17 citations · 39 across the 6 of their papers we have counts for
6 papers · 1 filter
Meta-Learning the Inductive Biases of Simple Neural Circuits
William Dorrell, Maria Yuffa, Peter Latham
Training data is always finite, making it unclear how to generalise to unseen situations. But, animals do generalise, wielding Occam's razor to select a parsimonious explanation of…
Actionable Neural Representations: Grid Cells from Minimal Constraints
William Dorrell, Peter E. Latham, Timothy E. J. Behrens +1
To afford flexible behaviour, the brain must build internal representations that mirror the structure of variables in the external world. For example, 2D space obeys rules: the sam…
Encoding priors in the brain: a reinforcement learning model for mouse decision making
Sanjukta Krishnagopal, Peter Latham
In two-alternative forced choice tasks, prior knowledge can improve performance, especially when operating near the psychophysical threshold. For instance, if subjects know that on…
Sparse connectivity for MAP inference in linear models using sister mitral cells
Sina Tootoonian, Peter Latham
Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in sensory processing is to understand how the br…
Robust information propagation through noisy neural circuits
Joel Zylberberg, Alexandre Pouget, Peter E. Latham +1
Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the f…
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…