Memory without Feedback in a Neural Network

Mark S Goldman

Research output: Contribution to journalArticlepeer-review

193 Scopus citations


Memory storage on short timescales is thought to be maintained by neuronal activity that persists after the remembered stimulus is removed. Although previous work suggested that positive feedback is necessary to maintain persistent activity, here it is demonstrated how neuronal responses can instead be maintained by a purely feedforward mechanism in which activity is passed sequentially through a chain of network states. This feedforward form of memory storage is shown to occur both in architecturally feedforward networks and in recurrent networks that nevertheless function in a feedforward manner. The networks can be tuned to be perfect integrators of their inputs or to reproduce the time-varying firing patterns observed during some working memory tasks but not easily reproduced by feedback-based attractor models. This work illustrates a mechanism for maintaining short-term memory in which both feedforward and feedback processes interact to govern network behavior.

Original languageEnglish (US)
Pages (from-to)621-634
Number of pages14
Issue number4
StatePublished - Feb 26 2009



ASJC Scopus subject areas

  • Neuroscience(all)


Dive into the research topics of 'Memory without Feedback in a Neural Network'. Together they form a unique fingerprint.

Cite this