Topic: line attractor

Strongly interconnected neuronal populations are thought to be a substrate for memory in the brain. Dynamic connectivity relies on plasticity rules to adjust synapses, enabling learning and neural circuit formation. Networks with only excitatory synaptic plasticity can become unstable due to runaway excitation, compromising a circuit's ability to store memories. To counter this, homeostatic synaptic plasticity of inhibitory connections provide a matched negative feedback that stabilizes neural activity and any associated learning. In this study, we outline the conditions under which recurrent

Behavioural tasks requiring context-dependent selection and accumulation of noisy sensory evidence are a powerful tool to study flexible decision-making (Mante et al., 2013), and recent work revealed a surprisingly degree of variability in the underlying neural mechanisms across individual animals (Pagan et al., 2022). Here we study how this individual variability develops during learning in rats and recurrent neural networks (RNNs). First, we show that the number of hidden units in RNNs plays a crucial role in the resulting amount of variability across networks initialized with different rand