Homeostatic Synaptic Scaling Establishes the Specificity of an Associative Memory

Abstract

Accurate memory formation has been hypothesized to depend on both rapid Hebbian plasticity for initial encoding, and slower homeostatic mechanisms that prevent runaway excitation and subsequent loss of memory specificity. Here, we tested the role of synaptic scaling in shaping the specificity of conditioned taste aversion (CTA) memory, a Hebbian plasticity-dependent form of associative learning. We found that CTA memory initially generalized to non-conditioned tastants (generalized aversion), becoming specific to the conditioned tastant only over the course of many hours. Blocking synaptic scaling in the gustatory cortex (GC) prolonged the duration of the initial generalized aversion and enhanced the persistence of synaptic strength increases observed after CTA. Taken together, these findings demonstrate that synaptic scaling is important for sculpting the specificity of an associative memory and suggest that the relative strengths of Hebbian and homeostatic plasticity can modulate the balance between stable memory formation and generalization.

Intrinsic excitability in layer IV-VI anterior insula to basolateral amygdala projection neurons encodes the confidence of taste valence

“…Consistent with studies in the hippocampus (Graves et al, 2016), we found that the percentage of BS LIV-VI aIC-BLA projecting neurons in the sampled population was highest in the context of novel taste learning (Extended Figure 1 (Dunn et al, 2018). Indeed, the ratio of BS:RS LIV-VI aIC-BLA neurons was progressively suppressed by familiarity acquisition (Saccharin 1x> 2x> 5x), as well as following aversive taste memory recall (CTA retrieval), compared to both appetitive learning Changes in the intrinsic properties of neuronal ensembles have recently been suggested to contribute to homeostatic mechanisms integrating both cellular and synaptic information (Wu et al, 2021). In our current study, we randomly sampled from neuroanatomically defined LIV-VI aIC-BLA projecting neurons, and even following spike sorting (Extended Figures 1-2), the probability of recording from engram cells (10% of neurons within a region) would be extremely low (Tonegawa et al, 2015).…”

“…Changes in the intrinsic properties of neuronal ensembles have recently been suggested to contribute to homeostatic mechanisms integrating both cellular and synaptic information (Wu et al, 2021). In our current study, we randomly sampled from neuroanatomically defined LIV-VI aIC-BLA projecting neurons, and even following spike sorting (Extended Figures 1-2), the probability of recording from engram cells (10% of neurons within a region) would be extremely low (Tonegawa et al, 2015).…”

“…Learning and memory are subserved by plasticity in both synapse strength and neuronal intrinsic properties (Citri and Malenka, 2008; Sehgal et al, 2013). While Hebbian rules can explain associative learning paradigms where seconds separate the CS and US (Krabbe et al, 2018), additional cellular-level mechanisms are needed to explain how learning operates in paradigms where the time between CS and US extends to hours (Adaikkan and Rosenblum, 2015; Wu et al, 2021). In this study, we demonstrate that following taste experiences, taste percept and prior experience are integrated in the intrinsic properties of the aIC in a time-dependent and cell-type specific manner.…”

“…Intrinsic excitability is the tendency of neurons to fire action potentials when exposed to inputs, reflecting changes in the suit and properties of specific ion channels (Disterhoft et al, 2004; Song and Moyer, 2018). Even though independent mechanisms are involved, recent evidence indicates learning and memory necessitates the that coupling of intrinsic and synaptic plasticity (Turrigiano, 2011; Greenhill et al, 2015; Wu et al, 2021).…”

Inactivity and Ca2+ signaling regulate synaptic compensation in motoneurons following hibernation in American bullfrogs

“…Given that the entire mEPSC amplitude distribution is thought to be regulated by a multiplicative scaling factor during inactivity 24 , we tested whether the increase in postsynaptic strength in each of our experimental conditions increases according to a multiplicative scaling rule. We used the rank-order method to test for synaptic scaling of mESPC amplitudes in populations of neurons 4,8,31 . To do this, mEPSCs from control and treatment groups (TTX, hibernation, hibernation+TTX, hibernation+nimodipine) were rank-ordered from the smallest to largest amplitude, plotted against each other (ranked control vs. ranked treatment), and fit with a linear regression (Supplemental Fig.…”