TY - JOUR
T1 - A Flexible Model of Working Memory
AU - Bouchacourt, Flora
AU - Buschman, Timothy J.
N1 - Funding Information:
The authors are grateful to Sarah Henrickson, Adam Charles, Alex Libby, Camden MacDowell, Sebastian Musslick, and Matt Panichello for discussions and feedback on the manuscript. This work was supported by NIMH R56MH115042 and ONR N000141410681 to T.J.B.
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/7/3
Y1 - 2019/7/3
N2 - Working memory is fundamental to cognition, allowing one to hold information “in mind.” A defining characteristic of working memory is its flexibility: we can hold anything in mind. However, typical models of working memory rely on finely tuned, content-specific attractors to persistently maintain neural activity and therefore do not allow for the flexibility observed in behavior. Here, we present a flexible model of working memory that maintains representations through random recurrent connections between two layers of neurons: a structured “sensory” layer and a randomly connected, unstructured layer. As the interactions are untuned with respect to the content being stored, the network maintains any arbitrary input. However, in our model, this flexibility comes at a cost: the random connections overlap, leading to interference between representations and limiting the memory capacity of the network. Additionally, our model captures several other key behavioral and neurophysiological characteristics of working memory.
AB - Working memory is fundamental to cognition, allowing one to hold information “in mind.” A defining characteristic of working memory is its flexibility: we can hold anything in mind. However, typical models of working memory rely on finely tuned, content-specific attractors to persistently maintain neural activity and therefore do not allow for the flexibility observed in behavior. Here, we present a flexible model of working memory that maintains representations through random recurrent connections between two layers of neurons: a structured “sensory” layer and a randomly connected, unstructured layer. As the interactions are untuned with respect to the content being stored, the network maintains any arbitrary input. However, in our model, this flexibility comes at a cost: the random connections overlap, leading to interference between representations and limiting the memory capacity of the network. Additionally, our model captures several other key behavioral and neurophysiological characteristics of working memory.
KW - capacity limitations
KW - cognitive control
KW - cognitive flexibility
KW - computational model
KW - excitation-inhibition balance
KW - mixed selectivity
KW - working memory
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U2 - 10.1016/j.neuron.2019.04.020
DO - 10.1016/j.neuron.2019.04.020
M3 - Article
C2 - 31103359
AN - SCOPUS:85068159762
SN - 0896-6273
VL - 103
SP - 147-160.e8
JO - Neuron
JF - Neuron
IS - 1
ER -