Tootfinder

Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit
Dean Brand, Domenica Dibenedetto, Francesco Petruccione
https://arxiv.org/abs/2506.21363

Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit
We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canonical quantization techniques to derive a quantum model grounded in circuit quantum electrodynamics. Numerical simulations demonstrate key dynamical features of the quantized memristor and LIF neuron in the weak-coupling and adiabatic regime, including memory …

AR-LIF: Adaptive reset leaky-integrate and fire neuron for spiking neural networks
Zeyu Huang, Wei Meng, Quan Liu, Kun Chen, Li Ma
https://arxiv.org/abs/2507.20746 https://

AR-LIF: Adaptive reset leaky-integrate and fire neuron for spiking neural networks
Spiking neural networks possess the advantage of low energy consumption due to their event-driven nature. Compared with binary spike outputs, their inherent floating-point dynamics are more worthy of attention. The threshold level and re- set mode of neurons play a crucial role in determining the number and timing of spikes. The existing hard reset method causes information loss, while the improved soft reset method adopts a uniform treatment for neurons. In response to this, this paper designs…

Unified Memcapacitor-Memristor Memory for Synaptic Weights and Neuron Temporal Dynamics
Simone D'Agostino, Marco Massarotto, Tristan Torchet, Filippo Moro, Niccol\`o Castellani, Laurent Grenouillet, Yann Beilliard, David Esseni, Melika Payvand, Elisa Vianello
https://arxiv.org/abs/2506.22227

Unified Memcapacitor-Memristor Memory for Synaptic Weights and Neuron Temporal Dynamics
We present a fabricated and experimentally characterized memory stack that unifies memristive and memcapacitive behavior. Exploiting this dual functionality, we design a circuit enabling simultaneous control of spatial and temporal dynamics in recurrent spiking neural networks (RSNNs). Hardware-aware simulations highlight its promise for efficient neuromorphic processing.

Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators
Alexander Yeung, Peter DelMastro, Arjun Karuvally, Hava Siegelmann, Edward Rietman, Hananel Hazan
https://arxiv.org/abs/2507.21377

Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators
Reservoir Computing is a machine learning approach that uses the rich repertoire of complex system dynamics for function approximation. Current approaches to reservoir computing use a network of coupled integrating neurons that require a steady current to maintain activity. Here, we introduce a small world graph of differentiating neurons that are active only when there are changes in input as an alternative to integrating neurons as a reservoir computing substrate. We find the coupling strengt…

Dual Mechanisms for Heterogeneous Responses of Inspiratory Neurons to Noradrenergic Modulation
Sreshta Venkatakrishnan, Andrew K. Tryba, Alfredo J. Garcia 3rd, Yangyang Wang
https://arxiv.org/abs/2507.19416

Dual Mechanisms for Heterogeneous Responses of Inspiratory Neurons to Noradrenergic Modulation
Respiration is an essential involuntary function necessary for survival. This poses a challenge for the control of breathing. The preBötzinger complex (preBötC) is a heterogeneous neuronal network responsible for driving the inspiratory rhythm. While neuromodulators such as norepinephrine (NE) allow it to be both robust and flexible for all living beings to interact with their environment, the basis for how neuromodulation impacts neuron-specific properties remains poorly understood. In this …

Analysis of a mean-field limit of interacting two-dimensional nonlinear integrate-and-fire neurons
Romain Veltz
https://arxiv.org/abs/2508.19134 https://ar…

Analysis of a mean-field limit of interacting two-dimensional nonlinear integrate-and-fire neurons
We study the solutions of a McKean-Vlasov stochastic differential equation (SDE) driven by a Poisson process. In neuroscience, this SDE models the mean field limit of a system of $N$ interacting excitatory neurons with $N$ large. Each neuron spikes randomly with rate depending on its membrane potential. At each spiking time, the neuron potential is reset to the value $\bar v$, its adaptation variable is incremented by $\bar w$ and all other neurons receive an additional amount $J/N$ of pote…

Prediction: interest in electrode-based BCIs will wane (aside from niches), as non-implantable ("non-invasive") phased array focused ultrasound will take over for both brain stimulation and measuring brain activity https://www.cell.com/neuron/fulltext/S0896-6273(20)…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- DualSparse-MoE: Coordinating Tensor/Neuron-Level Sparsity with Expert Partition and Reconstruction
Weilin Cai, Le Qin, Shwai He, Junwei Cui, Ang Li, Jiayi Huang

Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework
Hongyi Tang, Zhihao Zhu, Yi Yang
https://arxiv.org/abs/2507.16414 http…

Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework
The performance of large language models (LLMs) is closely tied to their training data, which can include copyrighted material or private information, raising legal and ethical concerns. Additionally, LLMs face criticism for dataset contamination and internalizing biases. To address these issues, the Pre-Training Data Detection (PDD) task was proposed to identify if specific data was included in an LLM's pre-training corpus. However, existing PDD methods often rely on superficial features like …

Quantum optical model of an artificial neuron
Vivek Mehta, Utpal Roy
https://arxiv.org/abs/2507.17349 https://arxiv.org/pdf/2507.17349

Quantum optical model of an artificial neuron
Magnini \emph{et al.} [\emph{Mach. Learn.: Sci. Technol. 1 (2020) 045008}] recently introduced a qubit-based model of an artificial neuron, along with its applications. The design of its quantum circuit is pivotal for effective implementation. In this context, we present two quantum circuit synthesis algorithms tailored for the realisation of the quantum neuron. Comprehensive circuit simulations are conducted, and the resulting performance is assessed using the circuit cost metric. Additionally…

How AI tools from ElevenLabs and others were used to recreate a woman's voice, lost to motor neuron disease, from just eight seconds of audio on a VHS tape (Beth Rose/BBC)
https://www.bbc.com/news/articles/c1ejvxne7elo

How eight seconds of scratchy audio from a VHS tape gave a mum back her voice
Sarah Ezekiel was 34 with two children when motor neurone disease took away her ability to speak.

HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Spikes
Feng Cao, Zishuo Feng
https://arxiv.org/abs/2507.17224 https://…

HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Spikes
Extracellular recordings are brief voltage fluctuations recorded near neurons, widely used in neuroscience as the basis for decoding brain activity at single-neuron resolution. Spike sorting, which assigns each spike to its source neuron, is a critical step in brain sensing pipelines. However, it remains challenging under low signal-to-noise ratio (SNR), electrode drift, and cross-session variability. In this paper, we propose HuiduRep, a robust self-supervised representation learning framework…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
Xiaohan Bi, Binhang Qi, Hailong Sun, Xiang Gao, Yue Yu, Xiaojun Liang
https://arxiv.org/abs/2508.11348

NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
With the growing incorporation of deep neural network (DNN) models into modern software systems, the prohibitive construction costs have become a significant challenge. Model reuse has been widely applied to reduce training costs, but indiscriminately reusing entire models may incur significant inference overhead. Consequently, DNN modularization has gained attention, enabling module reuse by decomposing DNN models. The emerging modularizing-while-training (MwT) paradigm, which incorporates mod…

Game-Theoretic Gradient Control for Robust Neural Network Training
Maria Zaitseva, Ivan Tomilov, Natalia Gusarova
https://arxiv.org/abs/2507.19143 https://…

Game-Theoretic Gradient Control for Robust Neural Network Training
Feed-forward neural networks (FFNNs) are vulnerable to input noise, reducing prediction performance. Existing regularization methods like dropout often alter network architecture or overlook neuron interactions. This study aims to enhance FFNN noise robustness by modifying backpropagation, interpreted as a multi-agent game, and exploring controlled target variable noising. Our "gradient dropout" selectively nullifies hidden layer neuron gradients with probability 1 - p during backpropagation, w…

Correcting model error bias in estimations of neuronal dynamics from time series observations
Ian Williams, Joseph D. Taylor, Alain Nogaret
https://arxiv.org/abs/2508.19948 http…

Correcting model error bias in estimations of neuronal dynamics from time series observations
Neuron models built from experimental data have successfully predicted observed voltage oscillations within and beyond training range. A tantalising prospect is the possibility of estimating the unobserved dynamics of ion channels which is largely inaccessible to experiment, from membrane voltage recordings. The main roadblock here is our lack of knowledge of the equations governing biological neurons which forces us to rely on surrogate models and parameter estimates biassed by model error. Er…

Reconfigurable qubit states and quantum trajectories in a synthetic artificial neuron network with a process to direct information generation from co-integrated burst-mode spiking under non-Markovianity
Osama M. Nayfeh, Chris S. Horne
https://arxiv.org/abs/2507.16669

Reconfigurable qubit states and quantum trajectories in a synthetic artificial neuron network with a process to direct information generation from co-integrated burst-mode spiking under non-Markovianity
A synthetic artificial neuron network functional in a regime where quantum information processes are co-integrated with spiking computation provides significant improvement in the capabilities of neuromorphic systems in performing artificial intelligence and autonomy tasks. This provides the ability to execute with the qubit coherence states and entanglement as well as in tandem to perform functions such as read out and basic arithmetic with conventional spike-encoding. Ultimately, this enables…

Adiabatic Capacitive Neuron: An Energy-Efficient Functional Unit for Artificial Neural Networks
Sachin Maheshwari, Mike Smart, Himadri Singh Raghav, Themis Prodromakis, Alexander Serb
https://arxiv.org/abs/2507.00831

Adiabatic Capacitive Neuron: An Energy-Efficient Functional Unit for Artificial Neural Networks
This paper introduces a new, highly energy-efficient, Adiabatic Capacitive Neuron (ACN) hardware implementation of an Artificial Neuron (AN) with improved functionality, accuracy, robustness and scalability over previous work. The paper describes the implementation of a \mbox{12-bit} single neuron, with positive and negative weight support, in an $\mathbf{0.18μm}$ CMOS technology. The paper also presents a new Threshold Logic (TL) design for a binary AN activation function that generates a low…

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
Ravin Kumar
https://arxiv.org/abs/2507.14270 https://

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
We propose the APTx Neuron, a novel, unified neural computation unit that integrates non-linear activation and linear transformation into a single trainable expression. The APTx Neuron is derived from the APTx activation function, thereby eliminating the need for separate activation layers and making the architecture both computationally efficient and elegant. The proposed neuron follows the functional form $y = \sum_{i=1}^{n} ((α_i + \tanh(β_i x_i)) \cdot γ_i x_i) + δ$, where all parameter…

Depth Gives a False Sense of Privacy: LLM Internal States Inversion
Tian Dong, Yan Meng, Shaofeng Li, Guoxing Chen, Zhen Liu, Haojin Zhu
https://arxiv.org/abs/2507.16372

Depth Gives a False Sense of Privacy: LLM Internal States Inversion
Large Language Models (LLMs) are increasingly integrated into daily routines, yet they raise significant privacy and safety concerns. Recent research proposes collaborative inference, which outsources the early-layer inference to ensure data locality, and introduces model safety auditing based on inner neuron patterns. Both techniques expose the LLM's Internal States (ISs), which are traditionally considered irreversible to inputs due to optimization challenges and the highly abstract represent…

Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?
Louis Vervoort, Vitaly Nikolaev
https://arxiv.org/abs/2506.14239

Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?
We propose a test for abstract causal reasoning in AI, based on scholarship in the philosophy of causation, in particular on the neuron diagrams popularized by D. Lewis. We illustrate the test on advanced Large Language Models (ChatGPT, DeepSeek and Gemini). Remarkably, these chatbots are already capable of correctly identifying causes in cases that are hotly debated in the literature. In order to assess the results of these LLMs and future dedicated AI, we propose a definition of cause in neur…

Why does the membrane potential of biological neuron develop and remain stable?
J\'anos V\'egh
https://arxiv.org/abs/2507.11448 https://

Why does the membrane potential of biological neuron develop and remain stable?
The neuronal electric operation can entirely be described by physics provided that we take into account that slow positive ions instead of fast light electrons are the charge carriers that biology uses. Based on the slow ions, we derive the correct physical model that Hodgkin and Huxley wanted. By using laws of electricity and thermodynamics in a non-disciplinary way, we derive the resting potential from the first principles of science, from the biological "construction" (the thickness of the i…

Ubiquity of Uncertainty in Neuron Systems
Brandon B. Le, Bennett Lamb, Luke Benfer, Sriharsha Sambangi, Nisal Geemal Vismith, Akshaj Jagarapu
https://arxiv.org/abs/2507.15702

Ubiquity of Uncertainty in Neuron Systems
We demonstrate that final-state uncertainty is ubiquitous in multistable systems of coupled neuronal maps, meaning that predicting whether one such system will eventually be chaotic or nonchaotic is often nearly impossible. We propose a "chance synchronization" mechanism that governs the emergence of unpredictability in neuron systems and support it by using basin classification, uncertainty exponent, and basin entropy techniques to analyze five simple discrete-time systems, each consisting of …

Time series analysis of coupled slow-fast neuron models: From Hurst exponent to Granger causality
Indranil Ghosh, Hammed O. Fatoyinbo, Sishu S. Muni
https://arxiv.org/abs/2507.13570

Time series analysis of coupled slow-fast neuron models: From Hurst exponent to Granger causality
We perform time series analysis of small networks where every node is the slow-fast version of the denatured Morris--Lecar neuron proposed by Schaeffer and Cain. We choose popular coupling strategies from the literature and provide a detailed account of how varying their strength drives the dynamics of the small networks. Algorithms for time series analysis range from measuring their persistence (ability to remember past values), irregularity, chaos and quasiperiodicity, to synchronization betw…

A Novel Discrete Memristor-Coupled Heterogeneous Dual-Neuron Model and Its Application in Multi-Scenario Image Encryption
Yi Zou, Mengjiao Wang, Xinan Zhang, Herbert Ho-Ching Iu
https://arxiv.org/abs/2505.24294

A Novel Discrete Memristor-Coupled Heterogeneous Dual-Neuron Model and Its Application in Multi-Scenario Image Encryption
Simulating brain functions using neural networks is an important area of research. Recently, discrete memristor-coupled neurons have attracted significant attention, as memristors effectively mimic synaptic behavior, which is essential for learning and memory. This highlights the biological relevance of such models. This study introduces a discrete memristive heterogeneous dual-neuron network (MHDNN). The stability of the MHDNN is analyzed with respect to initial conditions and a range of neuro…

Language Arithmetics: Towards Systematic Language Neuron Identification and Manipulation
Daniil Gurgurov, Katharina Trinley, Yusser Al Ghussin, Tanja Baeumel, Josef van Genabith, Simon Ostermann
https://arxiv.org/abs/2507.22608

Language Arithmetics: Towards Systematic Language Neuron Identification and Manipulation
Large language models (LLMs) exhibit strong multilingual abilities, yet the neural mechanisms behind language-specific processing remain unclear. We analyze language-specific neurons in Llama-3.1-8B, Mistral-Nemo-12B, and Aya-Expanse-8B & 32B across 21 typologically diverse languages, identifying neurons that control language behavior. Using the Language Activation Probability Entropy (LAPE) method, we show that these neurons cluster in deeper layers, with non-Latin scripts showing greater spec…

Learning Internal Biological Neuron Parameters and Complexity-Based Encoding for Improved Spiking Neural Networks Performance
Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
https://arxiv.org/abs/2508.11674

Learning Internal Biological Neuron Parameters and Complexity-Based Encoding for Improved Spiking Neural Networks Performance
This study introduces a novel approach by replacing the traditional perceptron neuron model with a biologically inspired probabilistic meta neuron, where the internal neuron parameters are jointly learned, leading to improved classification accuracy of spiking neural networks (SNNs). To validate this innovation, we implement and compare two SNN architectures: one based on standard leaky integrate-and-fire (LIF) neurons and another utilizing the proposed probabilistic meta neuron model. As a sec…

Low-intensity brain stimulation may restore neuron health in Alzheimer's disease https://spie.org/news/low-intensity-brain-stimulation-may-restore-neuron-health-in-alzheimers-disease

Viability of perturbative expansion for quantum field theories on neurons
Srimoyee Sen, Varun Vaidya
https://arxiv.org/abs/2508.03810 https://arxiv.org/pdf…

Viability of perturbative expansion for quantum field theories on neurons
Neural Network (NN) architectures that break statistical independence of parameters have been proposed as a new approach for simulating local quantum field theories (QFTs). 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 $O(1/N)$ …

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Benchmarking Spiking Neurons for Linear Quadratic Regulator Control of Multi-linked Pole on a Cart: from Single Neuron to Ensemble
Shreyan Banerjee, Luna Gava, Aasifa Rounak, Vikram Pakrashi
https://arxiv.org/abs/2507.03621

