architecture identified for predicting and maximizing the enzymatic synthesis of geranyl cinnamate was a 3-3-1 neurons model utilizing logsig activation function in the hidden layer. The ANN was trained using all available experimental data and demonstrated a strong fit to the experimental data, coefficient of determination near 1 (R2 = 0.9948) and low Sum-squared Error (SSE = 43.06). The polynomial model (CCRD; R2 = 0.9806; SSE = 161.56) indicated the same optimal conditions as the ANN model, predicting a temperature of 90.2 °C, molar ratio of 1:5.68, and enzyme concentration of 18.6% w/w. Comparison of R2 and SSE values due to lack of fit between both models suggests that ANN predictions closely align with experimental conversion values of geranyl cinnamate. Although the polynomial model is feasible to be applied in enzymatic synthesis, it may be less precise in its predictions than ANN models.

Artificial Neuron Based on the Bloch-Point Domain Wall in Ferromagnetic Nanowires.

Nanomagnetism and spintronics are currently active areas of research, with one of the main goals being the creation of low-energy-consuming magnetic memories based on nanomagnet switching. These types of devices could also be implemented in neuromorphic computing by crafting artificial neurons (ANs) that emulate the characteristics of biological neurons through the implementation of neuron models such as the widely used leaky integrate-and-fire (LIF) with a refractory period. In this study, we have carried out numerical simulations of a 120 nm diameter, 250 nm length ferromagnetic nanowire (NW) with the aim of exploring the design of an artificial neuron based on the creation and destruction of a Bloch-point domain wall. To replicate signal integration, we applied pulsed trains of spin currents to the opposite faces of the ferromagnetic NW. These pulsed currents (previously studied only in the continuous form) are responsible for inducing transitions between the stable single vortex (SV) state and the metastable Bloch point domain wall (BP-DW) state. To ensure the system exhibits leak and refractory properties, the NW was placed in a homogeneous magnetic field of the order of mT in the axial direction. The suggested configuration fulfills the requirements and characteristics of a biological neuron, potentially leading to the future creation of artificial neural networks (ANNs) based on reversible changes in the topology of magnetic NWs.

Spike-based computation using classical recurrent neural networks

Spiking neural networks (SNNs) are a type of artificial neural networks in which communication between neurons is only made of events, also called spikes. This property allows neural networks to make asynchronous and sparse computations and therefore drastically decrease energy consumption when run on specialized hardware. However, training such networks is known to be difficult, mainly due to the non-differentiability of the spike activation, which prevents the use of classical backpropagation. This is because state-of-the-art SNNs are usually derived from biologically-inspired neuron models, to which are applied machine learning methods for training. Nowadays, research about SNNs focuses on the design of training algorithms whose goal is to obtain networks that compete with their non-spiking version on specific tasks. In this paper, we attempt the symmetrical approach: we modify the dynamics of a well-known, easily trainable type of recurrent neural network (RNN) to make it event-based. This new RNN cell, called the spiking recurrent cell, therefore communicates using events, i.e. spikes, while being completely differen-tiable. Vanilla backpropagation can thus be used to train any network made of such RNN cell. We show that this new network can achieve performance comparable to other types of spiking networks in the MNIST benchmark and its variants, the Fashion-MNIST and the Neuromorphic-MNIST. Moreover, we show that this new cell makes the training of deep spiking networks achievable.

Lipid levels correlate with neuronal and dopaminergic markers during the differentiation of SH-SY5Y cells

Parkinson's Disease (PD) is characterised by the loss of dopaminergic neurons and the deposition of protein inclusions called Lewy Bodies (LBs). LBs are heterogeneous structures composed of protein and lipid molecules and their main constituent is the presynaptic protein α-synuclein. SH-SY5Y cells are neuroblastoma cells commonly used to model PD because they express dopaminergic markers and α-synuclein and they can be differentiated into neuronal cells using established protocols. Despite increasing evidence pointing towards a role of lipids in PD, limited knowledge is available on the lipidome of undifferentiated and differentiated SH-SY5Y cells. Using a combination of lipidomics, proteomics, morphological and electrophysiological measurements, we identified specific lipids, including sphingolipids, whose levels are affected by the differentiation of SH-SY5Y neuroblastoma cells and found that the levels of these lipids correlate with those of neuronal and dopaminergic markers. These results provide a quantitative characterisation of the changes in lipidome associated with the differentiation of SH-SY5Y cells into more neuronal and dopaminergic-like phenotype and serve as a basis for further characterisation of lipid disruptions in association with PD and its risk factors in this dopaminergic-like neuronal cell model.

