24 citations · 168 across the 34 of their papers we have counts for
4 papers · 1 filter
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…
Fast sampling for Bayesian inference in neural circuits
Guillaume Hennequin, Laurence Aitchison, Máté Lengyel
Time is at a premium for recurrent network dynamics, and particularly so when they are stochastic and correlated: the quality of inference from such dynamics fundamentally depends…