24 citations · 168 across the 34 of their papers we have counts for
7 papers · 1 filter
What deep reinforcement learning tells us about human motor learning and vice-versa
Michele Garibbo, Casimir Ludwig, Nathan Lepora +1
Machine learning and specifically reinforcement learning (RL) has been extremely successful in helping us to understand neural decision making processes. However, RL's role in unde…
Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability
Laurence Aitchison, Guillaume Hennequin, Mate Lengyel
Neural responses in the cortex change over time both systematically, due to ongoing plasticity and learning, and seemingly randomly, due to various sources of noise and variability…
Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations
Laurence Aitchison, Peter E. Latham
When an action potential is transmitted to a postsynaptic neuron, a small change in the postsynaptic neuron's membrane potential occurs. These small changes, known as a postsynapti…
Synaptic plasticity as Bayesian inference
Laurence Aitchison, Jannes Jegminat, Jorge Aurelio Menendez +3
Learning, especially rapid learning, is critical for survival. However, learning is hard: a large number of synaptic weights must be set based on noisy, often ambiguous, sensory in…
Zipf's law arises naturally in structured, high-dimensional data
Laurence Aitchison, Nicola Corradi, Peter E. Latham
Zipf's law, which states that the probability of an observation is inversely proportional to its rank, has been observed in many domains. While there are models that explain Zipf's…
The Hamiltonian brain: efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics
Laurence Aitchison, Máté Lengyel
Probabilistic inference offers a principled framework for understanding both behaviour and cortical computation. However, two basic and ubiquitous properties of cortical responses…