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Found 20 papers, 6 papers with code
Active Inference and Intentional Behaviour
First, we simulate the aforementioned in vitro experiments, in which neuronal cultures spontaneously learn to play Pong, by implementing nested, free energy minimising processes.
Supervised structure learning
This paper concerns structure learning or discovery of discrete generative models.
Designing Ecosystems of Intelligence from First Principles
In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world -- also known as self-evidencing.
Capsule Networks as Generative Models
Capsule networks are a neural network architecture specialized for visual scene recognition.
Hybrid Predictive Coding: Inferring, Fast and Slow
This is at odds with evidence that several aspects of visual perception - including complex forms of object recognition - arise from an initial "feedforward sweep" that occurs on fast timescales which preclude substantial recurrent activity.
A continuity of Markov blanket interpretations under the Free Energy Principle
Bruineberg and colleagues helpfully distinguish between instrumental and ontological interpretations of Markov blankets, exposing the dangers of using the former to make claims about the latter.
pymdp: A Python library for active inference in discrete state spaces
Active inference is an account of cognition and behavior in complex systems which brings together action, perception, and learning under the theoretical mantle of Bayesian inference.
Active Inference in Robotics and Artificial Agents: Survey and Challenges
Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning.
How particular is the physics of the free energy principle?
We discover that two requirements of the FEP -- the Markov blanket condition (i. e. a statistical boundary precluding direct coupling between internal and external states) and stringent restrictions on its solenoidal flows (i. e. tendencies driving a system out of equilibrium) -- are only valid for a very narrow space of parameters.
Neural Kalman Filtering
The Kalman filter is a fundamental filtering algorithm that fuses noisy sensory data, a previous state estimate, and a dynamics model to produce a principled estimate of the current state.
Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm
The recently proposed Activation Relaxation (AR) algorithm provides a simple and robust approach for approximating the backpropagation of error algorithm using only local learning rules.
Relaxing the Constraints on Predictive Coding Models
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 minimization of prediction errors.
Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
The backpropagation of error algorithm (backprop) has been instrumental in the recent success of deep learning.
Control as Hybrid Inference
The field of reinforcement learning can be split into model-based and model-free methods.
On the Relationship Between Active Inference and Control as Inference
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.
Reinforcement Learning as Iterative and Amortised Inference
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 online or offline.
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs
Recently, it has been shown that backprop in multilayer-perceptrons (MLPs) can be approximated using predictive coding, a biologically-plausible process theory of cortical computation which relies only on local and Hebbian updates.
Whence the Expected Free Energy?
The Expected Free Energy (EFE) is a central quantity in the theory of active inference.
Reinforcement Learning through Active Inference
The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of cumulative rewards.
Scaling active inference
In reinforcement learning (RL), agents often operate in partially observed and uncertain environments.
