Search Results for author:
Found 14 papers, 2 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.
Bayesian sparsification for deep neural networks with Bayesian model reduction
Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and competitive in terms of performance across various deep learning applications.
Life-inspired Interoceptive Artificial Intelligence for Autonomous and Adaptive Agents
Building autonomous --- i. e., choosing goals based on one's needs -- and adaptive -- i. e., surviving in ever-changing environments -- agents has been a holy grail of artificial intelligence (AI).
Linking fast and slow: the case for generative models
A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions.
Modelling cortical network dynamics
However, in contrast to previous work, we derive coupled equations between phase and amplitude dynamics.
Predictive coding and stochastic resonance as fundamental principles of auditory perception
How is information processed in the brain during perception?
Frontal effective connectivity increases with task demands and time on task: a Dynamic Causal Model of electrocorticogram in macaque monkeys
We apply Dynamic Causal Models to electrocorticogram recordings from two macaque monkeys performing a problem-solving task that engages working memory, and induces time-on-task effects.
Trust as Extended Control: Active Inference and User Feedback During Human-Robot Collaboration
This model is based on the cognitive neuroscience of active inference and suggests that, in the context of HRC, trust can be cast in terms of virtual control over an artificial agent.
Dynamic causal modelling of COVID-19
This technical report describes a dynamic causal model of the spread of coronavirus through a population.
Active inference: demystified and compared
In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in RL; 2) an explicit discrete-state comparison between active inference and RL on an OpenAI gym baseline.
Neurovascular coupling: insights from multi-modal dynamic causal modelling of fMRI and MEG
The ensuing estimates of neuronal parameters are used to generate neuronal drive functions, which model the pre or post synaptic responses to each experimental condition in the fMRI paradigm.
Quantitative Methods
How Robust are Deep Neural Networks?
In this paper, we evaluate the robustness of three recurrent neural networks to tiny perturbations, on three widely used datasets, to argue that high accuracy does not always mean a stable and a robust (to bounded perturbations, adversarial attacks, etc.)
Construct validation of a DCM for resting state fMRI
We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences.
