Abstract context representations in primate amygdala and prefrontal cortex

By A. Saez, Mattia Rigotti, Srdjan Ostojic, Stefano Fusi, C. Daniel Salzman. 2015

170 citations recorded by the source corpus.

Summary Neurons in prefrontal cortex (PFC) encode rules, goals and other abstract information thought to underlie cognitive, emotional, and behavioral flexibility. Here we show that the amygdala, a brain area traditionally thought to mediate emotions, also encodes abstract information that could underlie this flexibility. Monkeys performed a task in which stimulus-reinforcement contingencies varied between two sets of associations, each defining a context. Reinforcement prediction required identifying a stimulus and knowing the current context. Behavioral evidence indicated that monkeys utilized this information to perform inference and adjust their behavior. Neural representations in both amygdala and PFC reflected the linked sets of associations implicitly defining each context, a process requiring a level of abstraction characteristic of cognitive operations. Surprisingly, when errors were made, the context signal weakened substantially in the amygdala. These data emphasize the importance of maintaining abstract cognitive information in the amygdala to support flexible behavior.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, positive): “For example, we can switch nimbly between sequential tasks that require distinct responses to the same input data, as when alternately judging fruit by shape or size and friends by gender or age (Roy et al., 2010; Mante et al., 2013; Saez et al., 2015; Takagi et al., 2020).”
Low-dimensional dynamics for working memory and time encoding (2020, background): “For example, consider the trace-conditioning task (39), where the offset of a visual stimulus is followed by a delay period and then either a water reward or no reward.”
The geometry of abstraction in hippocampus and prefrontal cortex (2020, background): “Neurons in ACC and DLPFC have been shown to encode rules and other cognitive information (Wallis et al., 2001;Miller et al., 2003; Buckley et al., 2009; Antzoulatos and Miller, 2011; Wutz et al., 2018; Saez et al., 2015), but testing whether a neural ensemble of single units represents one or more v”
Why neurons mix: high dimensionality for higher cognition. (2016, background): “Mixed selectivity has also been reported in the hippocampus, where single units can respond to multiple contextual and episodic features [13–15,16 ], and in the amygdala, where neurons can be selective to specific combinations of visual stimuli, temporal context and predicted reinforcers during cond”
Optimal degrees of synaptic connectivity (2017, unknown): “This is consistent with the observation that both task-relevant variables and reward are represented in higher neocortical regions (Saez et al., 2015).”
Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks. (2017, unknown): “We find that the dynamical repertoire of the network increases quickly with the dimensionality of the connectivity structure, so that rank-two connectivity structures are already sufficient to implement complex, context-dependent tasks (Mante et al., 2013; Saez et al., 2015).”
Towards the neural population doctrine. (2019, background): “In [23], the authors show that neurons in the amygdala have representations pertaining to both stimulus and context in a ﬂexible decision-making task, which may be the mechanism for the parietal prefrontal cortex neurons to access the same information.”