Cupolets: History, Theory, and Applications

In chaos control, one usually seeks to stabilize the unstable periodic orbits (UPOs) that densely inhabit the attractors of many chaotic dynamical systems. These orbits collectively play a significant role in determining the dynamics and properties of chaotic systems and are said to form the skeleton of the associated attractors. While UPOs are insightful tools for analysis, they are naturally unstable and, as such, are difficult to find and computationally expensive to stabilize. An alternative to using UPOs is to approximate them using cupolets. Cupolets, a name derived from chaotic, unstable, periodic, orbit-lets, are a relatively new class of waveforms that represent highly accurate approximations to the UPOs of chaotic systems, but which are generated via a particular control scheme that applies tiny perturbations along Poincaré sections. Originally discovered in an application of secure chaotic communications, cupolets have since gone on to play pivotal roles in a number of theoretical and practical applications. These developments include using cupolets as wavelets for image compression, targeting in dynamical systems, a chaotic analog to quantum entanglement, an abstract reducibility classification, a basis for audio and video compression, and, most recently, their detection in a chaotic neuron model. This review will detail the historical development of cupolets, how they are generated, and their successful integration into theoretical and computational science and will also identify some unanswered questions and future directions for this work.

Utilizing Forward Characteristics of Pocket Doped SiGe Tunnel FET for Designing LIF Neuron Model

Utilizing Forward Characteristics of Pocket Doped SiGe Tunnel FET for Designing LIF Neuron Model

Dynamics and stability of neural systems with indirect interactions involved energy levels

Dynamical modelling for neural systems with direct and indirect connections is to understand how these connections contribute to neural dynamics. Despite recent findings suggesting the existence of indirect connections in neural systems, their dynamical characteristics remain poorly understood. In this paper, we propose a simplified circuit model with indirect interactions inspired by the indirect connection between two neurons, from an energy perspective. Through bifurcation and dynamics analysis, we find that the presented model has a striking resemblance with the classical Hodgkin-Huxley neuronal model. Moreover, stability in a neural network coupled with energy is demonstrated by combining stability analysis and numerical simulation. Our analysis sheds light on the excitability dynamics and multi-stability that can emerge in biophysical systems with nonlinear interactions inspired by the neural systems and highlights the role of energy in propagating electrical activities.

Anti-Hebbian plasticity drives sequence learning in striatum

Spatio-temporal activity patterns have been observed in a variety of brain areas in spontaneous activity, prior to or during action, or in response to stimuli. Biological mechanisms endowing neurons with the ability to distinguish between different sequences remain largely unknown. Learning sequences of spikes raises multiple challenges, such as maintaining in memory spike history and discriminating partially overlapping sequences. Here, we show that anti-Hebbian spike-timing dependent plasticity (STDP), as observed at cortico-striatal synapses, can naturally lead to learning spike sequences. We design a spiking model of the striatal output neuron receiving spike patterns defined as sequential input from a fixed set of cortical neurons. We use a simple synaptic plasticity rule that combines anti-Hebbian STDP and non-associative potentiation for a subset of the presented patterns called rewarded patterns. We study the ability of striatal output neurons to discriminate rewarded from non-rewarded patterns by firing only after the presentation of a rewarded pattern. In particular, we show that two biological properties of striatal networks, spiking latency and collateral inhibition, contribute to an increase in accuracy, by allowing a better discrimination of partially overlapping sequences. These results suggest that anti-Hebbian STDP may serve as a biological substrate for learning sequences of spikes.

Computational modeling to study the impact of changes in Nav1.8 sodium channel on neuropathic pain.

Nav1.8 expression is restricted to sensory neurons; it was hypothesized that aberrant expression and function of this channel at the site of injury contributed to pathological pain. However, the specific contributions of Nav1.8 to neuropathic pain are not as clear as its role in inflammatory pain. The aim of this study is to understand how Nav1.8 present in peripheral sensory neurons regulate neuronal excitability and induce various electrophysiological features on neuropathic pain. To study the effect of changes in sodium channel Nav1.8 kinetics, Hodgkin-Huxley type conductance-based models of spiking neurons were constructed using the NEURON v8.2 simulation software. We constructed a single-compartment model of neuronal soma that contained Nav1.8 channels with the ionic mechanisms adapted from some existing small DRG neuron models. We then validated and compared the model with our experimental data from in vivo recordings on soma of small dorsal root ganglion (DRG) sensory neurons in animal models of neuropathic pain (NEP). We show that Nav1.8 is an important parameter for the generation and maintenance of abnormal neuronal electrogenesis and hyperexcitability. The typical increased excitability seen is dominated by a left shift in the steady state of activation of this channel and is further modulated by this channel's maximum conductance and steady state of inactivation. Therefore, modified action potential shape, decreased threshold, and increased repetitive firing of sensory neurons in our neuropathic animal models may be orchestrated by these modulations on Nav1.8. Computational modeling is a novel strategy to understand the generation of chronic pain. In this study, we highlight that changes to the channel functions of Nav1.8 within the small DRG neuron may contribute to neuropathic pain.

