title: Modeling behavior in different delay match to sample tasks in one simple network.
abstract: Delay match to sample (DMS) experiments provide an important link between the theory of recurrent network models and behavior and neural recordings. We define a simple recurrent network of binary neurons with stochastic neural dynamics and Hebbian synaptic learning. Most DMS experiments involve heavily learned images, and in this setting we propose a readout mechanism for match occurrence based on a smaller increment in overall network activity when the matched pattern is already in working memory, and a reset mechanism to clear memory from stimuli of previous trials using random network activity. Simulations show that this model accounts for a wide range of variations on the original DMS tasks, including ABBA tasks with distractors, and more general repetition detection tasks with both learned and novel images. The differences in network settings required for different tasks derive from easily defined changes in the levels of noise and inhibition. The same models can also explain experiments involving repetition detection with novel images, although in this case the readout mechanism for match is based on higher overall network activity. The models give rise to interesting predictions that may be tested in neural recordings.
authors.affiliation: Department of Statistics, Chicago University Chicago, IL, USA ; Department of Computer Science, Chicago University Chicago, IL, USA.
authors.forename: Yali
authors.initials: Y
authors.lastname: Amit
authors.forename: Volodya
authors.initials: V
authors.lastname: Yakovlev
authors.forename: Shaul
authors.initials: S
authors.lastname: Hochstein
keywords: Hebbian learning; familiarity; forgetting; memory; readout mechanism; recurrent networks; reset mechanism; working memory
references.citation: Cereb Cortex. 2005 May;15(5):602-15
references.pmid: 15342436
references.citation: Nature. 1988 Oct 27;335(6193):817-20
references.pmid: 3185711
references.citation: J Neurosci. 2008 Jan 2;28(1):239-48
references.pmid: 18171941
references.citation: Psychon Bull Rev. 2008 Oct;15(5):906-26
references.pmid: 18926982
references.citation: Nat Rev Neurosci. 2007 Nov;8(11):872-83
references.pmid: 17948032
references.citation: J Comput Neurosci. 2001 Jul-Aug;11(1):63-85
references.pmid: 11524578
references.citation: Neural Comput. 2010 Mar;22(3):660-88
references.pmid: 19842984
references.citation: J Comput Neurosci. 2001 Jan-Feb;10(1):5-23
references.pmid: 11316340
references.citation: Cereb Cortex. 2003 May;13(5):435-43
references.pmid: 12679290
references.citation: Psychon Bull Rev. 2005 Oct;12(5):865-73
references.pmid: 16524003
references.citation: J Neurosci. 1996 Aug 15;16(16):5154-67
references.pmid: 8756444
references.citation: Nat Rev Neurosci. 2001 Jan;2(1):51-61
references.pmid: 11253359
references.citation: J Exp Psychol Learn Mem Cogn. 1994 Nov;20(6):1341-54
references.pmid: 7983467
references.citation: Nat Rev Neurosci. 2010 Jul;11(7):459-73
references.pmid: 20559335
references.citation: J Comput Neurosci. 2011 Jun;30(3):699-720
references.pmid: 20978831
references.citation: Science. 1971 Aug 13;173(3997):652-4
references.pmid: 4998337
references.citation: Psychol Rev. 2007 Jan;114(1):152-76
references.pmid: 17227185
references.citation: J Neurosci. 2011 May 11;31(19):6982-96
references.pmid: 21562260
references.citation: Neural Comput. 2008 Aug;20(8):1928-50
references.pmid: 18386988
references.citation: Science. 1994 Jan 28;263(5146):520-2
references.pmid: 8290960