Short-term working memory is critical for all cognition. It is important to fluid intelligence by definition and is disordered in many psychiatric conditions. It is also an ideal model system for studying the link between the dynamics and functions of neural circuits. Short-term storage requires dynamics that are flexible enough to allow continuous incorporation of new information, yet stable enough to retain information for tens of seconds. Much is known about the neuronal substrate of short-term memory. There is a gap, however, in our knowledge of how neuronal resources are efficiently allocated to store multiple items. This gap is particularly striking given that a multi-item memory task (memory span task) is often used to measure fluid intelligence. Neurons in frontal areas are active during a memory period, and individual neurons are tuned to respond to particular memoranda. It is known that individual cells ramp up or down during a memory period. However, we were surprised to discover in preliminary experiments that 80% of individual cells in memory circuits lose their tuning before the end of a 15s memory period. This loss of tuning occurs at similar times across repeated trials; a neuron that loses tuning at 3s in one trial seldom remains tuned for more than 7s in a subsequent trial, and vice versa. This leads to the question of whether cells with common “drop-out” times are linked together in a subnetwork, similar to the “slot” organization often posited to support multi-item memory. We formulated a theory about how these subnetworks might be organized to enact a form of efficient resource allocation that balances demand for memory capacity against memory duration. The primary goal of our TMM project is to test the validity of this theory, and more generally probe memory circuits for evidence of functional subnetworks, using a unique combination of long-delay multi-item memory tasks, computational modeling and analysis. Our project integrates experimental and computational methods, including formalisms from information and control theories, so as to build tight links between (i) the observed phenomenology; (ii) the mathematical consistency of the theory; and (iii) how (i) and (ii) might be reconciled mechanistically in the dynamics of neural circuits
What analytical (Theories, Models and Methods) tools have you developed?
We are developing bottom-up and top-down circuit-level models to derive new mechanistic understanding of how working memory is encoded. These models are constrained by neuronal biophysics but optimized in order to meet hypothetical high-level functional objectives associated with working memory function.
What questions can you answer?
Our immediate goal is to gain a deeper understanding of how memory resources are encoded and allocated/managed within neural circuits. More broadly, our goal is to develop a general modeling paradigm that can associate dynamics to higher-level circuit function.
What input do you need? (e.g., cellular activity, sub-cellular, sensory input, complex behavior)
Validation of our theory requires recordings of cellular activity alongside behavioral characterizations (working memory performance). Our immediate goals are in the domain of spatial working memory, but it would be of interest to broaden the scope to other memory domains.
What are the data specifications needed for your TMM tool? (e.g. data type, sampling frequency, species type, brain area, modality, cell type, duration of recording)
Our plans are to validate our theory in NHPs with recordings from dorsolateral prefrontal cortex and frontal eye fields while animas are engaged in a spatial working memory task.