Summary: Researchers present a new model that may explain the flexibility of working memory.
Source: Princeton University
A new article in Neuron from Princeton University neuroscientists Flora Bouchacourt and Tim Buschman presents a new model of working memory.
Working memory is your ability to hold things ‘in mind.’ It acts as a workspace in which information can be held, manipulated, and then used to guide behavior. In this way, it plays a critical role in cognition, decoupling behavior from the immediate sensory world. One of the remarkable things about working memory is its flexibility — you can hold anything in mind.
How this flexibility is achieved has not been understood. In their new manuscript, Bouchacourt and Buschman present a new model of working memory that captures this flexibility.
The model combines a high-dimensional random network with structured sensory networks to flexibly maintain any input. The untuned nature of the connections allows the network to maintain any arbitrary input.
However, this flexibility comes at a cost: the random connections overlap, leading to interference between representations and limiting the memory capacity of the network. This matches the limited capacity of working memory in humans and suggests there is a tradeoff between flexibility and capacity in working memory.
In addition, the model captures several other behavioral and neurophysiological characteristics of working memory.
This work provides new insight into a core cognitive function in humans. Ongoing work hopes to understand how these mechanisms may be disrupted in neuropsychiatric diseases that disrupt working memory.
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Source: Princeton University Media Contacts:
Annie Mingle – Princeton University Image Source:
The image is credited to Princeton University.
• Random recurrent connections can support flexible working memory
• Overlap of connections causes interference between memories, limiting capacity
• Model captures many behavioral and physiological characteristics of working memory
• Structured sensory networks can constrain high-dimensional random representations
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.
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