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Found 8 papers, 2 papers with code
Brain-Like Processing Pathways Form in Models With Heterogeneous Experts
The brain is made up of a vast set of heterogeneous regions that dynamically organize into pathways as a function of task demands.
Dynamical similarity analysis can identify compositional dynamics developing in RNNs
Here we propose that the phenomena of compositional learning in recurrent neural networks (RNNs) allows us to build a test case for dynamical representation alignment metrics.
Accelerated AI Inference via Dynamic Execution Methods
In this paper, we focus on Dynamic Execution techniques that optimize the computation flow based on input.
Spatial embedding promotes a specific form of modularity with low entropy and heterogeneous spectral dynamics
Understanding how biological constraints shape neural computation is a central goal of computational neuroscience.
Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience
As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging.
Getting aligned on representational alignment
These questions pertaining to the study of representational alignment are at the heart of some of the most promising research areas in contemporary cognitive science, neuroscience, and machine learning.
Brain-inspired learning in artificial neural networks: a review
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics.
Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecture
There is a concerted effort to build domain-general artificial intelligence in the form of universal neural network models with sufficient computational flexibility to solve a wide variety of cognitive tasks but without requiring fine-tuning on individual problem spaces and domains.
