Topic: cognitive tasks

Behavioural feedback is critical for learning in the cerebral cortex, but such feedback is often not readily available which slows down learning. Inspired by deep learning algorithms, we introduce a systems-level computational model of cerebro-cerebellar interactions in which a cerebral recurrent network receives continuous cerebral feedback predictions from a cerebellar network, thereby decoupling learning in cerebral networks from future feedback. When trained in a simple sensorimotor task the model shows faster learning and reduced ataxia-like behaviours, in line with experimental observati

In the human brain, some individual neurons respond selectively to abstract variables, invariant to sensory grounding (Mansouri, Freedman, & Buckley, 2020). Similarly tuned units also appear in artificial networks trained on cognitive tasks (Goh et al., 2021). It is often implicitly assumed that the emergence of such interpretable neurons plays a key role in the behavior of the trained network. Here we show that this is not necessarily the case. We train a biologically inspired artificial agent comprising two key components—a recurrent network and an associative memory—on a canonical rule

Artificial recurrent neural networks (RNNs) have become a customary research tool in theoretical neuroscience, being used as models of biological neural networks performing cognitive tasks. While RNNs are not generally intended as faithful, biologically realistic models of neuronal activity, there is a broad expectation that RNN dynamics may provide good models at the level of neural population dynamics. According to several authors, in both cases population activity should organize into low-dimensional manifolds encoding the relevant task variables (1). However, strong tests of this hypothesi