The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations

Abstract

It is commonly assumed that memories about experienced stimuli are represented by groups of highly interconnected neurons called cell assemblies. This requires allocating and storing information in the neural circuitry, which happens through synaptic weight adaptations at different types of synapses. In general, memory allocation is associated with synaptic changes at feed-forward synapses while memory storage is linked with adaptation of recurrent connections. It remains, however, largely unknown how memory allocation and storage can be achieved and the adaption of the different synapses involved be coordinated to allow for a faithful representation of multiple memories without disruptive interference between them. In this theoretical study, by using network simulations and phase space analyses, we show that the interplay between long-term synaptic plasticity and homeostatic synaptic scaling organizes simultaneously the adaptations of feed-forward and recurrent synapses such that a new stimulus forms a new memory and where different stimuli are assigned to distinct cell assemblies. The resulting dynamics can reproduce experimental in-vivo data, focusing on how diverse factors, such as neuronal excitability and network connectivity, influence memory formation. Thus, the here presented model suggests that a few fundamental synaptic mechanisms may suffice to implement memory allocation and storage in neural circuitry.

Memory destabilization during reconsolidation – A consequence of homeostatic plasticity?

“…The first (Osan et al, 2011) studied the transition between reconsolidation and extinction in an attractor network, with labilization driven by mismatch between the training and reactivation patterns. The second (Auth et al, 2020), used a combination of Hebbian plasticity and homeostatic synaptic scaling to allocate stimuli as internal representations in a memory network (Auth et al, 2020).…”

“…Code for the simulations is available at https://github.com/Felippe-espinelli/scaling_destabilization_models. Model 2 -Adaptation of Auth et al (2020) The model is composed of an input network and a memory network with 36 and 900 neurons, respectively. The memory network is a grid of 30 x 30 neurons organized in a toroidal topology; each of them connects with 4 random neurons in the input area and with their nearestneighbors in the memory area within a radius of 4 neurons.…”

“…To investigate whether homeostatic plasticity could be a feasible mechanism for retrieval-induced memory destabilization, we performed a computational study using two different network models, previously developed to study each of the processes separately (Osan et al, 2011; Auth et al, 2020). This was followed by systematic reviews of the molecular mechanisms of both phenomena and of existing links between homeostatic plasticity and memory destabilization in the literature.…”

Homeostatic Synaptic Scaling Establishes the Specificity of an Associative Memory

“…Extensive theoretical work has explored the idea that Hebbian forms of plasticity (such as LTP) can destabilize neural networks by saturating synaptic weights, and that the addition of synaptic scaling can counteract this instability to help establish or maintain memory specificity 8,9,10,17 . There is some experimental evidence that blocking pathways important for synaptic scaling can influence memory 45,46 , but it was not clear from these earlier studies whether these manipulations impacted the transition from generalized to specific memories following the induction of learning.…”

“…While LTP is generally considered critical for learning and memory 5,6,7 , it may not be sufficient to faithfully encode memories due to the positive feedback nature of Hebbian learning rules 8,9 . Left unchecked, this positive feedback could give rise to the unconstrained enhancement of synaptic strengths, which in turn might result in the degradation of memory specificity 10 . These theoretical considerations suggest that for a memory to be properly encoded, additional homeostatic plasticity mechanisms are required to counterbalance this “runaway” plasticity.…”