Purely STDP-based assembly dynamics: stability, learning, overlaps, drift and aging

Abstract

Memories may be encoded in the brain via strongly interconnected groups of neurons, called assemblies. The concept of Hebbian plasticity suggests that these assemblies are generated through synaptic plasticity, strengthening the recurrent connections within select groups of neurons that receive correlated stimulation. To remain stable in absence of such stimulation, the assemblies need to be self-reinforcing under the plasticity rule. Previous models of such assembly maintenance require additional mechanisms of fast homeostatic plasticity often with biologically implausible timescales. Here we provide a model of neuronal assembly generation and maintenance purely based on spike-timing-dependent plasticity (STDP) between excitatory neurons. It uses irregularly and stochastically spiking neurons and STDP that depresses connections of uncorrelated neurons. We find that assemblies do not grow beyond a certain size, because temporally imprecise spike correlations dominate the plasticity in large assemblies. Assemblies in the model can be learned or spontaneously emerge. The model allows for prominent, stable overlap structures between static assemblies. Further, assemblies can drift, particularly according to a novel, transient overlap-based mechanism. Finally the model indicates that assemblies grow in the aging brain, where connectivity decreases.

Formation and computational implications of assemblies in neural circuits

“…Another way to promote bidirectional connections is using a symmetric STDP rule which has dominant depression (Manz & Memmesheimer, 2022) or without any LTD (Ravid Tannenbaum & Burak, 2016). In this setting, synaptic weights grow linearly with the pre‐ and postsynaptic rates, and further increase bidirectionally when neurons fire close in time, regardless of the firing order.…”

“…Experiments in the sensory deprived zebrafish larvae have shown that assemblies can also form without external input, only due to network‐intrinsic activity (Pietri et al., 2017). Such assembly formation without structured input has been obtained in computational models with different forms of STDP based on the two frameworks discussed above: either mainly driven by the rate contribution (zero‐order motif) (Babadi & Abbott, 2013; Burkitt et al., 2007; Gilson et al., 2009b; Ocker et al., 2015; Ocker & Doiron, 2019), or by higher‐order motifs arising from internal correlation structure with the rate contribution minimised (Montangie et al., 2020; Ocker et al., 2015; Ocker & Doiron, 2019; Ravid Tannenbaum & Burak, 2016), or a combination thereof (Manz & Memmesheimer, 2022). When network‐intrinsic correlations contribute significantly to assembly formation, symmetry breaking in the network develops either due to random fluctuations in an otherwise symmetric network, or due to an initial bias in the connectivity matrix (Ocker & Doiron, 2019; Triplett et al., 2018).…”

“…In this example, synaptic competition follows directly from the plasticity rule in combination with the symmetry breaking of the training protocol with different input firing rates. A pairwise symmetric STDP rule dominated by depression can also induce competition without such structured external input (Manz & Memmesheimer, 2022). Here, the relative contributions of the zero‐ and higher‐order motifs regulate assembly size.…”

“…Experimental work suggests that assemblies may change over time, known as ‘representational drift’ (Rule et al., 2019). In computational models, ongoing synaptic plasticity after assembly formation results in single neurons dropping in and out of assemblies, leading to a drift of the assembly structure (Kossio et al., 2021; Manz & Memmesheimer, 2022; Raman & O'Leary, 2021; Triplett et al., 2018; Fig. 5).…”

Drift of neural ensembles driven by slow fluctuations of intrinsic excitability

“…However, the mechanisms underlying the emergence of drift and its relevance for the neural computation are not known. Drift is often thought to arise from variability of internal states [2], neurogenesis [1,9] or synaptic turnover [10] combined with noise [11,12]. On the other hand, excitability might also play a role in memory allocation [13][14][15][16], so that neurons having high excitability are preferentially allocated to memory ensembles [16][17][18].…”