Topic: recurrent circuits

Feedforward convolutional neural networks are currently the state-of-the-art approach for characterizing the transfer function of early visual cortical neurons. It is well known that the visual cortex has massive recurrent connections. Would models using recurrent circuits provide better neural response prediction than feedforward models? Here, we show that recurrent models consistently outperform feedforward ones when their parameters and hyperparameters are carefully matched. These benefits are robust against variation in the type of activation functions, loss function, number of channels, a

Layer 5 pyramidal neurons (L5 PNs) are a major cortical output. Their connectivity approximates a recurrent neural network (RNN), with the majority of inputs coming from other L5 PNs. These neurons may underlie anesthesia-induced unconsciousness, by restricting information output from cortex. Therefore, understanding the formation of the L5 PN circuitry, from the time at which L5 PNs first enter cortex, would provide insight into how a biological RNN can be constructed. However, this is technically challenging because, in mice, L5 PNs first enter cortex at embryonic day (E) 13.5, five days bef

In recurrent circuits, neurons and synapses are coupled in an intricate dance: neurons influence synapses through activity-dependent plasticity, and synapses influence neurons by shaping network dynamics. While previous studies have analyzed recurrent circuits with static synapses, or synapses displaying short-term facilitation and depression, the impact of ongoing Hebbian plasticity on network behavior is not well elucidated. Such an understanding is required to map the full landscape of neural-circuit dynamics and to probe computational roles of dynamic synapses. To address this knowledge ga