NeurIPS 2024PastNeuroscience
The First Workshop on NeuroAI @ NeurIPS2024
NeuroAI @ NeurIPS 2024
- Submission deadline
- Sep 10, 2024, 13:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (43)
Fetched from OpenReview (v2) on 2026-06-10.
A call for intrinsic learning
A Hopfield network model of neuromodulatory arousal state
A Walsh Hadamard Derived Linear Vector Symbolic Architecture
Asynchronous Hebbian/anti-Hebbian networks
Beyond Directed Acyclic Computation Graph with Cyclic Neural Network
Brain in the Dark: Design Principles for Neuromimetic Inference under the Free Energy Principle
Common visual learning constraints in transformers and newborn brains: Evidence from line drawings
Decoupling the Contributions of Spatio-Temporal Coding: From ANNs to SNNs
Doing More with Less: Computational Role of Information Structure in Neural Networks based on Entropy Maximization
Dyadic Learning in Recurrent and Feedforward Models
Dynamics Based Neural Encoding with Inter-Intra Region Connectivity
Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building
Hierarchical Control of Reaching Movements Via Compositional Gain Modulation
Homeostasis-aware Direct Spike Encoding for Deep Spiking Neural Networks
How do Active Dendrite Networks Mitigate Catastrophic Forgetting?
Improving out-of-distribution generalization by mimicking the human visual diet.
Invariant Spatiotemporal Representation Learning for Cross-patient Seizure Classification
Learning Bayes-Optimal Representation in Partially Observable Environments via Meta-Reinforcement Learning with Predictive Coding
Liquid Resistance Liquid Capacitance Networks
Multiple temporal credit assignment rules achieve comparable neural data similarity
Natural Language-guided Neural Encoding Benchmark for Vision
Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift
NetFormer: An interpretable model for recovering identity and structure in neural population dynamics
Neural Embedding Ranks: Aligning 3D latent dynamics with movement for long-term decoding
Neuron-Astrocyte Associative Memory
Not so griddy: Internal representations of RNNs path integrating more than one agent
Parallel Decision-Making yields Disentangled World Models: Impact and Implications
Partial observation can induce mechanistic mismatches in data-constrained RNNs
Path Divergence Objective: Boundedly-Rational Decision Making in Partially Observable Environments
Population Transformer: Learning Population-level Representations of Intracranial Activity
Predictive Coding Graphs are a Superset of Feedforward Neural Networks
Predictive Learning Induces Probabilistic Cognitive Maps
Proliferation of cosine-tuning in both artificial spiking and cortical neural networks during learning
Prospective Learning: Learning for a Dynamic Future
RNN Replay: Leakage and Underdamped Dynamics
SynapsNet: Enhancing Neuronal Population Dynamics Modeling via Learning Functional Connectivity
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning
The Role of Cortical Varibility in Supporting Few-shot Generalization: Theory and Empirical Evidence
Towards zero-shot adaptation of predictive models of neurons encoding posterior probability
Uncovering Neural Encoding Variability with Infinite Gaussian Process Factor Analysis
Value of Information and Reward Specification in Active Inference and POMDPs
What should a neuron aim for? Designing local objective functions based on information theory
Why learn if you can infer? Robot arm control with Hierarchical Active Inference