The capacity for correlated semantic memories in the cortex

Boboeva, Vezha; Brasselet, Romain; Treves, Alessandro

doi:10.1101/321703

Cited by 9 publications

(10 citation statements)

References 51 publications

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“…Our study is beyond the scope of multi-trial free recall, but the mechanisms at play in our model can be reasonably expected to be consistent with this set of findings. In this spirit, a most intriguing puzzle that remains to be solved is how the rich structure of semantic associations in human memory arises partially as a result of the repeated exposure to items in temporal proximity [73][74][75][76].…”

Section: Discussionmentioning

confidence: 99%

Free recall scaling laws and short-term memory effects in a latching attractor network

Boboeva

Pezzotta

Clopath

2020

Preprint

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Despite the complexity of human memory, paradigms like free recall have revealed robust qualitative and quantitative characteristics, such as power laws governing recall capacity. Although abstract random matrix models could explain such laws, the possibility of their implementation in large networks of interacting neurons has so far remained unexplored. We study an attractor network model of long-term memory endowed with firing rate adaptation and global inhibition. Under appropriate conditions, the transitioning behaviour of the network from memory to memory is constrained by limit cycles that prevent the network from recalling all memories, with scaling similar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing the standard autoassociative learning rule, as well as short-term synaptic facilitation, our model reproduces other key findings in the free recall literature, namely serial position effects, contiguity and forward asymmetry effects, as well as the semantic effects found to guide memory recall. The model is consistent with a broad series of manipulations aimed at gaining a better understanding of the variables that affect recall, such as the role of rehearsal, presentation rates and (continuous/end-of-list) distractor conditions. We predict that recall capacity may be increased with the addition of small amounts of noise, for example in the form of weak random stimuli during recall. Moreover, we predict that although the statistics of the encoded memories has a strong effect on the recall capacity, the power laws governing recall capacity may still be expected to hold.

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Section: Discussionmentioning

confidence: 99%

Free recall scaling laws and short-term memory effects in a latching attractor network

Boboeva

Pezzotta

Clopath

2020

Preprint

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“…The use of the presented model to describe memory schemata will require further steps, such as an account of the interaction between hippocampus and neocortex, and a mechanism for the transition between different dynamical memories. Nevertheless, the idea of dynamic retrieval of a continuous manifold and the integration of the model presented here with effective models of cortical memory networks [49] open promising perspectives.…”

Section: Discussionmentioning

confidence: 99%

Continuous attractors for dynamic memories

Spalla

Cornacchia

Treves

2020

Preprint

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Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, that spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent on the details of its implementation. By calculating the storage capacity we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes.

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“…The average number of pre-synaptic units for a given unit is c ij = c m . We use randomly-correlated memory patterns {ξ µ i } µ=1,...,p in this work but one can consider a set of correlated memory patterns, as produced by the algorithm presented in [33]. Sparsity of patterns (fraction of active units) is set by the parameter a.…”

Section: Potts Neural Networkmentioning

confidence: 99%

Latching dynamics as a basis for short-term recall

Ryom

Boboeva

Soldatkina

et al. 2021

Preprint

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We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state -- latching dynamics. We show that in one such model, and in simple spatial memory tasks we have given to human subjects, short-term memory can be limited to similar low capacity by interference effects, in tasks terminated by errors, and can exhibit similar sublinear scaling, when errors are overlooked. The same mechanism can drive serial recall if combined with weak order-encoding plasticity. Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.

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The capacity for correlated semantic memories in the cortex

Cited by 9 publications

References 51 publications

Free recall scaling laws and short-term memory effects in a latching attractor network

Free recall scaling laws and short-term memory effects in a latching attractor network

Continuous attractors for dynamic memories

Latching dynamics as a basis for short-term recall

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