MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI
- URL: http://arxiv.org/abs/2503.02351v1
- Date: Tue, 04 Mar 2025 07:20:42 GMT
- Title: MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI
- Authors: Guangyin Bao, Qi Zhang, Zixuan Gong, Zhuojia Wu, Duoqian Miao,
- Abstract summary: Concept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with particular concepts.<n> Conventional experiment-driven approaches hinge on manually constructed visual stimulus collections and corresponding brain activity recordings.<n>By synthesizing extensive brain activity recordings, we statistically localize various concept-selective regions.
- Score: 11.463093725231971
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Concept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with particular concepts. Precisely localizing these regions stands as a crucial long-term goal in neuroscience to grasp essential brain functions and mechanisms. Conventional experiment-driven approaches hinge on manually constructed visual stimulus collections and corresponding brain activity recordings, constraining the support and coverage of concept localization. Additionally, these stimuli often consist of concept objects in unnatural contexts and are potentially biased by subjective preferences, thus prompting concerns about the validity and generalizability of the identified regions. To address these limitations, we propose a data-driven exploration approach. By synthesizing extensive brain activity recordings, we statistically localize various concept-selective regions. Our proposed MindSimulator leverages advanced generative technologies to learn the probability distribution of brain activity conditioned on concept-oriented visual stimuli. This enables the creation of simulated brain recordings that reflect real neural response patterns. Using the synthetic recordings, we successfully localize several well-studied concept-selective regions and validate them against empirical findings, achieving promising prediction accuracy. The feasibility opens avenues for exploring novel concept-selective regions and provides prior hypotheses for future neuroscience research.
Related papers
- Artificial Kuramoto Oscillatory Neurons [65.16453738828672]
It has long been known in both neuroscience and AI that ''binding'' between neurons leads to a form of competitive learning.<n>We introduce Artificial rethinking together with arbitrary connectivity designs such as fully connected convolutional, or attentive mechanisms.<n>We show that this idea provides performance improvements across a wide spectrum of tasks such as unsupervised object discovery, adversarial robustness, uncertainty, and reasoning.
arXiv Detail & Related papers (2024-10-17T17:47:54Z) - MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
Brain decoding aims to reconstruct stimuli from acquired brain signals.
Currently, brain decoding is confined to a per-subject-per-model paradigm.
We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
arXiv Detail & Related papers (2024-04-11T15:46:42Z) - Brain-Inspired Machine Intelligence: A Survey of
Neurobiologically-Plausible Credit Assignment [65.268245109828]
We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology.
We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors.
The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
arXiv Detail & Related papers (2023-12-01T05:20:57Z) - A Goal-Driven Approach to Systems Neuroscience [2.6451153531057985]
Humans and animals exhibit a range of interesting behaviors in dynamic environments.
It is unclear how our brains actively reformat this dense sensory information to enable these behaviors.
We offer a new definition of interpretability that we show has promise in yielding unified structural and functional models of neural circuits.
arXiv Detail & Related papers (2023-11-05T16:37:53Z) - BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity [6.285481522918523]
We introduce a data-driven method that generates natural language descriptions for images predicted to maximally activate individual voxels of interest.
We validate our method through fine-grained voxel-level captioning across higher-order visual regions.
To demonstrate how our method enables scientific discovery, we perform exploratory investigations on the distribution of "person" representations in the brain.
arXiv Detail & Related papers (2023-10-06T17:59:53Z) - Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey) [9.14580723964253]
Can artificial intelligence unlock the secrets of the human brain?
Is it possible to enhance AI by tapping into the power of brain recordings?
Our survey focuses on human brain recording studies and cutting-edge cognitive neuroscience datasets.
arXiv Detail & Related papers (2023-07-17T06:54:36Z) - Identifying Shared Decodable Concepts in the Human Brain Using
Image-Language Foundation Models [2.213723689024101]
We introduce a method that takes advantage of high-quality pretrained multimodal representations to explore fine-grained semantic networks in the human brain.
To identify such brain regions, we developed a data-driven approach to uncover visual concepts that are decodable from a massive functional magnetic resonance imaging (fMRI) dataset.
arXiv Detail & Related papers (2023-06-06T03:29:47Z) - 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) - Constraints on the design of neuromorphic circuits set by the properties
of neural population codes [61.15277741147157]
In the brain, information is encoded, transmitted and used to inform behaviour.
Neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain.
arXiv Detail & Related papers (2022-12-08T15:16:04Z) - Towards a Neural Model for Serial Order in Frontal Cortex: a Brain
Theory from Memory Development to Higher-Level Cognition [53.816853325427424]
We propose that the immature prefrontal cortex (PFC) use its primary functionality of detecting hierarchical patterns in temporal signals.
Our hypothesis is that the PFC detects the hierarchical structure in temporal sequences in the form of ordinal patterns and use them to index information hierarchically in different parts of the brain.
By doing so, it gives the tools to the language-ready brain for manipulating abstract knowledge and planning temporally ordered information.
arXiv Detail & Related papers (2020-05-22T14:29:51Z) - Neuronal Sequence Models for Bayesian Online Inference [0.0]
Sequential neuronal activity underlies a wide range of processes in the brain.
Neuroscientific evidence for neuronal sequences has been reported in domains as diverse as perception, motor control, speech, spatial navigation and memory.
We review key findings about neuronal sequences and relate these to the concept of online inference on sequences as a model of sensory-motor processing and recognition.
arXiv Detail & Related papers (2020-04-02T10:52:54Z)
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.