Claudia Clopath

TL;DR: In this article, the spatial tuning of individual neurons led to the development of inverse selectivity in a subset of their presynaptic interneurons and recruited functionally coupled place cells at that location, and the spatial selectivity of single CA1 neurons is amplified through local circuit plasticity to enable effective multi-neuronal representations that can flexibly scale environmental features locally without degrading the feedforward input structure.

TL;DR: The optimal input has a sparse binary distribution, in good agreement with the burst firing of the Granule cells, and the weight distribution consists of a large fraction of silent synapses, as in previously studied binary perceptron models, and as seen experimentally.

TL;DR: A method for tackling catastrophic forgetting in deep reinforcement learning that isagnostic to the timescale of changes in the distribution of experiences, does not require knowledge of task boundaries, and can adapt in continuously changing environments is proposed.

TL;DR: It is shown in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning.

TL;DR: In this paper, the authors discuss the challenges of building biophysically plausible spiking neural models that are also capable of complex information processing, which creates new opportunities in neuroscience and neuromorphic engineering.