Can attractor network models account for the statistics of firing during persistent activity in prefrontal cortex?

被引:37
作者
Barbieri, Francesca [1 ]
Brunel, Nicolas [1 ,2 ,3 ]
机构
[1] ISI Fdn, Turin, Italy
[2] Univ Paris 05, Lab Neurophys & Physiol, Paris, France
[3] CNRS, UMR 8119, Paris, France
来源
FRONTIERS IN NEUROSCIENCE | 2008年 / 2卷 / 01期
关键词
network model; integrate-and-fire neuron; working memory; prefrontal cortex; short-term depression;
D O I
10.3389/neuro.01.003.2008
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Persistent activity observed in neurophysiological experiments in monkeys is thought to be the neuronal correlate of working memory. Over the last decade, network modellers have strived to reproduce the main features of these experiments. In particular, attractor network models have been proposed in which there is a coexistence between a non-selective attractor state with low background activity with selective attractor states in which sub-groups of neurons fire at rates which are higher (but not much higher) than background rates. A recent detailed statistical analysis of the data seems however to challenge such attractor models: the data indicates that firing during persistent activity is highly irregular (with an average CV larger than 1), while models predict a more regular firing process (CV smaller than 1). We discuss here recent proposals that allow to reproduce this feature of the experiments.
引用
收藏
页码:114 / 122
页数:9
相关论文
共 44 条
[2]   QUANTITATIVE STUDY OF ATTRACTOR NEURAL NETWORK RETRIEVING AT LOW SPIKE RATES .1. SUBSTRATE SPIKES, RATES AND NEURONAL GAIN [J].
AMIT, DJ ;
TSODYKS, MV .
NETWORK-COMPUTATION IN NEURAL SYSTEMS, 1991, 2 (03) :259-273
[3]   Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex [J].
Amit, DJ ;
Brunel, N .
CEREBRAL CORTEX, 1997, 7 (03) :237-252
[4]   Paradigmatic working memory (attractor) cell in IT cortex [J].
Amit, DJ ;
Fusi, S ;
Yakovlev, V .
NEURAL COMPUTATION, 1997, 9 (05) :1071-1092
[5]   QUANTITATIVE STUDY OF ATTRACTOR NEURAL NETWORKS RETRIEVING AT LOW SPIKE RATES .2. LOW-RATE RETRIEVAL IN SYMMETRICAL NETWORKS [J].
AMIT, DJ ;
TSODYKS, MV .
NETWORK-COMPUTATION IN NEURAL SYSTEMS, 1991, 2 (03) :275-294
[6]  
AMIT DJ, 1995, BEHAV BRAIN SCI, V18, P617, DOI 10.1017/S0140525X00040164
[7]   Irregular persistent activity induced by synaptic excitatory feedback [J].
Barbieri, Francesca ;
Brunel, Nicolas .
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2007, 1
[8]   Timing and neural encoding of somatosensory parametric working memory in macaque prefrontal cortex [J].
Brody, CD ;
Hernández, A ;
Zainos, A ;
Romo, R .
CEREBRAL CORTEX, 2003, 13 (11) :1196-1207
[9]   Persistent activity and the single-cell frequency-current curve in a cortical network model [J].
Brunel, N .
NETWORK-COMPUTATION IN NEURAL SYSTEMS, 2000, 11 (04) :261-280
[10]   Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition [J].
Brunel, N ;
Wang, XJ .
JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 2001, 11 (01) :63-85