Neuronal Dynamics Research Articles

Bridging the Chasm Between Cognitive Representations and Formal Structures of Linguistic Meanings.

This paper aims to show that properties of cognitive/conceptual representations and formal-logical structures of linguistic meaning can be inter-translated, recast, transformed into one another, and so united together, even though cognitive/conceptual representations and formal-logical structures of linguistic meaning are apparently distinct in ontology and divergent in their form or character. While cognitive/conceptual representations are ultimately rooted in sensory-motor systems, formal-logical structures of linguistic meaning are abstractions detached from and independent of the actualized world. This paper sketches out the foundations of how representations of linguistic meaning in terms of cognitive/conceptual structures in Cognitive/Conceptual Semantics can be unified with those in terms of formal-logical structures in Formal Semantics. This is done by recasting cognitive/conceptual representations in terms of formal-logical structures of linguistic meaning and re-encoding formal-logical structures of linguistic meaning in terms of cognitive/conceptual representations. Then, these two types of semantic representations, thus shown representationally equivalent, will be related to a series of derivations across levels in neuronal networks and dynamics. The general discussion on unifying cognitive/conceptual representations of linguistic meaning with formal-logical structures is contextualized within the broader context of theorizing in cognitive science.

Advanced iontronic spiking modes with multiscale diffusive dynamics in a fluidic circuit

Fluidic iontronics is emerging as a distinctive platform for implementing neuromorphic circuits, characterised by its reliance on the same aqueous medium and ionic signal carriers as the brain. Drawing upon recent theoretical advancements in both iontronic spiking circuits and in dynamic conductance of conical ion channels, which form fluidic memristors, we expand the repertoire of proposed neuronal spiking dynamics in iontronic circuits. Through a modelled circuit containing channels that carry a bipolar surface charge, we extract phasic bursting, mixed-mode spiking, tonic bursting, and threshold variability, all with spike voltages and frequencies within the typical range for mammalian neurons. These features are possible due to the strong dependence of the typical conductance memory retention time on the channel length, enabling timescales varying from individual spikes to bursts of multiple spikes within a single circuit. These advanced forms of neuronal-like spiking support the exploration of aqueous iontronics as an interesting platform for neuromorphic circuits.

Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation.

Reliable execution of precise behaviors requires that brain circuits are resilient to variations in neuronal dynamics. Genetic perturbation of the majority of excitatory neurons in HVC, a brain region involved in song production, in adult songbirds with stereotypical songs triggered severe degradation of the song. The song fully recovered within 2 weeks, and substantial improvement occurred even when animals were prevented from singing during the recovery period, indicating that offline mechanisms enable recovery in an unsupervised manner. Song restoration was accompanied by increased excitatory synaptic input to neighboring, unmanipulated neurons in the same brain region. A model inspired by the behavioral and electrophysiological findings suggests that unsupervised single-cell and population-level homeostatic plasticity rules can support the functional restoration after large-scale disruption of networks that implement sequential dynamics. These observations suggest the existence of cellular and systems-level restorative mechanisms that ensure behavioral resilience.

Unifying network model links recency and central tendency biases in working memory

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain’s ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience – producing short-term sensory history biases – naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution’s mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

Unifying network model links recency and central tendency biases in working memory.

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain's ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience - producing short-term sensory history biases - naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution's mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

The outer kinetochore components KNL-1 and Ndc80 complex regulate axon and neuronal cell body positioning in the C. elegans nervous system.

