The computational nature of memory modification

Abstract

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.DOI: http://dx.doi.org/10.7554/eLife.23763.001

Does mental context drift or shift?

“…Also, if multiple latent causes are active, how do we assign credit so that we update the appropriate latent cause when new information comes in? These issues intersect with the existing literatures on cognitive control, multitasking, and reinforcement learning [44*,52,63,64]. Drawing on these literatures might provide useful insight into how latent-cause models could be implemented in the brain, and how they interact with neural memory processes.…”

Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams

“…the components of which adhere to the calcium dynamics Equation 14, the connectivity dynamics Equation 15, and the activity dynamics Equation 13. The joint ODE system dy dt = F(y, t) defines the vector field F. We first explore the stationary states of the deterministic system (σ x = 0), setting the left hand side of all ODEs to zero.…”

“…In this case, calcium concentration and excitatory firing rates are fixed at their target values, and inhibitory firing rates r * I can be obtained using the self-consistency Equation 13. Stationary connectivity is calculated from the condition that the rates of creation and deletion of synaptic elements are zero.…”

Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

“…Emotions provide humans with useful internal signals that promote adaptive behavior (e.g., Gershman, Monfils, Norman, & Niv, 2017;Martin et al, 2015). However, emotions can become detrimental if left unregulated.…”