Hierarchical process memory: memory as an integral component of information processing

By Uri Hasson, Janice Chen, Christopher J. Honey. Trends in Cognitive Sciences, 2015

666 citations recorded by the source corpus.

Models of working memory commonly focus on how information is encoded into and retrieved from storage at specific moments. However, in the majority of real-life processes, past information is used continuously to process incoming information across multiple timescales. Considering single unit, electrocorticography, and functional imaging data, we argue that (i) virtually all cortical circuits can accumulate information over time, and (ii) the timescales of accumulation vary hierarchically, from early sensory areas with short processing timescales (tens to hundreds of milliseconds) to higher-order areas with long processing timescales (many seconds to minutes). In this hierarchical systems perspective, memory is not restricted to a few localized stores, but is intrinsic to information processing that unfolds throughout the brain on multiple timescales. “The present contains nothing more than the past, and what is found in the effect was already in the cause.”Henri L Bergson

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/1935827b73f2bcad1134fb9464d90a63e55ffdf7

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In Cognitive Neuroscience

Citations in the corpus

Switching between internal and external modes: A multiscale learning principle (2017, background): “Within the neocortex, the timescale of ﬂuctuation slows as one moves up the processing hierarchy (Hasson, Chen, & Honey, 2015; Honey et al., 2012).”
The Distributed Nature of Working Memory. (2017, background): “More recent accounts of distributed working memory have emphasized different timescales of retention in different brain areas [140,141], the role of attentional processes in the retention of memories [4,11,142], and the utility of synaptic plasticity and dynamic firing trajectories for working memor”
The theory of constructed emotion: an active inference account of interoception and categorization (2017, neutral): “…that are responsible for implementing allostasis, and that are usually assigned an emotional function, are ‘driving’ the perception signals, i.e. the ‘concepts’, that constitute the brain’s internal model, in conjunction with the hippocampus (e.g. see Davachi and DuBrow, 2015; Hasson et al., 2015).”
Shared computational principles for language processing in humans and deep language models (2022, background): “In natural comprehension (for example, listening to or reading a story), predictions for upcoming words are influenced by information accumulated over multiple timescales: from the most recent words to the information gathered over multiple paragraphs 37 .”
Complementary learning systems within the hippocampus: A neural network modeling approach to reconciling episodic memory with statistical learning (2016, neutral): “15 One intriguing possibility is that there is a hierarchy of learning rates, perhaps related to a hierarchy of temporal window sizes for information accumulation (82), where the TSP is fastest, followed by the MSP, then MTL cortex, then cortical areas like the anterior temporal lobe that integrate ”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “The constellation of densely connected hub regions – the rich club – appear to support a slow, stable dynamic “core” whereas peripheral sensory regions introduce (rapid) stimuli-related variability in the system (Bassett et al., 2013; Gollo et al., 2015; Hasson et al., 2015).”
Long short-term memory and Learning-to-learn in networks of spiking neurons (2018, methodology): “Since neurons in the brain are equipped with a host of dynamics processes on time scales larger than a few dozen ms [5], we enriched the inherent dynamics of neurons in our model by a standard neural adaptation process.”
Towards mental time travel: a hierarchical memory for reinforcement learning agents (2021, background): “Human learning and generalization relies on our detailed memory of the past [54, 51, 16, 29, 42].”
The hippocampus and related neocortical structures in memory transformation. (2018, background): “We know little about the transformation of temporal and sequential information with time and experience [180 – 182].”
Mirroring and beyond: coupled dynamics as a generalized framework for modelling social interactions (2016, background): “In parallel with these increasing levels of abstraction, there is a hierarchy of timescales [64], with the more abstract levels of representation being associated with longer integration time windows [62,65].”
Are episodic memories special? On the sameness of remembered and imagined event simulation (2018, methodology): “The second perspective comes from two related theories of how the continuous stream of experience is processed in the present moment: event segmentation theory (Zacks et al. 2007) and the hierarchical process memory framework (Hasson et al. 2015; Chen et al. 2016; Baldassano et al. 2017).”
Neural mechanisms of time-forward predictions for naturalistic auditory tone sequences (2026, unknown): “Previous studies have revealed a hierarchy of time scales across the cortex, with progressively longer time scales for stimulus processing as one ascends the cortical hierarchy 26 – 28 .”