The KMN (Knl1/Mis12/Ndc80) network at the kinetochore, primarily known for its role in chromosome segregation, has been shown to be repurposed during neurodevelopment. Here, we investigate the underlying neuronal mechanism and show that the KMN network promotes the proper axonal organization within the C. elegans head nervous system. Postmitotic degradation of KNL-1, which acts as a scaffold for signaling and has microtubule-binding activities at the kinetochore, led to disorganized ganglia and aberrant placement and organization of axons in the nerve ring - an interconnected axonal network. Through gene-replacement approaches, we demonstrate that the signaling motifs within KNL-1, responsible for recruiting protein phosphatase 1, and activating the spindle assembly checkpoint are required for neurodevelopment. Interestingly, while the microtubule-binding activity is crucial to KMN's neuronal function, microtubule dynamics and organization were unaffected in the absence of KNL-1. Instead, the NDC-80 microtubule-binding mutant displayed notable defects in axon bundling during nerve ring formation, indicating its role in facilitating axon-axon contacts. Overall, these findings provide evidence for a noncanonical role for the KMN network in shaping the structure and connectivity of the nervous system in C. elegans during brain development.

Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models

Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological and biophysical accurate neurons. Consequently, research has focused on simplified spiking neuron models, ranging from the computationally fast Leaky Integrate and Fire (LIF) linear models to more sophisticated non-linear implementations like Adaptive Exponential (AdEX) and Izhikevic models, through Generalized Leaky Integrate and Fire (GLIF) approaches. However, in almost all cases, these models are tuned (and can be validated) only under constant current injections and they may not, in general, also reproduce experimental findings under variable currents. This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.

Learning spiking neuronal networks with artificial neural networks: neural oscillations.

First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by a spiking neuronal network model. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks.

Current understanding of the molecular mechanisms of chemotherapy-induced peripheral neuropathy.

Chemotherapy-induced peripheral neuropathy (CIPN) is the most common off-target adverse effects caused by various chemotherapeutic agents, such as cisplatin, oxaliplatin, paclitaxel, vincristine and bortezomib. CIPN is characterized by a substantial loss of primary afferent sensory axonal fibers leading to sensory disturbances in patients. An estimated of 19-85% of patients developed CIPN during the course of chemotherapy. The lack of preventive measures and limited treatment options often require a dose reduction or even early termination of life-saving chemotherapy, impacting treatment efficacy and patient survival. In this Review, we summarized the current understanding on the pathogenesis of CIPN. One prominent change induced by chemotherapeutic agents involves the disruption of neuronal cytoskeletal architecture and axonal transport dynamics largely influenced by the interference of microtubule stability in peripheral neurons. Due to an ineffective blood-nerve barrier in our peripheral nervous system, exposure to some chemotherapeutic agents causes mitochondrial swelling in peripheral nerves, which lead to the opening of mitochondrial permeability transition pore and cytochrome c release resulting in degeneration of primary afferent sensory fibers. The exacerbated nociceptive signaling and pain transmission in CIPN patients is often linked the increased neuronal excitability largely due to the elevated expression of various ion channels in the dorsal root ganglion neurons. Another important contributing factor of CIPN is the neuroinflammation caused by an increased infiltration of immune cells and production of inflammatory cytokines. In the central nervous system, chemotherapeutic agents also induce neuronal hyperexcitability in the spinal dorsal horn and anterior cingulate cortex leading to the development of central sensitization that causes CIPN. Emerging evidence suggests that the change in the composition and diversity of gut microbiota (dysbiosis) could have direct impact on the development and progression of CIPN. Collectively, all these aspects contribute to the pathogenesis of CIPN. Recent advances in RNA-sequencing offer solid platform for in silico drug screening which enable the identification of novel therapeutic agents or repurpose existing drugs to alleviate CIPN, holding immense promises for enhancing the quality of life for cancer patients who undergo chemotherapy and improve their overall treatment outcomes.