Benchmarking Spiking Neurons for Linear Quadratic Regulator Control of Multi-linked Pole on a Cart: from Single Neuron to Ensemble
The emerging field of neuromorphic computing for edge control applications poses the need to quantitatively estimate and limit the number of spiking neurons, to reduce network complexity and optimize the number of neurons per core and hence, the chip size, in an application-specific neuromorphic hardware. While rate-encoding for spiking neurons provides a robust way to encode signals with the same number of neurons as an ANN, it often lacks precision. To achieve the desired accuracy, a populati…

Steering Conceptual Bias via Transformer Latent-Subspace Activation
Vansh Sharma, Venkat Raman
https://arxiv.org/abs/2506.18887 https://

Steering Conceptual Bias via Transformer Latent-Subspace Activation
This work examines whether activating latent subspaces in language models (LLMs) can steer scientific code generation toward a specific programming language. Five causal LLMs were first evaluated on scientific coding prompts to quantify their baseline bias among four programming languages. A static neuron-attribution method, perturbing the highest activated MLP weight for a C++ or CPP token, proved brittle and exhibited limited generalization across prompt styles and model scales. To address th…

This https://arxiv.org/abs/2409.09268 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Spintronic Neuron Using a Magnetic Tunnel Junction for Low-Power Neuromorphic Computing
This paper proposes a novel spiking artificial neuron design based on a combined spin valve/magnetic tunnel junction (SV/MTJ). Traditional hardware used in artificial intelligence and machine learning faces significant challenges related to high power consumption and scalability. To address these challenges, spintronic neurons, which can mimic biologically inspired neural behaviors, offer a promising solution. We present a model of an SV/MTJ-based neuron which uses technologies that have been s…

Analyzing Internal Activity and Robustness of SNNs Across Neuron Parameter Space
Szymon Mazurek, Jakub Caputa, Maciej Wielgosz
https://arxiv.org/abs/2507.14757

Analyzing Internal Activity and Robustness of SNNs Across Neuron Parameter Space
Spiking Neural Networks (SNNs) offer energy-efficient and biologically plausible alternatives to traditional artificial neural networks, but their performance depends critically on the tuning of neuron model parameters. In this work, we identify and characterize an operational space - a constrained region in the neuron hyperparameter domain (specifically membrane time constant tau and voltage threshold vth) - within which the network exhibits meaningful activity and functional behavior. Operati…

Evolutionary chemical learning in dimerization networks
Alexei V. Tkachenko, Bortolo Matteo Mognetti, Sergei Maslov
https://arxiv.org/abs/2506.14006 https:…

Evolutionary chemical learning in dimerization networks
We present a novel framework for chemical learning based on Competitive Dimerization Networks (CDNs) - systems in which multiple molecular species, e.g. proteins or DNA/RNA oligomers, reversibly bind to form dimers. We show that these networks can be trained in vitro through directed evolution, enabling the implementation of complex learning tasks such as multiclass classification without digital hardware or explicit parameter tuning. Each molecular species functions analogously to a neuron, wi…

Identifying multi-compartment Hodgkin-Huxley models with high-density extracellular voltage recordings
Ian Christopher Tanoh, Michael Deistler, Jakob H. Macke, Scott W. Linderman
https://arxiv.org/abs/2506.20233

Identifying multi-compartment Hodgkin-Huxley models with high-density extracellular voltage recordings
Multi-compartment Hodgkin-Huxley models are biophysical models of how electrical signals propagate throughout a neuron, and they form the basis of our knowledge of neural computation at the cellular level. However, these models have many free parameters that must be estimated for each cell, and existing fitting methods rely on intracellular voltage measurements that are highly challenging to obtain in vivo. Recent advances in neural recording technology with high-density probes and arrays enabl…

Structured First-Layer Initialization Pre-Training Techniques to Accelerate Training Process Based on $\varepsilon$-Rank
Tao Tang, Jiang Yang, Yuxiang Zhao, Quanhui Zhu
https://arxiv.org/abs/2507.11962

Structured First-Layer Initialization Pre-Training Techniques to Accelerate Training Process Based on $\varepsilon$-Rank
Training deep neural networks for scientific computing remains computationally expensive due to the slow formation of diverse feature representations in early training stages. Recent studies identify a staircase phenomenon in training dynamics, where loss decreases are closely correlated with increases in $\varepsilon$-rank, reflecting the effective number of linearly independent neuron functions. Motivated by this observation, this work proposes a structured first-layer initialization (SFLI) p…

This https://arxiv.org/abs/2506.00691 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Optimizing Sensory Neurons: Nonlinear Attention Mechanisms for Accelerated Convergence in Permutation-Invariant Neural Networks for Reinforcement Learning
Training reinforcement learning (RL) agents often requires significant computational resources and extended training times. To address this, we build upon the foundation laid by Google Brain's Sensory Neuron, which introduced a novel neural architecture for reinforcement learning tasks that maintained permutation in-variance in the sensory neuron system. While the baseline model demonstrated significant performance improvements over traditional approaches, we identified opportunities to enhance…

SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption
Zhiqiang Wang, Haohua Du, Junyang Wang, Haifeng Sun, Kaiwen Guo, Haikuo Yu, Chao Liu, Xiang-Yang Li
https://arxiv.org/abs/2506.05242

SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption
Large language models (LLMs) with diverse capabilities are increasingly being deployed in local environments, presenting significant security and controllability challenges. These locally deployed LLMs operate outside the direct control of developers, rendering them more susceptible to abuse. Existing mitigation techniques mainly designed for cloud-based LLM services are frequently circumvented or ineffective in deployer-controlled environments. We propose SECNEURON, the first framework that se…

