Memory recall and spike-frequency adaptation.
The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.
Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan 48109, USA.
James P
JP
Roach
Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.|Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan 48109, USA.
Leonard M
LM
Sander
Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.|Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan 48109, USA.|Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109, USA.
Michal R
MR
Zochowski
D001519:Behavior / Q000502:physiology; D006801:Humans; D008568:Memory; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology