TY - JOUR
T1 - Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity
AU - Koyluoglu, Onur Ozan
AU - Pertzov, Yoni
AU - Manohar, Sanjay
AU - Husain, Masud
AU - Fiete, Ila R.
N1 - Publisher Copyright:
© Koyluoglu et al..
PY - 2017/9/7
Y1 - 2017/9/7
N2 - It is widely believed that persistent neural activity underlies short-term memory. Yet, as we show, the degradation of information stored directly in such networks behaves differently from human short-term memory performance. We build a more general framework where memory is viewed as a problem of passing information through noisy channels whose degradation characteristics resemble those of persistent activity networks. If the brain first encoded the information appropriately before passing the information into such networks, the information can be stored substantially more faithfully. Within this framework, we derive a fundamental lower-bound on recall precision, which declines with storage duration and number of stored items. We show that human performance, though inconsistent with models involving direct (uncoded) storage in persistent activity networks, can be well-fit by the theoretical bound. This finding is consistent with the view that if the brain stores information in patterns of persistent activity, it might use codes that minimize the effects of noise, motivating the search for such codes in the brain.
AB - It is widely believed that persistent neural activity underlies short-term memory. Yet, as we show, the degradation of information stored directly in such networks behaves differently from human short-term memory performance. We build a more general framework where memory is viewed as a problem of passing information through noisy channels whose degradation characteristics resemble those of persistent activity networks. If the brain first encoded the information appropriately before passing the information into such networks, the information can be stored substantially more faithfully. Within this framework, we derive a fundamental lower-bound on recall precision, which declines with storage duration and number of stored items. We show that human performance, though inconsistent with models involving direct (uncoded) storage in persistent activity networks, can be well-fit by the theoretical bound. This finding is consistent with the view that if the brain stores information in patterns of persistent activity, it might use codes that minimize the effects of noise, motivating the search for such codes in the brain.
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U2 - 10.7554/eLife.22225
DO - 10.7554/eLife.22225
M3 - Article
C2 - 28879851
AN - SCOPUS:85032983544
SN - 2050-084X
VL - 6
JO - eLife
JF - eLife
M1 - e22225
ER -