Stable Synchronous Propagation in Feedforward Networks for Biped Locomotion
Ian Stewart, David Wood
https://arxiv.org/abs/2506.11780 https://

Stable Synchronous Propagation in Feedforward Networks for Biped Locomotion
Rhythmic gait patterns in animal locomotion are widely believed to be produced by a central pattern generator (CPG), a network of neurons that drives the muscle groups. In previous papers we have discussed how phase-synchronous signals can propagate along chains of neurons using a feedforward lift of the CPG, given sufficient conditions for stability to synchrony-breaking perturbations, and shown that stable signals are common for four standard neuron models. Here we apply these ideas to bipe…

Computational Economics in Large Language Models: Exploring Model Behavior and Incentive Design under Resource Constraints
Sandeep Reddy, Kabir Khan, Rohit Patil, Ananya Chakraborty, Faizan A. Khan, Swati Kulkarni, Arjun Verma, Neha Singh
https://arxiv.org/abs/2508.10426

Computational Economics in Large Language Models: Exploring Model Behavior and Incentive Design under Resource Constraints
Large language models (LLMs) are limited by substantial computational cost. We introduce a "computational economics" framework that treats an LLM as an internal economy of resource-constrained agents (attention heads and neuron blocks) that must allocate scarce computation to maximize task utility. First, we show empirically that when computation is scarce, standard LLMs reallocate attention toward high-value tokens while preserving accuracy. Building on this observation, we propose an incentiv…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Replaced article(s) found for q-bio.TO. https://arxiv.org/list/q-bio.TO/new
[1/1]:
- Revisiting convolutive blind source separation for identifying spiking motor neuron activity: Fro...
Thomas Klotz, Robin Rohl\'en

Impact of Hill coefficient and time delay on a perceptual decision-making model
Bart{\l}omiej Morawski, Anna Czartoszewska
https://arxiv.org/abs/2506.19853

Impact of Hill coefficient and time delay on a perceptual decision-making model
In this paper, a neural mass perceptual decision making model introduced by Piskała et al. is analyzed. The model describes activity of two neuron populations influenced by each other and external inputs. The groups' activities correspond to the process of making a perceptual binary decision. Existing results are generalized by investigating the impact of both a delay in self-inhibition and a generic Hill coefficient on solutions to the system of differential equations. Several versions of the…

A Hybrid Artificial Intelligence Method for Estimating Flicker in Power Systems
Javad Enayati, Pedram Asef, Alexandre Benoit
https://arxiv.org/abs/2506.13611

A Hybrid Artificial Intelligence Method for Estimating Flicker in Power Systems
This paper introduces a novel hybrid AI method combining H filtering and an adaptive linear neuron network for flicker component estimation in power distribution systems.The proposed method leverages the robustness of the H filter to extract the voltage envelope under uncertain and noisy conditions followed by the use of ADALINE to accurately identify flicker frequencies embedded in the envelope.This synergy enables efficient time domain estimation with rapid convergence and noise resilience ad…

Beyond Rate Coding: Surrogate Gradients Enable Spike Timing Learning in Spiking Neural Networks
Ziqiao Yu, Pengfei Sun, Dan F. M. Goodman
https://arxiv.org/abs/2507.16043 https:…

Beyond Rate Coding: Surrogate Gradients Enable Spike Timing Learning in Spiking Neural Networks
We investigate the extent to which Spiking Neural Networks (SNNs) trained with Surrogate Gradient Descent (Surrogate GD), with and without delay learning, can learn from precise spike timing beyond firing rates. We first design synthetic tasks isolating intra-neuron inter-spike intervals and cross-neuron synchrony under matched spike counts. On more complex spike-based speech recognition datasets (Spiking Heidelberg Digits (SHD) and Spiking Speech Commands (SSC), we construct variants where spi…

Hawkes Processes with Variable Length Memory: Existence, Inference and Application to Neuronal Activity
Sacha Quayle, Anna Bonnet, Maxime Sangnier
https://arxiv.org/abs/2507.22867

Hawkes Processes with Variable Length Memory: Existence, Inference and Application to Neuronal Activity
Motivated by applications in neuroscience, where the memory of a neuron may reset upon firing, we introduce a new class of nonlinear Hawkes processes with variable length memory. Multivariate Hawkes processes are past-dependant point processes originally introduced tomodel excitation effects, later extended to a nonlinear framework to account for the opposite effect, known as inhibition. Our model generalises classical Hawkes processes, with or without inhibition, focusing on the situation wher…

Convergent and divergent connectivity patterns of the arcuate fasciculus in macaques and humans
Jiahao Huang, Ruifeng Li, Wenwen Yu, Anan Li, Xiangning Li, Mingchao Yan, Lei Xie, Qingrun Zeng, Xueyan Jia, Shuxin Wang, Ronghui Ju, Feng Chen, Qingming Luo, Hui Gong, Xiaoquan Yang, Yuanjing Feng, Zheng Wang
https://arxiv.org/abs/25…

Convergent and divergent connectivity patterns of the arcuate fasciculus in macaques and humans
The organization and connectivity of the arcuate fasciculus (AF) in nonhuman primates remain contentious, especially concerning how its anatomy diverges from that of humans. Here, we combined cross-scale single-neuron tracing - using viral-based genetic labeling and fluorescence micro-optical sectioning tomography in macaques (n = 4; age 3 - 11 years) - with whole-brain tractography from 11.7T diffusion MRI. Complemented by spectral embedding analysis of 7.0T MRI in humans, we performed a compa…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Decision-making in light-trapped slime molds involves active mechanical processes
Lisa Schick, Emily Eichenlaub, Fabian Drexel, Alexander Mayer, Siyu Chen, Marcus Roper, Karen Alim
https://arxiv.org/abs/2506.12803

