Emergent perceptual biases from state-space geometry in trained spiking recurrent neural networks.
Cell Rep
; 43(7): 114412, 2024 Jul 23.
Article
in En
| MEDLINE
| ID: mdl-38968075
ABSTRACT
A stimulus held in working memory is perceived as contracted toward the average stimulus. This contraction bias has been extensively studied in psychophysics, but little is known about its origin from neural activity. By training recurrent networks of spiking neurons to discriminate temporal intervals, we explored the causes of this bias and how behavior relates to population firing activity. We found that the trained networks exhibited animal-like behavior. Various geometric features of neural trajectories in state space encoded warped representations of the durations of the first interval modulated by sensory history. Formulating a normative model, we showed that these representations conveyed a Bayesian estimate of the interval durations, thus relating activity and behavior. Importantly, our findings demonstrate that Bayesian computations already occur during the sensory phase of the first stimulus and persist throughout its maintenance in working memory, until the time of stimulus comparison.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Bayes Theorem
Limits:
Animals
Language:
En
Journal:
Cell Rep
Year:
2024
Document type:
Article
Affiliation country: