C. Buckley

发表

Understanding the origin of information-seeking exploration in probabilistic objectives for control pdf

Beren Millidge, Alexander Tschantz, Anil Seth, 2021, ArXiv.

Active Inference in Robotics and Artificial Agents: Survey and Challenges pdf

Jun Tani, Martijn Wisse, Pablo Lanillos, 2021, ArXiv.

Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions

Simon T. Powers, Adam P. Davies, Chris R. Cox, 2015, Evolutionary Biology.

Active Inference: First International Workshop, IWAI 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14, 2020, Proceedings

Raquel Oliveira Prates, C. Buckley, Pablo Lanillos, 2020, IWAI.

Scaling Active Inference

Alexander Tschantz, Christopher L. Buckley, Manuel Baltieri, 2019, 2020 International Joint Conference on Neural Networks (IJCNN).

Neural complexity: a graph theoretic interpretation.

C. Buckley, L. Barnett, S. Bullock, 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

Multi-Neuronal Refractory Period Adapts Centrally Generated Behaviour to Reward

Peter A. Passaro, M. O'Shea, A. Seth, 2012, PloS one.

A systemic analysis of the ideas immanent in neuromodulation

C. Buckley, 2008 .

An active inference implementation of phototaxis

C. Buckley, Manuel Baltieri, 2017, European Conference on Artificial Life.

Optimisation in ‘Self-modelling’ Complex Adaptive Systems

R. Watson, C. Buckley, Rob Mills, 2011 .

Lattice Boltzmann BGK simulation of nonlinear sound waves: the development of a shock front

C. Buckley, J. Gilbert, J. Buick, 2000 .

Brain State Control by Closed-Loop Environmental Feedback

T. Toyoizumi, C. Buckley, Satohiro Tajima, 2016, 1602.08881.

The role of sensorimotor feedback in a brain state transition from passive to active processing

T. Toyoizumi, C. Buckley, 2014 .

Sensitivityandstability:Asignalpropagationsweetspotinasheetof recurrentcentrecrossingneurons

C. Buckley, S. Bullock, 2007 .

Predictive Coding: a Theoretical and Experimental Review

Beren Millidge, A. Seth, C. Buckley, 2021, ArXiv.

How particular is the physics of the free energy principle?

Beren Millidge, Alexander Tschantz, C. Buckley, 2021, Physics of life reviews.

Competition-Based Model of Pheromone Component Ratio Detection in the Moth

Dominique Martinez, Thomas Nowotny, Jean-Pierre Rospars, 2011, PloS one.

Transient dynamics between displaced fixed points: an alternate nonlinear dynamical framework for olfaction

Thomas Nowotny, Christopher L Buckley, C. Buckley, 2011, Brain Research.

Successor Representation Active Inference

Beren Millidge, C. Buckley, 2022, ArXiv.

Spin glass systems as collective active inference

C. Buckley, Brennan Klein, Miguel Aguilera, 2022, IWAI.

Preventing Deterioration of Classification Accuracy in Predictive Coding Networks

Beren Millidge, C. Buckley, Paul Kinghorn, 2022, ArXiv.

Capsule Networks as Generative Models

Beren Millidge, Alexander Tschantz, C. Buckley, 2022, 2209.02567.

(2009). Embracing the tyranny of distance: Space as an enabling constraint. Technoetic Arts

C. Buckley, S. Bullock, 2022 .

Curious Inferences: Reply to Sun and Firestone on the Dark Room Problem

Beren Millidge, Alexander Tschantz, A. Seth, 2020, Trends in Cognitive Sciences.

Capsule Networks as Generative Models

Beren Millidge, Alexander Tschantz, C. Buckley, 2022, IWAI.

RL with KL penalties is better viewed as Bayesian inference

C. Buckley, Ethan Perez, Tomasz Korbak, 2022, Conference on Empirical Methods in Natural Language Processing.

Preventing Deterioration of Classification Accuracy in Predictive Coding Networks

Beren Millidge, C. Buckley, Paul Kinghorn, 2022, IWAI.

Whence the Expected Free Energy?

Beren Millidge, Alexander Tschantz, Christopher L Buckley, 2020, Neural Computation.

Designing ecosystems of intelligence from first principles

Karl J. Friston, Toby St Clere Smithe, Magnus T. Koudahl, 2022, Collective Intelligence.

Global Adaptation in Networks of Selfish Components: Emergent Associative Memory at the System Scale

Richard A. Watson, Rob Mills, Christopher L. Buckley, 2011, Artificial Life.

Hybrid predictive coding: Inferring, fast and slow

Beren Millidge, Alexander Tschantz, A. Seth, 2022, PLoS Comput. Biol..

Habitual and Reflective Control in Hierarchical Predictive Coding pdf

Beren Millidge, Christopher L. Buckley, Paul F. Kinghorn, 2021, PKDD/ECML Workshops.

