Sensory robustness through top-down feedback and neural stochasticity in recurrent vision models

• URL: http://arxiv.org/abs/2508.07115v1
• Date: Sat, 09 Aug 2025 22:51:50 GMT
• Title: Sensory robustness through top-down feedback and neural stochasticity in recurrent vision models
• Authors: Antonino Greco, Marco D'Alessandro, Karl J. Friston, Giovanni Pezzulo, Markus Siegel,
• Abstract summary: We trained convolutional recurrent neural networks (ConvRNN) on image classification in the presence or absence of top-down feedback projections.<n>We found that ConvRNNs with top-down feedback exhibited remarkable speed-accuracy trade-off and robustness to noise perturbations and adversarial attacks.
• Score: 0.9188951403098383
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Biological systems leverage top-down feedback for visual processing, yet most artificial vision models succeed in image classification using purely feedforward or recurrent architectures, calling into question the functional significance of descending cortical pathways. Here, we trained convolutional recurrent neural networks (ConvRNN) on image classification in the presence or absence of top-down feedback projections to elucidate the specific computational contributions of those feedback pathways. We found that ConvRNNs with top-down feedback exhibited remarkable speed-accuracy trade-off and robustness to noise perturbations and adversarial attacks, but only when they were trained with stochastic neural variability, simulated by randomly silencing single units via dropout. By performing detailed analyses to identify the reasons for such benefits, we observed that feedback information substantially shaped the representational geometry of the post-integration layer, combining the bottom-up and top-down streams, and this effect was amplified by dropout. Moreover, feedback signals coupled with dropout optimally constrained network activity onto a low-dimensional manifold and encoded object information more efficiently in out-of-distribution regimes, with top-down information stabilizing the representational dynamics at the population level. Together, these findings uncover a dual mechanism for resilient sensory coding. On the one hand, neural stochasticity prevents unit-level co-adaptation albeit at the cost of more chaotic dynamics. On the other hand, top-down feedback harnesses high-level information to stabilize network activity on compact low-dimensional manifolds.

Related papers

• ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling [57.91760520589592]
  Scaling network depth has been a central driver behind the success of modern foundation models.<n>This paper revisits the default mechanism for deepening neural networks, namely residual connections.<n>We introduce adaptive neural connection reassignment (ANCRe), a principled and lightweight framework that parameterizes and learns residual connectivities from the data.
  arXiv  Detail & Related papers  (2026-02-09T18:54:18Z)
• Simple Models, Rich Representations: Visual Decoding from Primate Intracortical Neural Signals [0.0]
  We address the problem of decoding visual information from high-density intracortical recordings in primates.<n>We develop a modular generative decoding pipeline that combines low-resolution latent reconstruction with semantically conditioned diffusion.<n>This framework provides principles for brain-computer interfaces and semantic neural decoding.
  arXiv  Detail & Related papers  (2026-01-16T09:10:31Z)
• LANCET: Neural Intervention via Structural Entropy for Mitigating Faithfulness Hallucinations in LLMs [23.96862009891609]
  Large Language Models have revolutionized information processing, yet their reliability is severely compromised by faithfulness hallucinations.<n>We propose Lancet, a novel framework that achieves precise neural intervention by leveraging structural entropy and hallucination difference ratios.
  arXiv  Detail & Related papers  (2026-01-04T06:41:28Z)
• Deep Recurrence for Dynamical Segmentation Models [0.0]
  We propose a predictive coding inspired feedback mechanism that introduces a recurrent loop from output to input, allowing the model to refine its internal state over time.<n>We implement this mechanism within a standard U-Net architecture and introduce two biologically motivated operations, softmax projection and exponential decay, to ensure stability of the feedback loop.<n>We show that the feedback model significantly outperforms its feedforward counterpart in noisy conditions and generalizes more effectively with limited supervision.
  arXiv  Detail & Related papers  (2025-07-14T10:41:07Z)
• The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns [3.9848584845601014]
  We introduce the Cooperative Network Architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed "nets"<n>We demonstrate that net fragments can be learned without supervision and flexibly recombined to encode novel patterns, enabling figure completion and resilience to noise.
  arXiv  Detail & Related papers  (2024-07-08T06:22:10Z)
• Enhancing Dynamic CT Image Reconstruction with Neural Fields and Optical Flow [0.0]
  We show the benefits of introducing explicit motion regularizers for dynamic inverse problems based on partial differential equations.<n>We also compare neural fields against a grid-based solver and show that the former outperforms the latter in terms of PSNR.
  arXiv  Detail & Related papers  (2024-06-03T13:07:29Z)
• 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)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• A Generic Shared Attention Mechanism for Various Backbone Neural Networks [53.36677373145012]
  Self-attention modules (SAMs) produce strongly correlated attention maps across different layers. Dense-and-Implicit Attention (DIA) shares SAMs across layers and employs a long short-term memory module. Our simple yet effective DIA can consistently enhance various network backbones.
  arXiv  Detail & Related papers  (2022-10-27T13:24:08Z)
• Minimizing Control for Credit Assignment with Strong Feedback [65.59995261310529]
  Current methods for gradient-based credit assignment in deep neural networks need infinitesimally small feedback signals. We combine strong feedback influences on neural activity with gradient-based learning and show that this naturally leads to a novel view on neural network optimization. We show that the use of strong feedback in DFC allows learning forward and feedback connections simultaneously, using a learning rule fully local in space and time.
  arXiv  Detail & Related papers  (2022-04-14T22:06:21Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• On the role of feedback in visual processing: a predictive coding perspective [0.6193838300896449]
  We consider deep convolutional networks (CNNs) as models of feed-forward visual processing and implement Predictive Coding (PC) dynamics. We find that the network increasingly relies on top-down predictions as the noise level increases. In addition, the accuracy of the network implementing PC dynamics significantly increases over time-steps, compared to its equivalent forward network.
  arXiv  Detail & Related papers  (2021-06-08T10:07:23Z)
• Non-Singular Adversarial Robustness of Neural Networks [58.731070632586594]
  Adrial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. We formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
  arXiv  Detail & Related papers  (2021-02-23T20:59:30Z)
• Neural Networks with Recurrent Generative Feedback [61.90658210112138]
  We instantiate this design on convolutional neural networks (CNNs) In the experiments, CNN-F shows considerably improved adversarial robustness over conventional feedforward CNNs on standard benchmarks.
  arXiv  Detail & Related papers  (2020-07-17T19:32:48Z)

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.