Simple Models, Rich Representations: Visual Decoding from Primate Intracortical Neural Signals

• URL: http://arxiv.org/abs/2601.11108v1
• Date: Fri, 16 Jan 2026 09:10:31 GMT
• Title: Simple Models, Rich Representations: Visual Decoding from Primate Intracortical Neural Signals
• Authors: Matteo Ciferri, Matteo Ferrante, Nicola Toschi,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding how neural activity gives rise to perception is a central challenge in neuroscience. We address the problem of decoding visual information from high-density intracortical recordings in primates, using the THINGS Ventral Stream Spiking Dataset. We systematically evaluate the effects of model architecture, training objectives, and data scaling on decoding performance. Results show that decoding accuracy is mainly driven by modeling temporal dynamics in neural signals, rather than architectural complexity. A simple model combining temporal attention with a shallow MLP achieves up to 70% top-1 image retrieval accuracy, outperforming linear baselines as well as recurrent and convolutional approaches. Scaling analyses reveal predictable diminishing returns with increasing input dimensionality and dataset size. Building on these findings, we design a modular generative decoding pipeline that combines low-resolution latent reconstruction with semantically conditioned diffusion, generating plausible images from 200 ms of brain activity. This framework provides principles for brain-computer interfaces and semantic neural decoding.

Related papers

• BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression [26.114257185901838]
  We propose BrainRVQ, a general-purpose EEG foundation model pre-trained on a large-scale corpus of clinical EEG data.<n>BrainRVQ features a Dual-Domain Residual Vector Quantization (DD-RVQ) tokenizer that disentangles temporal waveforms and spectral patterns into hierarchical discrete codes.
  arXiv  Detail & Related papers  (2026-02-18T23:30:36Z)
• Deep Models, Shallow Alignment: Uncovering the Granularity Mismatch in Neural Decoding [8.822848795081693]
  We propose a novel contrastive learning strategy that aligns neural signals with intermediate representations of visual encoders rather than their final outputs.<n>Our approach effectively unlocks the scaling law in neural visual decoding, enabling decoding performance to scale predictably with the capacity of pre-trained vision backbones.
  arXiv  Detail & Related papers  (2026-01-29T16:30:32Z)
• Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction [65.67001243986981]
  We propose MindHier, a coarse-to-fine fMRI-to-image reconstruction framework built on scale-wise autoregressive modeling.<n>MindHier achieves superior semantic fidelity, 4.67x faster inference, and more deterministic results than the diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-10-25T15:40:07Z)
• Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding [3.416130444086009]
  We propose STDA-SwiFT, a transformer-based model that learns transferable representations from large-scale fMRI datasets.<n>We show that our model substantially improves downstream decoding performance of task-evoked activity.<n>Our work showcases transfer learning as a viable approach to overcome challenges in decoding brain activity from fMRI data.
  arXiv  Detail & Related papers  (2025-07-30T04:36:58Z)
• TokenUnify: Scaling Up Autoregressive Pretraining for Neuron Segmentation [65.65530016765615]
  We propose a hierarchical predictive coding framework that captures multi-scale dependencies through three complementary learning objectives.<n> TokenUnify integrates random token prediction, next-token prediction, and next-all token prediction to create a comprehensive representational space.<n>We also introduce a large-scale EM dataset with 1.2 billion annotated voxels, offering ideal long-sequence visual data with spatial continuity.
  arXiv  Detail & Related papers  (2024-05-27T05:45:51Z)
• See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI [32.40827290083577]
  Deciphering visual content from functional Magnetic Resonance Imaging (fMRI) helps illuminate the human vision system. Previous approaches primarily employ subject-specific models, sensitive to training sample size. We propose shallow subject-specific adapters to map cross-subject fMRI data into unified representations. During training, we leverage both visual and textual supervision for multi-modal brain decoding.
  arXiv  Detail & Related papers  (2024-03-11T01:18:49Z)
• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• Deep Learning for real-time neural decoding of grasp [0.0]
  We present a Deep Learning-based approach to the decoding of neural signals for grasp type classification. The main goal of the presented approach is to improve over state-of-the-art decoding accuracy without relying on any prior neuroscience knowledge.
  arXiv  Detail & Related papers  (2023-11-02T08:26:29Z)
• 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)
• Joint fMRI Decoding and Encoding with Latent Embedding Alignment [77.66508125297754]
  We introduce a unified framework that addresses both fMRI decoding and encoding. Our model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework.
  arXiv  Detail & Related papers  (2023-03-26T14:14:58Z)
• Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images [79.07468367923619]
  We develop convolutional neural generative coding (Conv-NGC) We implement a flexible neurobiologically-motivated algorithm that progressively refines latent state maps. We study the effectiveness of our brain-inspired neural system on the tasks of reconstruction and image denoising.
  arXiv  Detail & Related papers  (2022-11-22T06:42:41Z)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• 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)

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.