Highly biomimetic spiking neuron using SiGe heterojunction bipolar transistors for energy-efficient neuromorphic systems

We demonstrate a highly biomimetic spiking neuron capable of fast and energy-efficient neuronal oscillation dynamics. Our simple neuron circuit is constructed using silicon–germanium heterojunction based bipolar transistors (HBTs) with nanowire structure. The HBT has a hysteresis window with steep switching characteristics and high current margin in the low voltage range, which enables a high spiking frequency (~ 245 kHz) with low energy consumption (≤ 1.37 pJ/spike). Also, gated structure achieves a stable balance in the activity of the neural system by incorporating both excitatory and inhibitory signal. Furthermore, inhibition of multiple strengths can be realized by adjusting the integration time according to the amplitude of the inhibitory signal. In addition, the spiking frequency can be tuned by mutually controlling the hysteresis window in the HBTs. These results ensure the sparse activity and homeostasis of neural networks.

Magnetic Flux Sensor Based on Spiking Neurons with Josephson Junctions.

Josephson junctions (JJs) are superconductor-based devices used to build highly sensitive magnetic flux sensors called superconducting quantum interference devices (SQUIDs). These sensors may vary in design, being the radio frequency (RF) SQUID, direct current (DC) SQUID, and hybrid, such as D-SQUID. In addition, recently many of JJ's applications were found in spiking models of neurons exhibiting nearly biological behavior. In this study, we propose and investigate a new circuit model of a sensory neuron based on DC SQUID as part of the circuit. The dependence of the dynamics of the designed model on the external magnetic flux is demonstrated. The design of the circuit and derivation of the corresponding differential equations that describe the dynamics of the system are given. Numerical simulation is used for experimental evaluation. The experimental results confirm the applicability and good performance of the proposed magnetic-flux-sensitive neuron concept: the considered device can encode the magnetic flux in the form of neuronal dynamics with the linear section. Furthermore, some complex behavior was discovered in the model, namely the intermittent chaotic spiking and plateau bursting. The proposed design can be efficiently applied to developing the interfaces between circuitry and spiking neural networks. However, it should be noted that the proposed neuron design shares the main limitation of all the superconductor-based technologies, i.e., the need for a cryogenic and shielding system.

All-optical neuromorphic XOR and XNOR operation utilizing a photonic spiking neuron based on a passive add-drop microring resonator.

We propose a concise hardware architecture supporting efficient exclusive OR (XOR) and exclusive NOR (XNOR) operations, by employing a single photonic spiking neuron based on a passive add-drop microring resonator (ADMRR). The threshold mechanism and inhibitory dynamics of the ADMRR-based spiking neuron are numerically discussed on the basis of the coupled mode theory. It is shown that a precise XOR operation in the ADMRR-based spiking neuron can be implemented by adjusting temporal differences within the inhibitory window. Additionally, within the same framework, the XNOR function can also be carried out by accumulating the input power over time to trigger an excitatory behavior. This work presents a novel, to the best of our knowledge, and pragmatic technique for optical neuromorphic computing and information processing utilizing passive devices.

Brain‐On‐A‐Chip Based on Human Pluripotent Stem Cell‐Derived Neurons and Astrocytes for Neurotoxicity Testing: Communicative Astrocyte–Neuron DYnamics (CANDY) Chip

AbstractMethylmercury (MeHg), an environmental pollutant, is a ubiquitous neurotoxicant that ultimately leads to neurodegenerative diseases. New experimental models are needed for public health toxicity testing associated with this pollutant. In this study, a human pluripotent stem cell‐based brain‐on‐a‐chip, named Communicative Astrocyte–Neuron DYnamics chip (CANDY chip), is reported, which enables observation of the interactions of neurons with astrocytes and accurate assessment of responses to penetration of MeHg across the blood‐brain barrier. Five neurophysiological characteristics of the CANDY chip provide an exact emulation of the brain: i) co‐culture of neurons with astrocytes, ii) transport of nutrients from blood vessels to neurons via astrocytes, iii) glia‐to‐neurons ratio of 1:1, iv) low‐speed flow of brain interstitial fluid, and v) neuroprotection by astrocytes against MeHg‐induced toxicity. The remarkable vulnerability of neurons is identified to MeHg toxicity as shattered neurites and verifies the tolerance of astrocytes to MeHg toxicity despite morphological changes such as cell shrinkage. It is also confirmed that the chip faithfully recapitulates actual neurophysiological responses through high neuronal cell viability because of the astrocytes and increased brain‐derived neurotrophic factor as a neuroprotector. The CANDY chip can be a powerful platform for accurate neurotoxicity testing and modeling of complex neurodegenerative diseases.

