Non-equilibrium statistical mechanics of continuous attractors
arXiv:1809.11167
Abstract
Continuous attractors have been used to understand recent neuroscience experiments where persistent activity patterns encode internal representations of external attributes like head direction or spatial location. However, the conditions under which the emergent bump of neural activity in such networks can be manipulated by space and time-dependent external sensory or motor signals are not understood. Here, we find fundamental limits on how rapidly internal representations encoded along continuous attractors can be updated by an external signal. We apply these results to place cell networks to derive a velocity-dependent non-equilibrium memory capacity in neural networks.
arXiv comment17 pages
References in corpus (6)
- Accurate path integration in continuous attractor network models of grid cells
- Attractor neural networks storing multiple space representations: a model for hippocampal place fields
- A balanced memory network
- Transitions between spatial attractors in place-cell networks
- Crosstalk and transitions between multiple spatial maps in an attractor neural network model of the hippocampus: Collective motion of the activity (II)
- Cross-talk and transitions between multiple spatial maps in an attractor neural network model of the hippocampus: phase diagram (I)