Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data
Ulises Pereira1, Nicolas Brunel2
1Department of Statistics, The University of Chicago, Chicago, IL 60637, USA.
Neuron
|June 19, 2018
Summary
This study models memory storage in the brain using attractor neural networks, finding optimized learning rules in the inferior temporal cortex (ITC) that support a large number of memory states.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Science
Background:
- Attractor neural network models are widely used for memory storage in the association cortex.
- A significant gap exists between these models and experimental data.
Purpose of the Study:
- To develop a recurrent network model that bridges the gap between attractor network theory and experimental data.
- To infer learning rules and pattern distributions from visual response data in the inferior temporal cortex (ITC).
Main Methods:
- Studied a recurrent network model incorporating inferred learning rules and stored pattern distributions.
- Inferred parameters from visual response distributions for novel and familiar images in the ITC.
- Analyzed network activity during retrieval states.
Main Results:
- The model exhibits graded activity in retrieval states, with lognormal firing rate distributions, unlike classical models.
- Inferred unsupervised Hebbian learning rules approximate maximization of stored patterns, suggesting optimization for capacity.
- Identified two retrieval states: one with constant firing rates and another with chaotic fluctuations.
Conclusions:
- The developed model provides a more biologically plausible account of memory storage in the ITC.
- Learning rules in the ITC appear optimized for storing a large repertoire of attractor states.
- The existence of distinct retrieval states (stable and chaotic) offers new insights into memory recall mechanisms.
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