Decision-making in light-trapped slime molds involves active mechanical processes
Decision-making is the process of selecting an action among alternatives, allowing biological and artificial systems to navigate complex environments and optimize behavior. While neural systems rely on neuron-based sensory processing and evaluation, decision-making also occurs in organisms without a centralized organizing unit, such as the unicellular slime mold \textit{Physarum polycephalum}. Unlike neural systems, P. polycephalum relies on rhythmic peristaltic contractions to drive internal f…

Energy Efficient p-Circuits for Generative Neural Networks
Lakshmi A. Ghantasala, Ming-Che Li, Risi Jaiswal, Archisman Ghosh, Behtash Behin-Aein, Joseph Makin, Shreyas Sen, Supriyo Datta
https://arxiv.org/abs/2507.07763

Energy Efficient p-Circuits for Generative Neural Networks
A p-circuit comprising a network of $N$ p-bits can generate samples from a probability distribution with $2^N$ possibilities just like a network of q-bits, though there are interesting fundamental differences. A p-bit is essentially a binary stochastic neuron used in Boltzmann machines which involve the repeated application of a specific elementary operation, whereby each p-bit looks at the state of 'n' other p-bits, computes the probability for being $0$ or $1$, and chooses its next state acco…

Benchmarking spike source localization algorithms in high density probes
Hao Zhao, Xinhe Zhang, Arnau Marin-Llobet, Xinyi Lin, Jia Liu
https://arxiv.org/abs/2508.13451 https://

Benchmarking spike source localization algorithms in high density probes
Estimating neuron location from extracellular recordings is essential for developing advanced brain-machine interfaces. Accurate neuron localization improves spike sorting, which involves detecting action potentials and assigning them to individual neurons. It also helps monitor probe drift, which affects long-term probe reliability. Although several localization algorithms are currently in use, the field is nascent and arguments for using one algorithm over another are largely theoretical or b…

Organic Electrochemical Neurons: Nonlinear Tools for Complex Dynamics
Gonzalo Rivera-Sierra, Roberto Fenollosa, Juan Bisquert
https://arxiv.org/abs/2508.00663 https://

Organic Electrochemical Neurons: Nonlinear Tools for Complex Dynamics
Hybrid oscillator architectures that combine feedback oscillators with self-sustained negative resistance oscillators have emerged as a promising platform for artificial neuron design. In this work, we introduce a modeling and analysis framework for amplifier-assisted organic electrochemical neurons, leveraging nonlinear dynamical systems theory. By formulating the system as coupled differential equations describing membrane voltage and internal state variables, we identify the conditions for s…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Topographic alignment of auditory inputs to the visual cortex (in mice) https://www.biorxiv.org/content/10.1101/2025.07.29.667237v1 More information in the Bluesky thread

IzhiRISC-V -- a RISC-V-based Processor with Custom ISA Extension for Spiking Neuron Networks Processing with Izhikevich Neurons
Wiktor J. Szczerek, Artur Podobas
https://arxiv.org/abs/2508.12846

IzhiRISC-V -- a RISC-V-based Processor with Custom ISA Extension for Spiking Neuron Networks Processing with Izhikevich Neurons
Spiking Neural Network processing promises to provide high energy efficiency due to the sparsity of the spiking events. However, when realized on general-purpose hardware -- such as a RISC-V processor -- this promise can be undermined and overshadowed by the inefficient code, stemming from repeated usage of basic instructions for updating all the neurons in the network. One of the possible solutions to this issue is the introduction of a custom ISA extension with neuromorphic instructions for s…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Pixel Embedding Method for Tubular Neurite Segmentation
Huayu Fu, Jiamin Li, Haozhi Qu, Xiaolin Hu, Zengcai Guo
https://arxiv.org/abs/2507.23359 https://ar…

Pixel Embedding Method for Tubular Neurite Segmentation
Automatic segmentation of neuronal topology is critical for handling large scale neuroimaging data, as it can greatly accelerate neuron annotation and analysis. However, the intricate morphology of neuronal branches and the occlusions among fibers pose significant challenges for deep learning based segmentation. To address these issues, we propose an improved framework: First, we introduce a deep network that outputs pixel level embedding vectors and design a corresponding loss function, enabli…

SDSNN: A Single-Timestep Spiking Neural Network with Self-Dropping Neuron and Bayesian Optimization
Changqing Xu, Buxuan Song, Yi Liu, Xinfang Liao, Wenbin Zheng, Yintang Yang
https://arxiv.org/abs/2508.10913

SDSNN: A Single-Timestep Spiking Neural Network with Self-Dropping Neuron and Bayesian Optimization
Spiking Neural Networks (SNNs), as an emerging biologically inspired computational model, demonstrate significant energy efficiency advantages due to their event-driven information processing mechanism. Compared to traditional Artificial Neural Networks (ANNs), SNNs transmit information through discrete spike signals, which substantially reduces computational energy consumption through their sparse encoding approach. However, the multi-timestep computation model significantly increases inferenc…

Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization
Sergii Kavun
https://arxiv.org/abs/2507.22090 https://

Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization
Activation functions are critical components in deep neural networks, directly influencing gradient flow, training stability, and model performance. Traditional functions like ReLU suffer from dead neuron problems, while sigmoid and tanh exhibit vanishing gradient issues. We introduce two novel hybrid activation functions: S3 (Sigmoid-Softsign) and its improved version S4 (smoothed S3). S3 combines sigmoid for negative inputs with softsign for positive inputs, while S4 employs a smooth transiti…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants #BCI

Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants
Brain–computer interfaces have enabled people with paralysis to control computer cursors, operate prosthetic limbs, and communicate through handwriting, speech, and typing. Most high-performance demonstrations have used silicon microelectrode "Utah" arrays to record brain activity at single neuron resolution. However, reports so far have typically been limited to one or two individuals, with no systematic assessment of the longevity, decoding accuracy, and day–to–day stability properties …

Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
Filippo Marostica, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
https://arxiv.org/abs/2506.13268

Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
This paper presents a comprehensive evaluation of Spiking Neural Network (SNN) neuron models for hardware acceleration by comparing event driven and clock-driven implementations. We begin our investigation in software, rapidly prototyping and testing various SNN models based on different variants of the Leaky Integrate and Fire (LIF) neuron across multiple datasets. This phase enables controlled performance assessment and informs design refinement. Our subsequent hardware phase, implemented on …

Measuring the entropy of a neuron cell from its membrane current signal
Mahmut Akilli
https://arxiv.org/abs/2508.00968 https://arxiv.org/pdf/2508.00968

Measuring the entropy of a neuron cell from its membrane current signal
The purpose of this study was to investigate how the entropy of a neuron cell can be measured using membrane ion current signals, which were recorded from neurons in the mouse medial prefrontal cortex (mPFC). The sample entropy and the Scalogram entropy were used as entropy measurement methods. It is well known that the entropy increases in the direction of the movement of the system towards the equilibrium. Therefore, in the process of the electrical activity of a living cell, the entropy is e…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Integer Binary-Range Alignment Neuron for Spiking Neural Networks
Binghao Ye, Wenjuan Li, Dong Wang, Man Yao, Bing Li, Weiming Hu, Dong Liang, Kun Shang
https://arxiv.org/abs/2506.05679

Integer Binary-Range Alignment Neuron for Spiking Neural Networks
Spiking Neural Networks (SNNs) are noted for their brain-like computation and energy efficiency, but their performance lags behind Artificial Neural Networks (ANNs) in tasks like image classification and object detection due to the limited representational capacity. To address this, we propose a novel spiking neuron, Integer Binary-Range Alignment Leaky Integrate-and-Fire to exponentially expand the information expression capacity of spiking neurons with only a slight energy increase. This is a…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Field-theoretic approach to compartmental neuronal networks: impact of dendritic calcium spike-dependent bursting
Audrey O'Brien Teasley, Gabriel Koch Ocker
https://arxiv.org/abs/2508.08405

Field-theoretic approach to compartmental neuronal networks: impact of dendritic calcium spike-dependent bursting
Neurons are spatially extended cells; different parts of a neuron have specific voltage dynamics. Important types of neurons even generate different spikes in different parts of the cell. Neurons' inputs are also often spatially compartmentalized, with different sources targeting different locations on the cell. Classic mean-field theories for neural population activity, however, rely on point-neuron models with at most one type of spike. Here, we develop a statistical field-theoretic approach …

Replaced article(s) found for nlin.CD. https://arxiv.org/list/nlin.CD/new
[1/1]:
- Hyperchaos and complex dynamical regimes in $N$-dimensional neuron lattices
Brandon B. Le, Dima Watkins

Low dimensional dynamics of a sparse balanced synaptic network of quadratic integrate-and-fire neurons
Maria V. Ageeva, Denis S. Goldobin
https://arxiv.org/abs/2508.06253 https:…

Low dimensional dynamics of a sparse balanced synaptic network of quadratic integrate-and-fire neurons
Kinetics of a balanced network of neurons with a sparse grid of synaptic links is well representable by the stochastic dynamics of a generic neuron subject to an effective shot noise. The rate of delta-pulses of the noise is determined self-consistently from the probability density of the neuron states. Importantly, the most sophisticated (but robust) collective regimes of the network do not allow for the diffusion approximation, which is routinely adopted for a shot noise in mathematical neuro…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

A Neuronal Model at the Edge of Criticality: An Ising-Inspired Approach to Brain Dynamics
Sajedeh Sarmastani, Maliheh Ghodrat, Yousef Jamali
https://arxiv.org/abs/2506.07027

A Neuronal Model at the Edge of Criticality: An Ising-Inspired Approach to Brain Dynamics
We present a neuronal network model inspired by the Ising model, where each neuron is a binary spin ($s_i = \pm1$) interacting with its neighbors on a 2D lattice. Updates are asynchronous and follow Metropolis dynamics, with a temperature-like parameter $T$ introducing stochasticity. To incorporate physiological realism, each neuron includes fixed on/off durations, mimicking the refractory period found in real neurons. These counters prevent immediate reactivation, adding biologically grounde…

Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods
Liying Tao, Zonglin Yang, Delong Shang
https://arxiv.org/abs/2507.02960

Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods
The refractory period controls neuron spike firing rate, crucial for network stability and noise resistance. With advancements in spiking neural network (SNN) training methods, low-latency SNN applications have expanded. In low-latency SNNs, shorter simulation steps render traditional refractory mechanisms, which rely on empirical distributions or spike firing rates, less effective. However, omitting the refractory period amplifies the risk of neuron over-activation and reduces the system's rob…

Minimal Neuron Circuits -- Part I: Resonators
Amr Nabil, T. Nandha Kumar, Haider Abbas F. Almurib
https://arxiv.org/abs/2506.02341 https://

Minimal Neuron Circuits -- Part I: Resonators
Spiking Neural Networks have earned increased recognition in recent years owing to their biological plausibility and event-driven computation. Spiking neurons are the fundamental building components of Spiking Neural Networks. Those neurons act as computational units that determine the decision to fire an action potential. This work presents a methodology to implement biologically plausible yet scalable spiking neurons in hardware. We show that it is more efficient to design neurons that mimic …

