artificial neural networks
Artificial neural networks are computational models inspired by the biological neural networks of the human brain. They consist of interconnected nodes (neurons) and are capable of learning from data through processes like backpropagation, making them foundational for many AI applications.
- Anatomically inspired digital twins capture hierarchical object representations in visual cortex
- Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected
- Decomposing stimulus-specific sensory neural information via diffusion models
- Dimensionality Mismatch Between Brains and Artificial Neural Networks
- Disentangling Superpositions: Interpretable Brain Encoding Model with Sparse Concept Atoms
- Jacobian-Based Interpretation of Nonlinear Neural Encoding Model
- Localist Topographic Expert Routing: A Barrel Cortex-Inspired Modular Network for Sensorimotor Processing
- Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex
- Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics
- Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control
- S$^2$M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection
- SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
- Scaling and context steer LLMs along the same computational path as the human brain
- Self-Assembling Graph Perceptrons
- Spike-RetinexFormer: Rethinking Low-light Image Enhancement with Spiking Neural Networks
- Stable Port-Hamiltonian Neural Networks