Plasticity and tuning by visual feedback of the stability of a neural integrator

Guy Major, Robert Baker, Emre Aksay, Brett Mensh, H. Sebastian Seung, David W. Tank

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

Persistent neural firing is of fundamental importance to working memory and other brain functions because it allows information to be held "online" following an input and to be integrated over time. Many models of persistent activity rely on some kind of positive feedback internal to the neural circuit concerned; however, too much feedback causes runaway firing (instability), and too little results in loss of persistence (leak). This parameter sensitivity leads to the hypothesis that the brain uses an error signal (external feedback) to tune the stability of persistent firing by adjusting the amount of internal feedback. We test this hypothesis by manipulating external visual feedback, a putative sensory error signal, in a model system for persistent firing, the goldfish oculomotor neural integrator. Over tens of minutes to hours, electronically controlled visual feedback consistent with a leaky or unstable integrator can drive the integrator progressively more unstable or leaky, respectively. Eye fixation time constants can be reduced > 100-fold to < 1 s. Normal visual feedback gradually retunes the integrator back to stability. Changes in the phase of the sinusoidal vestibuto-ocular response are consistent with integrator detuning, as are changes in ocular drift following eye position shifts compensating for brief passive head movements during fixations. Corresponding changes in persistent firing of integrator neurons are presented in the accompanying article. The presence, strength, and reversibility of the plasticity demonstrate that, in this system, external visual feedback plays a vital role in gradually tuning the stability of the neural integrator.

Original languageEnglish (US)
Pages (from-to)7739-7744
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume101
Issue number20
DOIs
StatePublished - May 18 2004

All Science Journal Classification (ASJC) codes

  • General

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