Switching between internal and external modes: A multiscale learning principle

By Christopher J. Honey, Ehren L. Newman, Anna C. Schapiro. 2017

112 citations recorded by the source corpus.

Brains construct internal models that support perception, prediction, and action in the external world. Individual circuits within a brain also learn internal models of the local world of input they receive, in order to facilitate efficient and robust representation. How are these internal models learned? We propose that learning is facilitated by continual switching between internally biased and externally biased modes of processing. We review computational evidence that this mode-switching can produce an error signal to drive learning. We then consider empirical evidence for the instantiation of mode-switching in diverse neural systems, ranging from subsecond fluctuations in the hippocampus to wake-sleep alternations across the whole brain. We hypothesize that these internal/external switching processes, which occur at multiple scales, can drive learning at each scale. This framework predicts that (a) slower mode-switching should be associated with learning of more temporally extended input features and (b) disruption of switching should impair the integration of new information with prior information.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Tinne Tuytelaars
2. Rahaf Aljundi

In Cognitive Neuroscience

1. Min Lin
2. Tinne Tuytelaars
3. Massimo Caccia
4. Laurent Charlin
5. Rahaf Aljundi
6. Lucas Caccia

Citations in the corpus

• Online Continual Learning with Maximally Interfered Retrieval (2019, background): “For example it is hypothesized in [15, 25] similar mechanisms might occur to accommodate recent events while preserving old memories.”
• Continual Learning in Neural Networks (2019, background): “In fact, there seems to be evidence for experience replay between hippocampus and neocortex in order to accommodate the recent events while preserving the old memories [59].”
• Decoding the tradeoff between encoding and retrieval to predict memory for overlapping events (2018, unknown): “Thus, considering the current findings in relation to prior evidence for encoding/retrieval tradeoffs, it seems likely that these tradeoffs can occur at multiple timescales and that different timescales for these biases may reflect distinct neural mechanisms (Honey et al., 2017).”
• Dynamic internal states shape memory retrieval. (2019, unknown): “One hypothesis is that externally oriented attention modulates hippocampal representations via acetylcholine (Newman et al., 2012; Honey et al., 2017).”