Topic: neural circuit architecture

A fundamental goal of neuroscience is the functional understanding of neural circuit architecture. Previous studies revealed the importance of network depth in visual and auditory circuits, but it remains elusive why predominantly non-convolutional circuits, such as the cerebellum or olfactory systems, have multiple feedforwardly connected layers. Recent deep learning works revealed that a two-hidden-layer network has a quadratically more memory capacity than a single-hidden-layer network with the same number of hidden layer neurons, suggesting the importance of the network depth for memory ca

To successfully forage for food, animals much balance the energetic cost of searching for and traveling between food sources with the energetic benefit of exploiting those sources. The Marginal Value Theorem (MVT) provides one normative account of this balance by specifying that a forager should leave a food patch when its energetic yield falls below the average yield of other patches in the environment. This framework and its extensions treat the decision of when and how fast to leave a patch under the assumption that other patches exist and are readily reachable. However, in natural settings