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.

A novel coupled functional neuron model and its application in medical image encryption

A novel coupled functional neuron model and its application in medical image encryption

Spiral wave dynamics in a neuronal network model

Spiral waves are spatial-temporal patterns that can emerge in different systems as heart tissues, chemical oscillators, ecological networks and the brain. These waves have been identified in the neocortex of turtles, rats, and humans, particularly during sleep-like states. Although their functions in cognitive activities remain until now poorly understood, these patterns are related to cortical activity modulation and contribute to cortical processing. In this work, ,we construct a neuronal network layer based on the spatial distribution of pyramidal neurons. Our main goal is to investigate how local connectivity and coupling strength are associated with the emergence of spiral waves. Therefore, we propose a trustworthy method capable of detecting different wave patterns, based on local and global phase order parameters. As a result, we find that the range of connection radius (R) plays a crucial role in the appearance of spiral waves. For R < 20 µm, only asynchronous activity is observed due to small number of connections. The coupling strength ( gsyn ) greatly influences the pattern transitions for higher R, where spikes and bursts firing patterns can be observed in spiral and non-spiral waves. Finally, we show that for some values of R and gsyn bistable states of wave patterns are obtained.

Propionic acid promotes neurite recovery in damaged multiple sclerosis neurons.

Neurodegeneration in the autoimmune disease multiple sclerosis still poses a major therapeutic challenge. Effective drugs that target the inflammation can only partially reduce accumulation of neurological deficits and conversion to progressive disease forms. Diet and the associated gut microbiome are currently being discussed as crucial environmental risk factors that determine disease onset and subsequent progression. In people with multiple sclerosis, supplementation of the short-chain fatty acid propionic acid, as a microbial metabolite derived from the fermentation of a high-fiber diet, has previously been shown to regulate inflammation accompanied by neuroprotective properties. We set out to determine whether the neuroprotective impact of propionic acid is a direct mode of action of short-chain fatty acids on CNS neurons. We analysed neurite recovery in the presence of the short-chain fatty acid propionic acid and butyric acid in a reverse-translational disease-in-a-dish model of human-induced primary neurons differentiated from people with multiple sclerosis-derived induced pluripotent stem cells. We found that recovery of damaged neurites is induced by propionic acid and butyric acid. We could also show that administration of butyric acid is able to enhance propionic acid-associated neurite recovery. Whole-cell proteome analysis of induced primary neurons following recovery in the presence of propionic acid revealed abundant changes of protein groups that are associated with the chromatin assembly, translational, and metabolic processes. We further present evidence that these alterations in the chromatin assembly were associated with inhibition of histone deacetylase class I/II following both propionic acid and butyric acid treatment, mediated by free fatty acid receptor signalling. While neurite recovery in the presence of propionic acid is promoted by activation of the anti-oxidative response, administration of butyric acid increases neuronal ATP synthesis in people with multiple sclerosis-specific induced primary neurons.

SpikeExplorer: Hardware-Oriented Design Space Exploration for Spiking Neural Networks on FPGA

One of today’s main concerns is to bring artificial intelligence capabilities to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable devices. Spiking Neural Networks (SNNs) can represent a solution in this sense: inspired by neuroscience, they reach unparalleled power and resource efficiency when run on dedicated hardware accelerators. However, when designing such accelerators, the amount of choices that can be taken is huge. This paper presents SpikExplorer, a modular and flexible Python tool for hardware-oriented Automatic Design Space Exploration to automate the configuration of FPGA accelerators for SNNs. SpikExplorer enables hardware-centric multiobjective optimization, supporting target factors such as accuracy, area, latency, power, and various combinations during the exploration process. The tool searches the optimal network architecture, neuron model, and internal and training parameters leveraging Bayesian optimization, trying to reach the desired constraints imposed by the user. It allows for a straightforward network configuration, providing the full set of explored points for the user to pick the trade-off that best fits their needs. The potential of SpikExplorer is showcased using three benchmark datasets. It reaches 95.8% accuracy on the MNIST dataset, with a power consumption of 180 mW/image and a latency of 0.12 ms/image, making it a powerful tool for automatically optimizing SNNs.