A Mathematical Walkthrough and Discussion of the Free Energy Principle pdf

Beren Millidge, Christopher L Buckley, Anil Seth, 2021, ArXiv.

Predictive Coding: a Theoretical and Experimental Review pdf

Beren Millidge, Christopher L Buckley, Anil Seth, 2021, ArXiv.

Whence the Expected Free Energy?

Beren Millidge, Alexander Tschantz, C. Buckley, 2020, Neural Computation.

Relaxing the Constraints on Predictive Coding Models pdf

Beren Millidge, Alexander Tschantz, Christopher L Buckley, 2020, ArXiv.

On the Relationship Between Active Inference and Control as Inference pdf

Beren Millidge, Alexander Tschantz, Christopher L Buckley, 2020, IWAI.

The dark room problem in predictive processing and active inference, a legacy of cognitivism?

Christopher L. Buckley, Manuel Baltieri, C. Buckley, 2019, Artificial Life Conference Proceedings.

PID Control as a Process of Active Inference with Linear Generative Models †

Manuel Baltieri, Christopher Buckley, C. Buckley, 2019, Entropy.

A probabilistic interpretation of PID controllers using active inference

Christopher L. Buckley, Manuel Baltieri, C. Buckley, 2018, bioRxiv.

An active inference implementation of phototaxis

Christopher L. Buckley, Manuel Baltieri, C. Buckley, 2017, ECAL.

The free energy principle for action and perception: A mathematical review

Simon McGregor, Anil K. Seth, Christopher L. Buckley, 2017, 1705.09156.

A Minimal Active Inference Agent pdf

Simon McGregor, Christopher L. Buckley, Manuel Baltieri, 2015, ArXiv.

Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality

R. Watson, C. Buckley, M. Levin, 2022, Frontiers in Ecology and Evolution.

Reinforcement Learning through Active Inference pdf

Anil K. Seth, Beren Millidge, Alexander Tschantz, 2020, ArXiv.

Learning action-oriented models through active inference

Anil K. Seth, Alexander Tschantz, Christopher L. Buckley, 2019, bioRxiv.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

Silvestro Micera, Johannes Schemmel, Szabolcs Káli, 2017, BMC Neuroscience.

Spatially embedded dynamics and complexity

Seth Bullock, Lionel Barnett, Christopher L. Buckley, 2010, Complex..

Embracing the tyranny of distance: space as an enabling constraint

Seth Bullock, Christopher L. Buckley, C. Buckley, 2009 .

Simulating homeostatic, allostatic and goal-directed forms of interoceptive control using active inference

G. Pezzulo, D. Maisto, Alexander Tschantz, 2021, Biological Psychology.

Neural complexity and structural connectivity.

L Barnett, C L Buckley, S Bullock, 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

Reinforcement Learning as Iterative and Amortised Inference pdf

Beren Millidge, Alexander Tschantz, Christopher L Buckley, 2020, ArXiv.

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs pdf

Beren Millidge, Alexander Tschantz, Christopher L. Buckley, 2020, Neural Computation.

From the free energy principle to a confederation of Bayesian mechanics: Reply to comments on "How particular is the physics of the free energy principle?"

Beren Millidge, Alexander Tschantz, C. Buckley, 2023, Physics of life reviews.

Predictions in the eye of the beholder: an active inference account of Watt governors

Christopher L. Buckley, Manuel Baltieri, Jelle Bruineberg, 2020, ALIFE.

Control as Hybrid Inference pdf

Anil K. Seth, Beren Millidge, Alexander Tschantz, 2020, ArXiv.

Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain pdf

Beren Millidge, Alexander Tschantz, Christopher L Buckley, 2020, ArXiv.

Adaptation without Natural Selection

Jason Noble, Richard A. Watson, Seth Bullock, 2010, ALIFE.

Visual associative learning in wood ants

A. Sofia D. Fernandes, C. L. Buckley, J. E. Niven, 2018, Journal of Experimental Biology.

Visual Classical Conditioning in Wood Ants

A Sofia D Fernandes, C L Buckley, J E Niven, 2018, Journal of visualized experiments : JoVE.

Associative Memory in Gene Regulation Networks

Richard A. Watson, Rob Mills, Christopher L. Buckley, 2010, ALIFE.

Timescale and Stability in Adaptive Behaviour

Seth Bullock, Christopher L. Buckley, Netta Cohen, 2005, ECAL.

The modularity of action and perception revisited using control theory and active inference

Christopher L. Buckley, Manuel Baltieri, C. Buckley, 2018, ALIFE.

Spatial, temporal, and modulatory factors affecting GasNet evolvability in a visually guided robotics task

Andrew Philippides, Phil Husbands, Ezequiel A. Di Paolo, 2010, Complex..

Attention: Marginal Probability is All You Need?

C. Buckley, Ryan Singh, 2023, ArXiv.

On Kalman-Bucy filters, linear quadratic control and active inference

Christopher L. Buckley, Manuel Baltieri, C. Buckley, 2020, 2005.06269.

Understanding Predictive Coding as an Adaptive Trust-Region Method

C. Buckley, Francesco Innocenti, Ryan Singh, 2023, ArXiv.

Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm pdf

Beren Millidge, Alexander Tschantz, Christopher L Buckley, 2020, ArXiv.

Sensitivity and stability: A signal propagation sweet spot in a sheet of recurrent centre crossing neurons

Seth Bullock, Christopher L. Buckley, C. Buckley, 2008, Biosyst..

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback

Taro Toyoizumi, Christopher L. Buckley, T. Toyoizumi, 2018, PLoS Comput. Biol..

μSPIM Toolset: A software platform for selective plane illumination microscopy

Christopher L. Buckley, Daniel Saska, Paul Pichler, 2020, Journal of Neuroscience Methods.

μSPIM: A Software Platform for Selective Plane Illumination Microscopy

Christopher L. Buckley, Daniel Saska, Paul Pichler, 2020, bioRxiv.

Neural Kalman Filtering pdf

Beren Millidge, Alexander Tschantz, Anil Seth, 2021, ArXiv.

Generative models as parsimonious descriptions of sensorimotor loops

Christopher L. Buckley, Manuel Baltieri, C. Buckley, 2019, Behavioral and Brain Sciences.

Recurrent, nonequilibrium systems and the Markov blanket assumption

C. Buckley, Miguel Aguilera, 2022, Behavioral and Brain Sciences.

Knitting a Markov Blanket is Hard When You are Out-of-Equilibrium: Two Examples in Canonical Nonequilibrium Models

C. Buckley, Miguel Aguilera, Conor Heins, 2022, IWAI.

A behavioral and modeling study of control algorithms underlying the translational optomotor response in larval zebrafish with implications for neural circuit function

L. Lagnado, C. Buckley, P. Pichler, 2023, PLoS Comput. Biol..

Control algorithms underlying the translational optomotor response

L. Lagnado, C. Buckley, P. Pichler, 2022, bioRxiv.

Transformations in the scale of behavior and the global optimization of constraints in adaptive networks

Richard A. Watson, Rob Mills, Christopher L. Buckley, 2011, Adapt. Behav..

Brain-Inspired Computational Intelligence via Predictive Coding

Rajesh P. N. Rao, Thomas Lukasiewicz, C. Buckley, 2023, ArXiv.

“If You Can't Be With the One You Love, Love the One You're With”: How Individual Habituation of Agent Interactions Improves Global Utility

Jason Noble, Richard A. Watson, Rob Mills, 2011, Artificial Life.

Optimization in "self-modeling" complex adaptive systems

Richard A. Watson, Rob Mills, Christopher L. Buckley, 2011, Complex..

The Effect of Hebbian Learning on Optimisation in Hopfield Networks

Richard A. Watson, Rob Mills, Christopher L. Buckley, 2009 .

Emergent associative memory as a local organising principle for global adaptation in adaptive networks

Richard A. Watson, Rob Mills, Christopher L. Buckley, 2011 .

Multiscale model of an inhibitory network shows optimal properties near bifurcation.

Thomas Nowotny, Christopher L Buckley, C. Buckley, 2011, Physical review letters.

Monostable Controllers for Adaptive Behaviour

Seth Bullock, Ezequiel A. Di Paolo, Christopher L. Buckley, 2008, SAB.

Nonmodular Architectures of Cognitive Systems based on Active Inference

Christopher L. Buckley, Manuel Baltieri, C. Buckley, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

Active Inference: Computational Models of Motor Control without Efference Copy

Christopher L. Buckley, Manuel Baltieri, C. Buckley, 2019, 2019 Conference on Cognitive Computational Neuroscience.

The role of environmental feedback in a brain state switch from passive to active sensing

C. Buckley, Taro Toyoizumi, 2013, BMC Neuroscience.

An active inference implementation of phototaxis

C. Buckley, Manuel Baltieri, 2017, ECAL.

Spatial Embedding and Complexity: The Small-World Is Not Enough

Seth Bullock, Christopher L. Buckley, C. Buckley, 2007, ECAL.

Toward a dynamical systems analysis of neuromodulation

Seth Bullock, Christopher L. Buckley, Netta Cohen, 2004 .

Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain

Beren Millidge, Alexander Tschantz, A. Seth, 2020, ArXiv.

Relaxing the Constraints on Predictive Coding Models

Beren Millidge, Alexander Tschantz, A. Seth, 2020, ArXiv.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

Vito Paolo Pastore, Alfred B. Yu, Kelvin S. Oie, 2017, BMC Neuroscience.

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs

Beren Millidge, Alexander Tschantz, C. Buckley, 2020, Neural Computation.

Predictive Coding: a Theoretical and Experimental Review

Beren Millidge, A. Seth, C. Buckley, 2021, arXiv.org.

Reinforcement Learning as Iterative and Amortised Inference

Beren Millidge, Alexander Tschantz, A. Seth, 2020, ArXiv.