Predicting distributed working memory activity in a large-scale mouse brain: the importance of the cell type-specific connectome

Ding, Xingyu; Jaramillo, Jorge; Jiang, Junjie; Wang, Xiaojing

doi:10.1101/2022.12.05.519094

Cited by 7 publications

(18 citation statements)

References 131 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This suggests that the linearity of hierarchical interactions between human brain areas eliminates those restrictions for the human brain, providing additional flexibility in the way top-down projections might modulate perception in the case of humans. Differences with the case of mice are even more salient: while current efforts identify gradients of inhibitory neuron density as a fundamental factor for the distributed patterns 33 , our model suggests that variations in NMDAr densities are enough to explain the evidence in humans. More detailed models will allow for a deep computational study of the differences in working memory mechanisms across these and other species.…”

Section: Discussionmentioning

confidence: 75%

“…On the other hand, computational modeling work has explored the idea of distributed working memory in macaques across various levels of detail along recent years 23,[48][49][50] , with the first complete computational study of distributed working memory in a data-constrained macaque network recently proposing the core mechanistic principles of the phenomenon 32 . While similar efforts have been carried out for the case of mice 33 , the case of distributed working memory in humans had not been tackled until now.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, studies have shown that different working memory tasks activate different brain regions, suggesting that there is not a single "working memory area" in the brain [29][30][31] . Despite all available evidence, particularly for the human brain, computational works have only recently proposed potential mechanisms for the emergence of distributed working memory for the case of NHPs 32 and rodents 33 ; the question remains unexplored for the human brain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Emergence of distributed working memory in a human brain network model

Feng

Bandyopadhyay

Mejías

2023

Preprint

View full text Add to dashboard Cite

Working memory is a fundamental cognitive function which allows to transiently store and manipulate relevant information in memory. While it has been traditionally linked to activity in specific prefrontal cortical areas, recent electrophysiological and imaging evidence has shown co-occurrent activities in different brain regions during working memory. To dissect the mechanisms behind the emergence of such distributed working memory activity in the human brain, we built and analyzed a detailed, data-constrained model of the human brain combining multiple sources of large-scale brain data. The model reveals the emergence of distributed working memory patterns which rely on long-range synaptic projections, differing from classical models where working memory emerges due to local recurrent connectivity. When compared to existing evidence regarding areas linked to working memory, our model explains almost 80% of the observed variability across brain areas. Our results predict important differences between the distributed working memory in human and non-human primates, and propose a potential solution for the debate regarding the role of early visual areas in working memory encoding. This work provides a novel approach to simulate the human brain which combines the richness of large-scale brain dynamics with the insight of mechanistic models of cognitive functions.

show abstract

Section: Discussionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Emergence of distributed working memory in a human brain network model

Feng

Bandyopadhyay

Mejías

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…1 D). Building on experimental findings [30][31][32][33][34][35], we designed the PPC network such that it integrates activity over a longer timescale compared to the WM network (Sect. 3.1).…”

Section: The Ppc As a Slower Integrator Networkmentioning

confidence: 99%

From recency to central tendency biases in working memory: a unifying network model

Boboeva

Pezzotta

Akrami

et al. 2022

Preprint

View full text Add to dashboard Cite

The central tendency bias, or contraction bias is a phenomenon where the judgment of the magnitude of items held in working memory is biased towards the average of past observations. This phenomenon has been first described more than a century ago [1] and since then, has been replicated in various decision making tasks in humans [2–10], and rodents [11, 12]. Contraction bias is assumed to be an optimal strategy by the brain, given the noisy nature of working memory. From a Bayesian perspective [7], the progressive shift of the noisy memory towards the mean of a prior distribution built from past sensory experience helps with more accurate estimates of the memory. In this work, we propose an alternative account, via short-term history biases (serial dependence) [12–15]. Our model is motivated and inspired by recent results from an auditory delayed-discrimination task in rats, where the posterior parietal cortex (PPC) has been shown to be critical to these memory effects [12]. The dynamics of our model suggests that contraction bias can emerge as a result of a volatile working memory content which makes it susceptible to shifting to the past sensory experience. The errors, at the level of individual trials, are sampled from the full distribution of the stimuli, and are not due to a gradual shift of the memory towards the distribution’s mean. Our model explains both short-term history biases, as well as contraction bias towards the sensory mean for the averaged performance. The results are consistent with the role of the PPC in encoding such sensory history biases, and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas.

show abstract

“…Previous quantifications of labeled neurons in the PV-cre mouse line led to the conclusion that association areas are under a lower inhibitory control because of the low density of PV-INs (Whissel et al, 2015;Kim et al, 2017;Ding et al, 2023). However, the total GABAergic population has not yet been quantified, and other GABAergic types might take on the role of PV-INs in association areas.…”

Section: Comparison Of the Gabaergic Population Of Primary Sensory An...mentioning

confidence: 99%

Association cortical areas in the mouse contain a large population of fast-spiking GABAergic neurons that do not express parvalbumin

Courcelles,

Kjelsberg,

Convertino

et al. 2023

Preprint

View full text Add to dashboard Cite

GABAergic neurons represent 10-15% of the neuronal population of the cortex but exert a powerful control over information flow in cortical circuits. The largest GABAergic class in the neocortex is represented by the parvalbumin-expressing (PV-INs) fast-spiking neurons, which provide powerful somatic inhibition to their postsynaptic targets. Recently, the density of PV-INs has been shown to be lower in associative areas of the mouse cortex as compared to sensory and motor areas. Modeling work based on these quantifications linked the low-density of PV-INs with specific computations of associative cortices. However, it is still unknown whether the total GABAergic population of association cortices is smaller or whether another GABAergic type can compensate for the low density of PV-INs. In the present study we investigated these hypotheses using a combination of neuroanatomy, mouse genetics and neurophysiology. We found that the GABAergic population of association areas is comparable to that of primary sensory areas, and it is enriched of fast-spiking neurons that do not express PV and were not accounted for by previous quantifications. We developed an intersectional viral strategy to demonstrate that the synaptic output of fast-spiking neurons is comparable across cortical regions. Our results provide quantifications of the density and output strength of fast-spiking GABAergic neurons and offers new biological constrains to refine current models of cortical computations.

show abstract

Predicting distributed working memory activity in a large-scale mouse brain: the importance of the cell type-specific connectome

Cited by 7 publications

References 131 publications

Emergence of distributed working memory in a human brain network model

Emergence of distributed working memory in a human brain network model

From recency to central tendency biases in working memory: a unifying network model

Association cortical areas in the mouse contain a large population of fast-spiking GABAergic neurons that do not express parvalbumin

Contact Info

Product

Resources

About