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Molecular and ultrastructural correlates of function in neuron, neuronal nets and brain studied, using genetic and immunological concepts
Artificial neurons exhibiting volatile threshold switching and action potential‐like oscillations are crucial for brain‐inspired computing. While Complimentary Metal‐Oxide‐Semiconductor (CMOS)‐based strategies require hundreds of transistors to simulate each neuron, neuronal oscillations arise spontaneously in individual electro‐thermal devices due to nonlinearities like the Mott transition in VO 2 . Despite improved understanding of the physics, quantitative connections between neuronal performance and material properties remain under‐explored, preventing predictive neuron design and rational materials selection. In this work, a physics‐aware forward design methodology is developed for interrogating a wide palette of materials with properties varying by orders of magnitude, and their performance (high frequency, high dynamical reconfigurability and low power) under external circuit and device geometry constraints is assessed. The space of viable materials is identified to be much larger than previously recognized, with candidates from a range of materials classes, including Ge, GaP and MoS 2 . CMOS‐compatible performance (such as 100 GHz oscillating frequencies) can be achieved with CMOS‐compatible node sizes (≈10 nm). Finally, combinations of material properties yielding desired neuronal performance under uncertain design constraints are considered. This work solidifies forward design principles for electro‐thermal neuron devices, a necessary pre‐condition for inverse design from desired neuronal performance to required materials properties.
Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes. Here, in this work, we study DW-MTJ artificial neurons capable of leaky integrate-and-fire (LIF) behavior and demonstrate geometry-dependent leaking dynamics that results in repeatable, tunable LIF operation. Studying the behavior of five different device designs, we show tuning the geometry, stimulating fields and currents, and location of electrical contacts results in a wide range of neuron behavior. Additionally, implementation of an asymmetric notch allows for nonlinear pinning which increased expressivity without sacrificing leaking. The measured behavior is implemented in a simulated spiking neural network that outperforms a 1D model of continuous DW motion and approaches the performance of an ideal LIF activation function. The results show that the analog LIF capability of DW-MTJ neurons combines many desirable neuron functions into a single device, which can result in varied forms of multifunctional neuromorphic computing.
Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. We implemented these characteristics of mouse neurons in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, birds, and automobiles. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the five datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.
High-throughput massively parallel reporter assays (MPRAs) and phenotype-rich in vivo transgenic mouse assays are two potentially complementary ways to study the impact of noncoding variants associated with psychiatric diseases. Here, we investigate the utility of combining these assays. Specifically, we carry out an MPRA in induced human neurons on over 50,000 sequences derived from fetal neuronal ATAC-seq datasets and enhancers validated in mouse assays. We also test the impact of over 20,000 variants, including synthetic mutations and 167 common variants associated with psychiatric disorders. We find a strong and specific correlation between MPRA and mouse neuronal enhancer activity. Four out of five tested variants with significant MPRA effects affected neuronal enhancer activity in mouse embryos. Mouse assays also reveal pleiotropic variant effects that could not be observed in MPRA. Our work provides a catalog of functional neuronal enhancers and variant effects and highlights the effectiveness of combining MPRAs and mouse transgenic assays.
Most neuromorphic chips implement the single-compartment point neuron model where synapse circuits connect directly to a leaky integrate and fire (LIF) soma circuit. However, when using a biologically plausible soma circuit (e.g., Hodgkin-Huxley neuron model), an interface circuitry, such as a current conveyor circuit, is needed to transmit synaptic current to the soma circuit. This is especially true for ultra-low power neuron circuits, where membrane capacitance is on the order of 20 fF. This need for an interface circuit arises because the parasitic capacitance and leakage current caused by fabrication mismatch and second-order effects of the output transistors in the synapse circuits can disturb the spiking dynamics of the soma circuit if connected without an interface. Using an interface circuit to isolate the soma’s membrane capacitor from synapses resolves this issue. We propose to use a unidirectional resistor (a transconductance circuit) to connect the synapse and soma circuits instead of conventional current conveyor circuits. Using a biologically plausible spike pattern detection model, we show that the on-chip spike-timing-dependent plasticity (STDP) learning performance of the proposed unidirectional two-compartment neuron circuit is similar to a single-compartment circuit (with a current conveyor as an interface) and additionally, it is more power-efficient and biologically plausible. The chip is fabricated in the Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm technology node and comprises a single neuron circuit.
