Metastable Attractors Explain the Variable Timing of Stable Behavioral Action Sequences

Recanatesi, Stefano; Pereira, Ulises; Murakami, Masayoshi; Mainen, Zachary F.; Mazzucato, Luca

doi:10.2139/ssrn.3869115

Cited by 15 publications

(39 citation statements)

References 105 publications

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“…Parameters were estimated via the EM algorithm. In each session, M was a free parameter estimated via 20-fold crossvalidation 63,64 . For a set value of M, spanning a range from 2 to 30, the test data loglikelihood (LL) was averaged across the 20 "test" folds and expressed as a deviance (−2 × LL).…”

Section: Methodsmentioning

confidence: 99%

Inhibition allocates spikes during hippocampal ripples

et al. 2022

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Sets of spikes emitted sequentially across neurons constitute fundamental pulse packets in neural information processing, including offline memory replay during hippocampal sharp-wave ripples (SWRs). The relative timing of neuronal spikes is fine-tuned in each spike sequence but can vary between different sequences. However, the microcircuitry mechanism that enables such flexible spike sequencing remains unexplored. We recorded the membrane potentials of multiple hippocampal CA1 pyramidal cells in mice and found that the neurons were transiently hyperpolarized prior to SWRs. The pre-SWR hyperpolarizations were spatiotemporally heterogeneous, and larger hyperpolarizations were associated with later spikes during SWRs. Intracellular blockade of Cl−-mediated inhibition reduced pre-SWR hyperpolarizations and advanced spike times. Single-unit recordings also revealed that the pre-SWR firing rates of inhibitory interneurons predicted the SWR-relevant spike times of pyramidal cells. Thus, pre-SWR inhibitory activity determines the sequential spike times of pyramidal cells and diversifies the repertoire of sequence patterns.

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Section: Methodsmentioning

confidence: 99%

Inhibition allocates spikes during hippocampal ripples

et al. 2022

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“…An event or item sequence is mapped to a path through a network of cognitive states, whereupon the states’ neural representations are summed into a path vector from which the sequence can later be retrieved. Networks of hidden states have found success in accounting for animal behavior and neural activity (36, 37) and human free recall (6, 38, 39); however, it is unknown how sequences of states are stored (e.g. to repeat back a series of stimuli or actions), possibly retained in a unique compact representation, and retrieved.…”

Section: Discussionmentioning

confidence: 99%

Path vectors: a neural code for sequential memory

Pang

Recanatesi

2022

Preprint

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While recalling lists of unrelated items is highly challenging we can recall much longer sequences structured as an episode or story. It is unknown why these patterns have such a striking influence on memory. We introduce a model where the experience of an episode or story is represented as a path through a pre-existing network of cognitive states. We demonstrate that by summing the neural representations of the visited states, this path can be transformed into a simple neural code: a path vector. We show how, by leveraging sparse connectivity and high dimensionality, path vectors provide robust codes for a large set of sequences and can be decoded mechanistically for memory retrieval. Fitting our model to data reveals how human free and serial recall may emerge from adapting coding mechanisms tuned for sequences aligned with existing network paths. We thus posit that sequences such as episodes or stories map more directly to existing cognitive network paths than arbitrary lists, with the latter eliciting paths that tend to interfere and impair recall. Our model suggests that mnemonic strategies like imposing narrative structure on a list act to improve recall by reducing such path interference. This work illuminates a simple bioplausible means for flexibly recruiting existing cognitive structures to encode new memories.

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“…This suggests that either different patterns of motor network activation are occurring on each trial, leading to missed classifications, or that only some of the population of neurons sampled by each sEEG electrode contact are involved in movement. Previous work has shown that, at a cellular level, stable sequences of activity are observed during simple motor tasks (Recanatesi et al, 2022). Thus, the more likely explanation is that a large portion of the population of neurons recorded by an sEEG channel are not involved in movement, causing classifications to fail on trials where the activity of movement-unrelated neural circuits is more prominent.…”

Section: Discussionmentioning

confidence: 99%

Classification of Movement-Related Oscillations in sEEG Recordings with Machine Learning

Rockhill

Mantovani

Stedelin

et al. 2022

Preprint

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Previous electrophysiological research has characterized canonical oscillatory patterns associated with movement mostly from recordings of primary sensorimotor cortex. Less work has attempted to decode movement based on electrophysiological recordings from a broader array of brain areas such as those sampled by stereoelectroencephalography (sEEG). Here we decoded movement using a linear support vector machine (SVM). We were able to accurately classify sEEG spectrograms during a keypress movement in a task versus those during the inter-trial interval. Furthermore, the important time-frequency patterns for this classification recapitulated findings from previous studies that used non-invasive electroencephalography (EEG) and electrocorticography (ECoG) and identified brain regions that were not associated with movement in previous studies. Specifically, we found these previously described patterns: beta (13 - 30 Hz) desynchronization, beta synchronization (rebound), pre-movement alpha (8 - 15 Hz) modulation, a post-movement broadband gamma (60 - 90 Hz) increase and an event-related potential. These oscillatory patterns were newly observed in a wide range of brain areas accessible with sEEG that are not accessible with other electrophysiology recording methods. For example, the presence of beta desynchronization in the frontal lobe was more widespread than previously described, extending outside primary and secondary motor cortices. We provide evidence for a system of putative motor networks that exhibit unique oscillatory patterns by describing the anatomical extent of the movement-related oscillations that were observed most frequently across all sEEG contacts.

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Metastable Attractors Explain the Variable Timing of Stable Behavioral Action Sequences

Cited by 15 publications

References 105 publications

Inhibition allocates spikes during hippocampal ripples

Inhibition allocates spikes during hippocampal ripples

Path vectors: a neural code for sequential memory

Classification of Movement-Related Oscillations in sEEG Recordings with Machine Learning

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