58 citations · 109 across the 8 of their papers we have counts for
8 papers · 1 filter
Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm
Beren Millidge, Alexander Tschantz, Anil Seth +1
The recently proposed Activation Relaxation (AR) algorithm provides a simple and robust approach for approximating the backpropagation of error algorithm using only local learning…
Relaxing the Constraints on Predictive Coding Models
Beren Millidge, Alexander Tschantz, Anil Seth +1
Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs, which underlies both perception and learning, is the…
Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
Beren Millidge, Alexander Tschantz, Anil K Seth +1
The backpropagation of error algorithm (backprop) has been instrumental in the recent success of deep learning. However, a key question remains as to whether backprop can be formul…
Control as Hybrid Inference
Alexander Tschantz, Beren Millidge, Anil K. Seth +1
The field of reinforcement learning can be split into model-based and model-free methods. Here, we unify these approaches by casting model-free policy optimisation as amortised var…
Reinforcement Learning as Iterative and Amortised Inference
Beren Millidge, Alexander Tschantz, Anil K Seth +1
There are several ways to categorise reinforcement learning (RL) algorithms, such as either model-based or model-free, policy-based or planning-based, on-policy or off-policy, and…
On the Relationship Between Active Inference and Control as Inference
Beren Millidge, Alexander Tschantz, Anil K Seth +1
Active Inference (AIF) is an emerging framework in the brain sciences which suggests that biological agents act to minimise a variational bound on model evidence. Control-as-Infere…