Abstract An animal’s feeding state changes its behavioral priorities and thus influences even nonfeeding-related decisions. How the feeding state information is transmitted to nonfeeding-related circuits and what circuit mechanisms are involved in biasing nonfeeding-related decisions remain open questions. By combining calcium imaging, neuronal manipulations, behavioral analysis and computational modeling, we determined that the competition between different aversive responses to mechanical cues is biased by changes in the feeding state. We found that this effect is achieved by the differential modulation of two different types of reciprocally connected inhibitory neurons promoting opposing actions. This modulation results in a more frequent active type of response and, less frequently, a protective type of response if larvae are fed sugar than when they are fed a balanced diet. Information about the internal state is conveyed to inhibitory neurons through homologs of the vertebrate neuropeptide Y, which is known to be involved in regulating feeding behavior.
ABSTRACT Invertebrate chordates, such as the tunicate Ciona, can offer insight into the evolution of the chordate phylum. Anatomical features shared between invertebrate chordates and vertebrates may be taken as evidence of their presence in a common chordate ancestor. The central nervous systems (CNSs) of Ciona larvae and vertebrates share a similar anatomy despite the Ciona CNS having only ∼180 neurons. However, the depth of conservation between the Ciona CNS and those of vertebrates is not resolved. The Ciona caudal CNS, while appearing spinal cord-like, has hitherto been thought to lack motor neurons, bringing into question its homology with the vertebrate spinal cord. We show here that the Ciona larval caudal CNS does, in fact, have functional motor neurons along its length, pointing to the presence of a functional spinal cord-like structure at the base of the chordates.
Emerging neuromorphic devices such as insulator--metal transition (IMT) devices exhibit complex temporal dynamics, including slow internal state memory, hysteresis, and burst-like firing, which are poorly captured by conventional leaky integrate-and-fire (LIF) neurons. However, it remains unclear when such dynamics influence learning and inference at the network level, particularly under commonly used unsupervised plasticity rules. We present a controlled, full-stack co-design study spanning experimental characterization of individual IMT devices, compact neuron model development, and large-scale spiking network simulations with identical architectures and learning rules. Rather than optimizing benchmark accuracy, our goal is to diagnose when neuron-level dynamics survive learning and competition, and when they are suppressed, to inform the co-design of devices, networks, and learning rules that can exploit beyond-LIF complexity.
The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.
Melatonin, a hormone primarily produced by the brain’s pineal gland, not only regulates circadian rhythms, but also influences the structural and biophysical properties of neuronal membranes. Its amphiphilic nature enables direct incorporation into lipid bilayers and preferential interactions with cholesterol-rich lipid rafts, critical hubs for cellular signaling and membrane organization. Despite increasing recognition of its membrane activity, the molecular basis of melatonin’s interactions with coexisting liquid-ordered (L o ) and liquid-disordered (L d ) phases remains unclear. Here, in this study, we combine small-angle neutron scattering (SANS) and all-atom molecular dynamics simulations to examine model neuronal membranes composed of DSPC, DOPC, POPC, and cholesterol. Our results show that melatonin preserves domain morphology while adopting distinct orientations within the bilayer and at the membrane interface, allowing both lateral and transmembrane bridging across lipid phases. These findings establish the molecular underpinnings of melatonin’s modulation of membrane heterogeneity and provide strong support for its receptor-independent actions.