Hardware in Loop Learning with Spin Stochastic Neurons

Despite the promise of superior efficiency and scalability, real‐world deployment of emerging nanoelectronic platforms for brain‐inspired computing have been limited thus far, primarily because of inter‐device variations and intrinsic non‐idealities. In this work, mitigation of these issues is demonstrated by performing learning directly on practical devices through a hardware‐in‐loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin–orbit torque heterostructures. The probabilistic switching and device‐to‐device variability of the fabricated devices of various sizes is characterized to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network‐level performance. The efficacy of the hardware‐in‐loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large‐scale implementations of neuromorphic hardware and realization of truly autonomous edge‐intelligent devices.

Empirical modeling and prediction of neuronal dynamics

Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although less realistic, have also contributed to understand neuronal dynamics. However, there is still a vast volume of data that have not been associated with a mathematical model, mainly because data are acquired more rapidly than they can be analyzed or because it is difficult to analyze (for instance, if the number of ionic channels involved is huge). Therefore, developing new methodologies to obtain mathematical or computational models associated with data (even without previous knowledge of the source) can be helpful to make future predictions. Here, we explore the capability of a wavelet neural network to identify neuronal (single-cell) dynamics. We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces.

IM-LIF: Improved Neuronal Dynamics With Attention Mechanism for Direct Training Deep Spiking Neural Network

IM-LIF: Improved Neuronal Dynamics With Attention Mechanism for Direct Training Deep Spiking Neural Network

Linking fast and slow: The case for generative models.

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

A stochastic model for neural progenitor dynamics in the mouse cerebral cortex

We have designed a stochastic model of embryonic neurogenesis in the mouse cerebral cortex, using the formalism of compound Poisson processes. The model accounts for the dynamics of different progenitor cell types and neurons. The expectation and variance of the cell number of each type are derived analytically and illustrated through numerical simulations. The effects of stochastic transition rates between cell types, and stochastic duration of the cell division cycle have been investigated sequentially. The model does not only predict the number of neurons, but also their spatial distribution into deeper and upper cortical layers. The model outputs are consistent with experimental data providing the number of neurons and intermediate progenitors according to embryonic age in control and mutant situations.

A homotopy-based computational scheme for two-dimensional fractional cable equation

In this paper, we examine the time-dependent two-dimensional cable equation of fractional order in terms of the Caputo fractional derivative. This cable equation plays a vital role in diverse areas of electrophysiology and modeling neuronal dynamics. This paper conveys a precise semi-analytical method called the q-homotopy analysis transform method to solve the fractional cable equation. The proposed method is based on the conjunction of the q-homotopy analysis method and Laplace transform. We explained the uniqueness of the solution produced by the suggested method with the help of Banach’s fixed-point theory. The results obtained through the considered method are in the form of a series solution, and they converge rapidly. The obtained outcomes were in good agreement with the exact solution and are discussed through the 3D plots and graphs that express the physical representation of the considered equation. It shows that the proposed technique used here is reliable, well-organized and effective in analyzing the considered non-homogeneous fractional differential equations arising in various branches of science and engineering.

Release 2.0 — NEMSIM-RT: A real-time distributed spiking neural network simulator

NESIM-RT is a specialized tool designed for simulating neuromorphic systems. In this new release we extend its capabilities to include state-of-the art models like the AdexLIF and Izhikevich, and to incorporate dynamic synaptic mechanisms such as Spike-Timing Dependent Plasticity (STDP). With these new features, researchers can now observe in real-time how different parameters influence these models and learning rules, thereby gaining deeper insights into neuronal function and network dynamics.