Shifting Attention to You: Personalized Brain-Inspired AI Models

• URL: http://arxiv.org/abs/2502.04658v1
• Date: Fri, 07 Feb 2025 04:55:31 GMT
• Title: Shifting Attention to You: Personalized Brain-Inspired AI Models
• Authors: Stephen Chong Zhao, Yang Hu, Jason Lee, Andrew Bender, Trisha Mazumdar, Mark Wallace, David A. Tovar,
• Abstract summary: We introduce personalized brain-inspired modeling that integrates human behavioral embeddings and neural data to align with cognitive processes.<n>Fine-tuning on behavioral data enhances its ability to predict human similarity judgments while indirectly aligning it with dynamic representations captured via MEG.<n>We trained individualized models on participant-specific neural data, effectively capturing individualized neural dynamics.
• Score: 3.0128071072792366
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The integration of human and artificial intelligence represents a scientific opportunity to advance our understanding of information processing, as each system offers unique computational insights that can enhance and inform the other. The synthesis of human cognitive principles with artificial intelligence has the potential to produce more interpretable and functionally aligned computational models, while simultaneously providing a formal framework for investigating the neural mechanisms underlying perception, learning, and decision-making through systematic model comparisons and representational analyses. In this study, we introduce personalized brain-inspired modeling that integrates human behavioral embeddings and neural data to align with cognitive processes. We took a stepwise approach, fine-tuning the Contrastive Language-Image Pre-training (CLIP) model with large-scale behavioral decisions, group-level neural data, and finally, participant-level neural data within a broader framework that we have named CLIP-Human-Based Analysis (CLIP-HBA). We found that fine-tuning on behavioral data enhances its ability to predict human similarity judgments while indirectly aligning it with dynamic representations captured via MEG. To further gain mechanistic insights into the temporal evolution of cognitive processes, we introduced a model specifically fine-tuned on millisecond-level MEG neural dynamics (CLIP-HBA-MEG). This model resulted in enhanced temporal alignment with human neural processing while still showing improvement on behavioral alignment. Finally, we trained individualized models on participant-specific neural data, effectively capturing individualized neural dynamics and highlighting the potential for personalized AI systems. These personalized systems have far-reaching implications for the fields of medicine, cognitive research, human-computer interfaces, and AI development.

Related papers

• RTify: Aligning Deep Neural Networks with Human Behavioral Decisions [10.510746720313303]
  Current neural network models of primate vision focus on replicating overall levels of behavioral accuracy. We introduce a novel computational framework to model the dynamics of human behavioral choices by learning to align the temporal dynamics of a recurrent neural network to human reaction times (RTs) We show that the approximation can be used to optimize an "ideal-observer" RNN model to achieve an optimal tradeoff between speed and accuracy without human data.
  arXiv  Detail & Related papers  (2024-11-06T03:04:05Z)
• Neural Dynamics Model of Visual Decision-Making: Learning from Human Experts [28.340344705437758]
  We implement a comprehensive visual decision-making model that spans from visual input to behavioral output. Our model aligns closely with human behavior and reflects neural activities in primates. A neuroimaging-informed fine-tuning approach was introduced and applied to the model, leading to performance improvements.
  arXiv  Detail & Related papers  (2024-09-04T02:38:52Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data [3.46029409929709]
  State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis. Inspired by the success of large pretrained models in vision and language domains, we reframe the analysis of large-scale, cellular-resolution neuronal spiking data into an autoregressive generation problem. We first trained Neuroformer on simulated datasets, and found that it both accurately predicted intrinsically simulated neuronal circuit activity, and also inferred the underlying neural circuit connectivity, including direction.
  arXiv  Detail & Related papers  (2023-10-31T20:17:32Z)
• Evaluating alignment between humans and neural network representations in image-based learning tasks [5.657101730705275]
  We tested how well the representations of $86$ pretrained neural network models mapped to human learning trajectories. We found that while training dataset size was a core determinant of alignment with human choices, contrastive training with multi-modal data (text and imagery) was a common feature of currently publicly available models that predicted human generalisation. In conclusion, pretrained neural networks can serve to extract representations for cognitive models, as they appear to capture some fundamental aspects of cognition that are transferable across tasks.
  arXiv  Detail & Related papers  (2023-06-15T08:18:29Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Overcoming the Domain Gap in Neural Action Representations [60.47807856873544]
  3D pose data can now be reliably extracted from multi-view video sequences without manual intervention. We propose to use it to guide the encoding of neural action representations together with a set of neural and behavioral augmentations. To reduce the domain gap, during training, we swap neural and behavioral data across animals that seem to be performing similar actions.
  arXiv  Detail & Related papers  (2021-12-02T12:45:46Z)
• Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations [60.47807856873544]
  A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies. This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
  arXiv  Detail & Related papers  (2021-11-29T15:27:51Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)
• A brain basis of dynamical intelligence for AI and computational neuroscience [0.0]
  More brain-like capacities may demand new theories, models, and methods for designing artificial learning systems. This article was inspired by our symposium on dynamical neuroscience and machine learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.
  arXiv  Detail & Related papers  (2021-05-15T19:49:32Z)
• Deep Interpretable Models of Theory of Mind For Human-Agent Teaming [0.7734726150561086]
  We develop an interpretable modular neural framework for modeling the intentions of other observed entities. We demonstrate the efficacy of our approach with experiments on data from human participants on a search and rescue task in Minecraft.
  arXiv  Detail & Related papers  (2021-04-07T06:18:58Z)
• Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition [54.749127627191655]
  The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. One solution is to make robots learn from their first-hand sensory data with self-supervision.
  arXiv  Detail & Related papers  (2021-03-16T13:50:24Z)
• The Neural Coding Framework for Learning Generative Models [91.0357317238509]
  We propose a novel neural generative model inspired by the theory of predictive processing in the brain. In a similar way, artificial neurons in our generative model predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality.
  arXiv  Detail & Related papers  (2020-12-07T01:20:38Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits [91.3755431537592]
  Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neuro-robotics.
  arXiv  Detail & Related papers  (2020-03-23T14:55:00Z)

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.