Neuron modeling - simulation of neuronal behavior, model based on Hodgkin and Huxley equations, and simplified model using properties of pulse frequency modulated systems
Neuronal circuitry and neuron populations of structures in normal animal brains and brains modified by unambiguous lesion arrays
Various applications—including brain-like computing and on-chip artificial vision—increasingly demand a combination of electronic and photonic techniques. However, integrating both approaches on a single chip is challenging, and solutions typically rely on disparate components with power-hungry signal conversions. Here, in this paper, we report electro-optical Mott neurons that combine visible light emission with electrical threshold switching, as well as neuron-like oscillations. The devices are based on thin films of sputtered niobium dioxide (NbO 2 ), a Mott insulator–metal transition material, operating at room temperature and emitting light that peaks around 810 nm. Operando measurements reveal an electronic origin to the light emission: charge carrier relaxation initiated by high-field transport in the NbO 2 . Our devices combine electrical and optical functions within a single material, thereby expanding the options available for future artificial intelligence hardware.
Neural Network (NN) architectures that break statistical independence of parameters have been proposed as a new approach for simulating local quantum field theories (QFTs) [1]. In the infinite neuron number limit, single-layer NNs can exactly reproduce QFT results. This paper examines the viability of this architecture for perturbative calculations of local QFTs for finite neuron number N using scalar ϕ 4 theory in d Euclidean dimensions as an example. We find that the renormalized O(1/N ) corrections to two-and four-point correlators yield perturbative series which are sensitive to the UV cut-off and therefore have a weak convergence. We propose a modification to the architecture to improve this convergence and discuss constraints on the parameters of the theory and the scaling of N which allow us to extract accurate field theory results.
Vision-Language-Action (VLA) models enable test-time behavioral steering via neuron-level interventions, but existing methods use fixed strengths and operate in open loop. This static modulation fails under evolving task dynamics, leading to overcorrection, oscillations, and reduced task success—especially for temporal attributes like speed. We propose CTRL-STEER, a control-theoretic framework that casts activation steering as closed-loop feedback with adaptive, time-varying interventions. Instead of assuming neurons encode temporal concepts, we steer along motion-aligned residual directions and regulate intervention magnitude via feedback. We instantiate this with both PID and reinforcement learning controllers that jointly optimize concept adherence and task success. Experiments on fine-tuned OpenVLA policies across four LIBERO suites show improved stability and a better steering–success trade-off over fixed-coefficient baselines, without retraining the base model.
Cognitive impairment is one of the many symptoms reported by individuals suffering from long-COVID and other post-viral infection disorders such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). A common factor among these conditions is a sustained immune response and increased levels of inflammatory cytokines. Tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) are two such cytokines that are elevated in patients diagnosed with long-COVID and ME/CFS. In this study, we characterized the changes in neural functionality, secreted cytokine profiles, and gene expression in co-cultures of human iPSC-derived neurons and primary astrocytes in response to prolonged exposure to TNF-α and IL-6. We found that exposure to TNF-α produced both a concentration-independent and concentration-dependent response in neural activity. Burst duration was significantly reduced within a few days of exposure regardless of concentration (1 pg/mL – 100 ng/mL) but returned to baseline after 7 days. Treatment with low concentrations of TNF-α (e.g., 1 and 25 pg/mL) did not lead to changes in the secreted cytokine profile or gene expression but still resulted in significant changes to electrophysiological features such as interspike interval and burst duration. Conversely, treatment with high concentrations of TNF-α (e.g., 10 and 100 ng/mL) led to reduced spiking activity, which may be correlated to changes in neural health, gene expression, and increases in inflammatory cytokine secretion (e.g., IL-1β, IL-4, and CXCL-10) that were observed at higher TNF-α concentrations. Prolonged exposure to IL-6 led to changes in bursting features, with significant reduction in the number of spikes in bursts across a wide range of treatment concentrations (i.e., 1 pg/mL–10 ng/mL). In combination, the addition of IL-6 appears to counteract the changes to neural function induced by low concentrations of TNF-α, while at high concentrations of TNF-α the addition of IL-6 had little to no effect. Conversely, the changes to electrophysiological features induced by IL-6 were lost when the cultures were co-stimulated with TNF-α regardless of the concentration, suggesting that TNF-α may play a more pronounced role in altering neural function. These results indicate that increased concentrations of key inflammatory cytokines associated with long-COVID can directly impact neural function and may be a component of the cognitive impairment associated with long-COVID and other post-viral infection disorders.
Rhythm variations of neurons of reticular formation during lateral overloads