ePoster

A BASAL GANGLIA CIRCUIT FOR LEARNING TO SUPPRESS ESCAPE BEHAVIOUR

Stephen Lenziand 4 co-authors

Sainsbury Wellcome Centre for Neural Circuits and Behaviour

FENS Forum 2026 (2026)
Barcelona, Spain
Board PS06-09PM-423

Presentation

Date TBA

Board: PS06-09PM-423

Poster preview

A BASAL GANGLIA CIRCUIT FOR LEARNING TO SUPPRESS ESCAPE BEHAVIOUR poster preview

Event Information

Poster Board

PS06-09PM-423

Abstract

Escape responses to innately threatening stimuli can be learned to be suppressed and reflect a flexible decision making process that is dependent on recent experience. Here in mice we find that dopaminergic input from the Substantia Nigra pars Lateralis/Compacta (SNL/SNC) onto the Tail of the Striatum (TS) forms a circuit that gates experience-dependent suppression of escape.

We first show that chemical lesions of the TS abolish escape. Fibre photometry recordings reveal that dopaminergic inputs from SNL/SNC to the TS exhibit robust Ca2+ responses to looming stimuli that correlated with escape probability and were reduced during and following Learned Suppression of Escape (LSE). In contrast, in mice where the learning protocol was ineffective and LSE did not occur, there was no attenuation in SNL/SNC input signals. Furthermore, during the learning protocol, optogenetic activation of SNL/SNC fibres within the TS reduced the likelihood of escape suppression.

In the TS, Ca2+ signals in D1 neurons showed robust responses to looming stimuli that correlated with escape probability and vigour. Although both TS D1 and D2 responses attenuated during LSE, in cases where mice failed to suppress escape, TS D1 but not D2 responses were maintained. A simple biologically plausible two-rule learning model suggests that plasticity mechanisms drive changes in TS-D1 signalling that can implement either reinforcement or attenuation of escape in an experience-dependent manner. Together these results place the SNL/SNC and TS as a circuit hub for integrating threat and experience signals to modulate escape decisions.

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