Topic: neuronal network

The ability to discriminate different sensory patterns by disentangling their neural representations is an important property of neural networks. While a variety of learning rules are known to be highly effective at fine-tuning synapses to achieve this, less is known about how different cell types in the brain can facilitate this process by providing architectural priors that bias the network towards sparse, selective, and discriminable representations. We studied this by simulating a neuronal network modelled on the dentate gyrus—an area characterised by sparse activity associated with patter

For more than 20 years, a growing body of evidence indicates that the brain functions near a critical point, where neural activity is balanced between damping and amplification. One of the appealing benefits of criticality is its optimal information processing [1, 2]. In addition, deviations from criticality are associated with age and disease [3] but also physiological state [4]. Yet the neural mechanisms involved in maintaining and/or altering this criticality remain unclear. To investigate how cellular mechanisms can influence criticality, we analyze a model of neuronal network [5] and quan