An Oscillatory Hebbian Network Model of Short-Term Memory

Neural Computation - Tập 21 Số 3 - Trang 741-761 - 2009
Ransom K. Winder1, James A. Reggia1, Scott A. Weems2, Michael F. Bunting2
1Center for Advanced Study of Language and Department of Computer Science, University of Maryland, College Park, MD 20742, U.S.A.
2Center for Advanced Study of Language and Department of Psychology, University of Maryland, College Park, MD 20742, U.S.A.

Tóm tắt

Recurrent neural architectures having oscillatory dynamics use rhythmic network activity to represent patterns stored in short-term memory. Multiple stored patterns can be retained in memory over the same neural substrate because the network's state persistently switches between them. Here we present a simple oscillatory memory that extends the dynamic threshold approach of Horn and Usher ( 1991 ) by including weight decay. The modified model is able to match behavioral data from human subjects performing a running memory span task simply by assuming appropriate weight decay rates. The results suggest that simple oscillatory memories incorporating weight decay capture at least some key properties of human short-term memory. We examine the implications of the results for theories about the relative role of interference and decay in forgetting, and hypothesize that adjustments of activity decay rate may be an important aspect of human attentional mechanisms.

Từ khóa


Tài liệu tham khảo

10.1111/1467-9280.00405

10.1017/CBO9780511623257

Baddeley A., 2000, The Oxford handbook of memory, 77, 10.1093/oso/9780195122657.003.0005

10.1016/0001-6918(82)90043-9

Brown A., 2000, Oscillations in neural systems, 327

10.1080/17470215808416249

Bunting M., 2006, Journal of Experimental Psychology: Human Perception and Performance, 59, 1691, 10.1080/17470210600848402

10.1093/acprof:oso/9780195301069.001.0001

10.1007/s004220050290

10.1016/S0893-6080(02)00028-X

10.1017/S0140525X01003922

10.1016/j.cogpsych.2004.12.001

Deb K., 2001, Multi-objective optimization using evolutionary algorithms, 13

Downing C., 1985, Attention and performance, 11, 171

10.1111/1467-8721.00160

10.3758/BF03211502

10.1038/nature04587

10.3758/CABN.1.2.137

10.3758/BF03196193

10.1162/089976602317318965

10.1016/0893-6080(94)90017-5

Haykin S., 1999, Neural networks: A comprehensive foundation

10.1073/pnas.79.8.2554

10.1162/neco.1991.3.1.31

M. Howard (in press ).Computational models of memory. In L. Squire (Ed.),The new encyclopedia of neuroscience.Amsterdam:Elsevier.

10.1093/cercor/bhg084

10.1016/0749-596X(91)90032-F

10.1007/s004220000211

10.1103/PhysRevE.52.4561

10.1046/j.1460-9568.2002.01975.x

10.1016/S0959-4388(01)00278-1

10.3758/BF03196323

Kimura H., 2002, Proc. 2002 IEEE Internat. Conf. Robotics and Automation, 2228

10.1093/cercor/bhl088

10.1016/j.brainres.2007.06.067

10.1126/science.7878473

10.1093/acprof:oso/9780195050387.001.0001

10.1162/089976606775093909

10.1162/089976602760805331

10.1037/h0046137

10.1162/08989290152541430

Rizzuto D., 2003, Proceedings of the National Academy of Science of the United States of America, 19, 1

10.1523/JNEUROSCI.23-34-10809.2003

10.1109/72.392256

10.1037/h0021797

Thông tin
Thông tin xuất bản

Neural Computation

Tập 21 Số 3

741-761

Thông tin tác giả