Long- and short-term history effects in a spiking network model of statistical learning

Maes, Amadeus; Barahona, Mauricio; Clopath, Claudia

doi:10.1038/s41598-023-39108-3

Cited by 5 publications

(1 citation statement)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More studies are needed to fully verify the extent to which the statistical structure of the stimuli affects the performance. Finally, we note that in our model the stimulus distribution is not explicitly learned (but see Maes et al, 2023 ): instead, the PPC dynamics follows the input, and its marginal distribution of activity is similar to that of the external input. This is in agreement with Hachen et al, 2021 , where the authors used different stimulus ranges across different sessions and noted that rats initiated each session without any residual influence of the previous session’s range/boundary on the current session, ruling out long-term learning of the input structure.…”

Section: Discussionmentioning

confidence: 99%

Unifying network model links recency and central tendency biases in working memory

Boboeva,

Pezzotta,

Clopath

et al. 2024

eLife

Self Cite

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 appears to be biased towards the average of past observations. It is assumed to be an optimal strategy by the brain, and commonly thought of as an expression of the brain’s ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed, and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats, suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience – producing short-term sensory history biases – naturally leads to contraction bias. The errors, occurring 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 sensory distribution’s mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

show abstract

Section: Discussionmentioning

confidence: 99%

Unifying network model links recency and central tendency biases in working memory

Boboeva,

Pezzotta,

Clopath

et al. 2024

eLife

Self Cite

View full text Add to dashboard Cite

show abstract

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

Boboeva

Pezzotta

Akrami

et al. 2022

Preprint

Self Cite

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

Simplest Model of Nervous System. I. Formalism

Sinitskiy

2023

Preprint

View full text Add to dashboard Cite

This study presents a novel, highly simplified model of the nervous system, inspired by one hypothetical scenario of its origin. The model is designed to accommodate both mathematical derivations and numerical simulations, offering a template for studying generalized principles and dynamics beyond the specifics of the referenced origin scenario. The model offers a holistic perspective by treating the nervous system and the environment (in their simplest forms) as parts of one system and, together with a companion paper, notes the key role of evolutionary factors (in this model, predator evasion) in shaping the properties of the nervous system. To emphasize these fundamental principles, some aspects, such as the highly dimensional nature of the networks or detailed molecular mechanisms of their functioning, are omitted in the current version. Analytically, the model facilitates insights into the stationary distribution as a solution to the Fokker-Planck equation and the corresponding effective potential and rotation (solenoidal) terms. Numerically, it generates biologically plausible (given its high abstraction) solutions and supports comprehensive sampling with limited computational resources. Noteworthy findings from the study include limitations of the commonly used weak noise approximation and the significance of rigorous mathematical analysis over heuristic interpretations of the potential. We hope that this abstract model will serve as a fruitful tool for better understanding a complete set of principles for modeling nervous systems.

show abstract

Long- and short-term history effects in a spiking network model of statistical learning

Cited by 5 publications

References 73 publications

Unifying network model links recency and central tendency biases in working memory

Unifying network model links recency and central tendency biases in working memory

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

Simplest Model of Nervous System. I. Formalism

Contact Info

Product

Resources

About