PT  - JOURNAL ARTICLE
AU  - Ulises Pereira
AU  - Nicolas Brunel
TI  - Attractor dynamics in networks with learning rules inferred from <em>in vivo</em> data
AID  - 10.1101/199521
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 199521
4099  - http://biorxiv.org/content/early/2017/10/06/199521.short
4100  - http://biorxiv.org/content/early/2017/10/06/199521.full
AB  - The attractor neural network scenario is a popular scenario for memory storage in association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both learning rules and distribution of stored patterns are inferred from distributions of visual responses for novel and familiar images in inferior temporal cortex (ITC). Unlike classical attractor neural network models, our model exhibits graded activity in retrieval states, with distributions of firing rates that are close to lognormal. Inferred learning rules are close to maximizing the number of stored patterns within a family of unsupervised Hebbian learning rules, suggesting learning rules in ITC are optimized to store a large number of attractor states. Finally, we show that there exists two types of retrieval states: one in which firing rates are constant in time, another in which firing rates fluctuate chaotically.