Identifying interactions across brain areas while accounting for individual-neuron dynamics with a Transformer-based variational autoencoder
Qi Xin, Robert E. Kass
https://arxiv.org/abs/2506.02263

Identifying interactions across brain areas while accounting for individual-neuron dynamics with a Transformer-based variational autoencoder
Advances in large-scale recording technologies now enable simultaneous measurements from multiple brain areas, offering new opportunities to study signal transmission across interacting components of neural circuits. However, neural responses exhibit substantial trial-to-trial variability, often driven by unobserved factors such as subtle changes in animal behavior or internal states. To prevent evolving background dynamics from contaminating identification of functional coupling, we developed …

Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows
Peter Bouss, Sandra Nester, Kirsten Fischer, Claudia Merger, Alexandre Ren\'e, Moritz Helias
https://arxiv.org/abs/2506.12187

Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows
Neuronal activity is found to lie on low-dimensional manifolds embedded within the high-dimensional neuron space. Variants of principal component analysis are frequently employed to assess these manifolds. These methods are, however, limited by assuming a Gaussian data distribution and a flat manifold. In this study, we introduce a method designed to satisfy three core objectives: (1) extract coordinated activity across neurons, described either statistically as correlations or geometrically as…

Neuromorphic Online Clustering and Its Application to Spike Sorting
James E. Smith
https://arxiv.org/abs/2506.12555 https://arxiv.org…

Neuromorphic Online Clustering and Its Application to Spike Sorting
Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. …

Tangma: A Tanh-Guided Activation Function with Learnable Parameters
Shreel Golwala
https://arxiv.org/abs/2507.10560 https://arxiv.org…

Tangma: A Tanh-Guided Activation Function with Learnable Parameters
Activation functions are key to effective backpropagation and expressiveness in deep neural networks. This work introduces Tangma, a new activation function that combines the smooth shape of the hyperbolic tangent with two learnable parameters: $α$, which shifts the curve's inflection point to adjust neuron activation, and $γ$, which adds linearity to preserve weak gradients and improve training stability. Tangma was evaluated on MNIST and CIFAR-10 using custom networks composed of convolutio…

Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex
Xavier Vasques, Laura Cif
https://arxiv.org/abs/2508.06514

Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex
The accurate classification of neuronal cell types is central to decoding brain function, yet remains hindered by data scarcity and cellular heterogeneity. Here, we benchmarked classical and deep generative synthetic data augmentation strategies -- including SMOTE, GANs, VAEs, Normalizing Flows, and DDPMs -- for supervised classification of both electrophysiological (e-type) and morpho-electrophysiological (mee-type) neuron types from the mouse visual cortex. Using a curated dataset annotated w…

Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
Maxime Fabre, Lyubov Dudchenko, Emre Neftci
https://arxiv.org/abs/2506.06374

Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
Multi-state spiking neurons such as the adaptive leaky integrate-and-fire (AdLIF) neuron offer compelling alternatives to conventional deep learning models thanks to their sparse binary activations, second-order nonlinear recurrent dynamics, and efficient hardware realizations. However, such internal dynamics can cause instabilities during inference and training, often limiting performance and scalability. Meanwhile, state space models (SSMs) excel in long sequence processing using linear state…

A Practical Guide to Tuning Spiking Neuronal Dynamics
William Gebhardt, Alexander G. Ororbia, Nathan McDonald, Clare Thiem, Jack Lombardi
https://arxiv.org/abs/2506.08138

A Practical Guide to Tuning Spiking Neuronal Dynamics
In this work, we examine fundamental elements of spiking neural networks (SNNs) as well as how to tune them. Concretely, we focus on two different foundational neuronal units utilized in SNNs -- the leaky integrate-and-fire (LIF) and the resonate-and-fire (RAF) neuron. We explore key equations and how hyperparameter values affect behavior. Beyond hyperparameters, we discuss other important design elements of SNNs -- the choice of input encoding and the setup for excitatory-inhibitory population…

Learning to cluster neuronal function
Nina S. Nellen, Polina Turishcheva, Michaela Vystr\v{c}ilov\'a, Shashwat Sridhar, Tim Gollisch, Andreas S. Tolias, Alexander S. Ecker
https://arxiv.org/abs/2506.03293

Learning to cluster neuronal function
Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embeddings derived from these models could potentially be used to map the functional landscape or identify cell types. However, state-of-the-art predictive models of mouse V1 do not generate functional embeddings that exhibit clear clustering patterns which would correspond to cell types. This raises the question whether th…

From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems
Kun Jiang
https://arxiv.org/abs/2507.22916 https://arxiv…

From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems
In our previous work, we proposed a novel neuron model based on symmetric differential equations and demonstrated its potential as an efficient signal propagator. Building upon that foundation, the present study delves deeper into the intrinsic dynamics and functional diversity of this model. By systematically exploring the parameter space and employing a range of mathematical analysis tools, we theoretically reveal the system 's core property of functional duality. Specifically, the model exhi…

Pendulum Model of Spiking Neurons
Joy Bose
https://arxiv.org/abs/2507.22146 https://arxiv.org/pdf/2507.22146

Pendulum Model of Spiking Neurons
We propose a biologically inspired model of spiking neurons based on the dynamics of a damped, driven pendulum. Unlike traditional models such as the Leaky Integrate-and-Fire (LIF) neurons, the pendulum neuron incorporates second-order, nonlinear dynamics that naturally give rise to oscillatory behavior and phase-based spike encoding. This model captures richer temporal features and supports timing-sensitive computations critical for sequence processing and symbolic learning. We present an anal…