When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning

• URL: http://arxiv.org/abs/2102.01316v1
• Date: Tue, 2 Feb 2021 05:16:55 GMT
• Title: When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning
• Abstract summary: We demonstrate Resonance (SR) phenomenon in Neurochaos Learning (NL) SR manifests at the level of a single neuron of NL and enables efficient subthreshold signal detection. Intermediate levels of noise in neurochaos learning enables peak performance in classification tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chaos and Noise are ubiquitous in the Brain. Inspired by the chaotic firing of neurons and the constructive role of noise in neuronal models, we for the first time connect chaos, noise and learning. In this paper, we demonstrate Stochastic Resonance (SR) phenomenon in Neurochaos Learning (NL). SR manifests at the level of a single neuron of NL and enables efficient subthreshold signal detection. Furthermore, SR is shown to occur in single and multiple neuronal NL architecture for classification tasks - both on simulated and real-world spoken digit datasets. Intermediate levels of noise in neurochaos learning enables peak performance in classification tasks thus highlighting the role of SR in AI applications, especially in brain inspired learning architectures.

Related papers

• Towards Interpretable Hallucination Analysis and Mitigation in LVLMs via Contrastive Neuron Steering [60.23509717784518]
  Existing mitigation methods predominantly focus on output-level adjustments, leaving internal mechanisms that give rise to hallucinations largely unexplored.<n>We propose Contrastive Neuron Steering ( CNS), which identifies image-specific neurons via contrastive analysis between clean and noisy inputs.<n> CNS selectively amplifies informative neurons while suppressing perturbation-induced activations, producing more robust and semantically grounded visual representations.
  arXiv  Detail & Related papers  (2026-01-31T09:21:04Z)
• Paradoxical noise preference in RNNs [0.0]
  In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biological variability and regularize learning.<n>We find that continuous-time recurrent neural networks (CTRNNs) often perform best at a nonzero noise level, specifically, the same level used during training.<n>This noise preference typically arises when noise is injected inside the neural activation function; networks trained with noise injected outside the activation function perform best with zero noise.
  arXiv  Detail & Related papers  (2026-01-08T03:11:51Z)
• Neuronal Group Communication for Efficient Neural representation [85.36421257648294]
  This paper addresses the question of how to build large neural systems that learn efficient, modular, and interpretable representations.<n>We propose Neuronal Group Communication (NGC), a theory-driven framework that reimagines a neural network as a dynamical system of interacting neuronal groups.<n>NGC treats weights as transient interactions between embedding-like neuronal states, with neural computation unfolding through iterative communication among groups of neurons.
  arXiv  Detail & Related papers  (2025-10-19T14:23:35Z)
• BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics [8.36470471250669]
  Decoding speech directly from neural activity is a central goal in brain-computer interface (BCI) research.<n>In recent years, exciting advances have been made through the growing use of intracranial field potential recordings, such as stereo-ElectroEncephaloGraphy (sEEG) and ElectroCorticoGraphy (ECoG)<n>These neural signals capture rich population-level activity but present key challenges: (i) task-relevant neural signals are sparsely distributed across sEEG electrodes, and (ii) they are often entangled with task-irrelevant neural signals in both sEEG and ECo
  arXiv  Detail & Related papers  (2025-05-26T19:36:39Z)
• Spatiotemporal Learning of Brain Dynamics from fMRI Using Frequency-Specific Multi-Band Attention for Cognitive and Psychiatric Applications [5.199807441687141]
  We introduce Multi-Band Brain Net (MBBN), a transformer-based framework that models frequency-specific brain dynamics from fMRI. Trained on three large-scale neuroimaging cohorts totaling 45,951 individuals, MBBN reveals previously undetectable frequency-dependent network interactions. MBBN up to 30.59% higher predictive accuracy than state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-30T10:56:50Z)
• Artificial Kuramoto Oscillatory Neurons [65.16453738828672]
  We introduce Artificial Kuramotoy Neurons (AKOrN) as a dynamical alternative to threshold units. We show that this idea provides performance improvements across a wide spectrum of tasks. We believe that these empirical results show the importance of our assumptions at the most basic neuronal level of neural representation.
  arXiv  Detail & Related papers  (2024-10-17T17:47:54Z)
• CrEIMBO: Cross Ensemble Interactions in Multi-view Brain Observations [3.3713037259290255]
  CrEIMBO (Cross-Ensemble Interactions in Multi-view Brain Observations) identifies the composition of per-session neural ensembles. CrEIMBO distinguishes session-specific from global (session-invariant) computations by exploring when distinct sub-circuits are active. We demonstrate CrEIMBO's ability to recover ground truth components in synthetic data and uncover meaningful brain dynamics.
  arXiv  Detail & Related papers  (2024-05-27T17:48:32Z)
• Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
  We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks. We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
  arXiv  Detail & Related papers  (2024-04-22T09:40:07Z)
• Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data [0.0]
  We propose a novel unsupervised learning framework, Neural Latent Aligner (NLA), to find well-constrained, behaviorally relevant neural representations of complex behaviors. The proposed framework learns more cross-trial consistent representations than the baselines, and when visualized, the manifold reveals shared neural trajectories across trials.
  arXiv  Detail & Related papers  (2023-08-12T02:35:24Z)
• Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition [8.998797644039064]
  The spiking neural network (SNN) using leaky-integrated-and-fire (LIF) neurons has been commonly used in automatic speech recognition (ASR) tasks. Here we introduce four types of neuronal dynamics to post-process the sequential patterns generated from the spiking transformer. We found that the DyTr-SNN could handle the non-toy automatic speech recognition task well, representing a lower phoneme error rate, lower computational cost, and higher robustness.
  arXiv  Detail & Related papers  (2023-02-02T16:20:27Z)
• STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer [19.329190789275565]
  We introduce SpatioTemporal Neural Data Transformer (STNDT), an NDT-based architecture that explicitly models responses of individual neurons. We show that our model achieves state-of-the-art performance on ensemble level in estimating neural activities across four neural datasets.
  arXiv  Detail & Related papers  (2022-06-09T18:54:23Z)
• Learning Personal Representations from fMRIby Predicting Neurofeedback Performance [52.77024349608834]
  We present a deep neural network method for learning a personal representation for individuals performing a self neuromodulation task, guided by functional MRI (fMRI) The representation is learned by a self-supervised recurrent neural network, that predicts the Amygdala activity in the next fMRI frame given recent fMRI frames and is conditioned on the learned individual representation.
  arXiv  Detail & Related papers  (2021-12-06T10:16:54Z)
• Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment [57.91534223695695]
  We evolved weights in a biologically plausible recurrent neural network (RNN) using an evolutionary algorithm to replicate the behavior and neural activity observed in rats. Our method demonstrates how the dynamic activity in evolved RNNs can capture interesting and complex cognitive behavior.
  arXiv  Detail & Related papers  (2021-02-25T02:13:52Z)
• Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting [135.0863818867184]
  artificial neural variability (ANV) helps artificial neural networks learn some advantages from natural'' neural networks. ANV plays as an implicit regularizer of the mutual information between the training data and the learned model. It can effectively relieve overfitting, label noise memorization, and catastrophic forgetting at negligible costs.
  arXiv  Detail & Related papers  (2020-11-12T06:06:33Z)
• Non-linear Neurons with Human-like Apical Dendrite Activations [81.18416067005538]
  We show that a standard neuron followed by our novel apical dendrite activation (ADA) can learn the XOR logical function with 100% accuracy. We conduct experiments on six benchmark data sets from computer vision, signal processing and natural language processing.
  arXiv  Detail & Related papers  (2020-02-02T21:09:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.