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<p>Neurons in basolateral amygdala (BLA) encode positive and negative valence. However, many additional variables must be represented to describe all aspects of emotional states. To investigate how BLA encodes these states, we presented mice with conditioned stimuli that elicited two behavioral responses: tremble and ingress into a burrow, reflecting fear and flight to safety, respectively. BLA inactivation eliminated several aspects of differential responses to aversive versus neutral stimuli without eliminating tremble and ingress themselves, consistent with BLA’s encoding valence not motor commands. However, individual neurons rarely represented only valence, exhibiting, instead, mixed selectivity for stimulus identity, stimulus valence, tremble and/or ingress. Despite prevalent mixed selectivity, population activity sometimes realized a representational geometry that conferred two computational properties defining specialized readouts: generalization across conditions and no interference between readouts of different variables. These specialized readouts enable output responses to depend on one specific variable and to remain unaffected by the others.</p>

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The representational geometry of emotional states in basolateral amygdala

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Pia-Kelsey O’Neill,
  • Lorenzo Posani,
  • Jozsef Meszaros,
  • Phebe Warren,
  • Carl E. Schoonover,
  • Andrew J. P. Fink,
  • Stefano Fusi,
  • C. Daniel Salzman

摘要

Neurons in basolateral amygdala (BLA) encode positive and negative valence. However, many additional variables must be represented to describe all aspects of emotional states. To investigate how BLA encodes these states, we presented mice with conditioned stimuli that elicited two behavioral responses: tremble and ingress into a burrow, reflecting fear and flight to safety, respectively. BLA inactivation eliminated several aspects of differential responses to aversive versus neutral stimuli without eliminating tremble and ingress themselves, consistent with BLA’s encoding valence not motor commands. However, individual neurons rarely represented only valence, exhibiting, instead, mixed selectivity for stimulus identity, stimulus valence, tremble and/or ingress. Despite prevalent mixed selectivity, population activity sometimes realized a representational geometry that conferred two computational properties defining specialized readouts: generalization across conditions and no interference between readouts of different variables. These specialized readouts enable output responses to depend on one specific variable and to remain unaffected by the others.