Topic: primate visual system

Mammalian brains increasingly reuse the same neurons and regions for different tasks, while maintaining robust performance when the environment or stimulus context changes. Udo Ernst combines theoretical modelling with quantitative physiological observations to investigate the mechanisms that support both flexibility and resilience. Selective processing in the primate visual system provides the central example. One analytically tractable framework routes attended external signals through avalanches of spikes in recurrent neuronal networks; a second examines control circuits that preserve relia

Task-trained deep neural networks have emerged as leading models of neural responses in the primate visual system, especially for later stages of the ventral stream such as inferotemporal (IT) cortex. One major criticism of this approach is that the tasks used to train such networks rely on large numbers of labeled examples, and are thus not ecologically plausible. Recent approaches in representation learning circumvent the need for labeled examples, and match or surpass supervised learning methods on a variety of tasks. These approaches generally rely on supervision signals extracted from the