Hierarchical dynamics of informational patterns and decision-making

Varona, Pablo; Rabinovich, M. I.

doi:10.1098/rspb.2016.0475

Cited by 18 publications

(18 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Intriguingly, the weak correlations observed (Fig 1B) are also suggestive of a relatively irregular, near-asynchronous dynamics, typically associated with a wide dynamical repertoire (e.g., [65,77]). In a more conjectural vein, the successful processing of the task by small lOFC ensembles could map to long-lasting metastable states over tens of trials [78], which gain stability when the choice is deterministic and behaviorally relevant, and destabilize otherwise. Such a metastable portrait might subserve the enhancement of detailed coding of task-epoch variables that enable the animal to effectively predict the upcoming choice.…”

Section: Neuronal Correlations and State-space Decodingmentioning

confidence: 99%

Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions

et al. 2020

View full text Add to dashboard Cite

Shared neuronal variability has been shown to modulate cognitive processing. However, the relationship between shared variability and behavioral performance is heterogeneous and complex in frontal areas such as the orbitofrontal cortex (OFC). Mounting evidence shows that single-units in OFC encode a detailed cognitive map of task-space events, but the existence of a robust neuronal ensemble coding for the predictability of choice outcome is less established. Here, we hypothesize that the coding of foreseeable outcomes is potentially unclear from the analysis of units activity and their pairwise correlations. However, this code might be established more conclusively when higher-order neuronal interactions are mapped to the choice outcome. As a case study, we investigated the trial-to-trial shared variability of neuronal ensemble activity during a two-choice interval-discrimination task in rodent OFC, specifically designed such that a lose-switch strategy is optimal by repeating the rewarded stimulus in the upcoming trial. Results show that correlations among triplets are higher during correct choices with respect to incorrect ones, and that this is sustained during the entire trial. This effect is not observed for pairwise nor for higher than third-order correlations. This scenario is compatible with constellations of up to three interacting units assembled during trials in which the task is performed correctly. More interestingly, a state-space spanned by such constellations shows that only correct outcome states that can be successfully predicted are robust over 100 trials of the task, and thus they can be accurately decoded. However, both incorrect and unpredictable outcome representations were unstable and thus non-decodeable, due to spurious negative correlations. Our results suggest that predictability of successful outcomes, and hence the optimal behavioral strategy, can be mapped out in OFC ensemble states reliable over trials of the task, and revealed by sufficiency complex neuronal interactions.

show abstract

Section: Neuronal Correlations and State-space Decodingmentioning

confidence: 99%

Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This work provides an addition to recent work on learning of chunking sequences (Fonollosa et al, 2015) including specific roles in cognitive process (Varona and Rabinovich, 2016). Specifically, this work provides a useful hardware validation means for many advanced theory researches.…”

Section: Discussionmentioning

confidence: 89%

Hierarchical Chunking of Sequential Memory on Neuromorphic Architecture with Reduced Synaptic Plasticity

Deng

Wang

et al. 2016

Front. Comput. Neurosci.

View full text Add to dashboard Cite

Chunking refers to a phenomenon whereby individuals group items together when performing a memory task to improve the performance of sequential memory. In this work, we build a bio-plausible hierarchical chunking of sequential memory (HCSM) model to explain why such improvement happens. We address this issue by linking hierarchical chunking with synaptic plasticity and neuromorphic engineering. We uncover that a chunking mechanism reduces the requirements of synaptic plasticity since it allows applying synapses with narrow dynamic range and low precision to perform a memory task. We validate a hardware version of the model through simulation, based on measured memristor behavior with narrow dynamic range in neuromorphic circuits, which reveals how chunking works and what role it plays in encoding sequential memory. Our work deepens the understanding of sequential memory and enables incorporating it for the investigation of the brain-inspired computing on neuromorphic architecture.

show abstract

“…Cognitive information processing must contain a mechanism of binding between different information modalities in the brain. Here we consider heteroclinic binding of sequences (Rabinovich et al, 2010a ; Varona and Rabinovich, 2016 ). The basic model (1)–(2) that we propose is able to explain the origin of the temporal coordination of competitive dynamics of active brain modes representing the processing of different cognitive modalities and resources in parallel.…”

Section: Sequential Memory and Brain Oscillations Temporal Entrainmementioning

confidence: 99%

Discrete Sequential Information Coding: Heteroclinic Cognitive Dynamics

Rabinovich

Varona

2018

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

Discrete sequential information coding is a key mechanism that transforms complex cognitive brain activity into a low-dimensional dynamical process based on the sequential switching among finite numbers of patterns. The storage size of the corresponding process is large because of the permutation capacity as a function of control signals in ensembles of these patterns. Extracting low-dimensional functional dynamics from multiple large-scale neural populations is a central problem both in neuro- and cognitive- sciences. Experimental results in the last decade represent a solid base for the creation of low-dimensional models of different cognitive functions and allow moving toward a dynamical theory of consciousness. We discuss here a methodology to build simple kinetic equations that can be the mathematical skeleton of this theory. Models of the corresponding discrete information processing can be designed using the following dynamical principles: (i) clusterization of the neural activity in space and time and formation of information patterns; (ii) robustness of the sequential dynamics based on heteroclinic chains of metastable clusters; and (iii) sensitivity of such sequential dynamics to intrinsic and external informational signals. We analyze sequential discrete coding based on winnerless competition low-frequency dynamics. Under such dynamics, entrainment, and heteroclinic coordination leads to a large variety of coding regimes that are invariant in time.

show abstract

Hierarchical dynamics of informational patterns and decision-making

Cited by 18 publications

References 69 publications

Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions

Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions

Hierarchical Chunking of Sequential Memory on Neuromorphic Architecture with Reduced Synaptic Plasticity

Discrete Sequential Information Coding: Heteroclinic Cognitive Dynamics

Contact Info

Product

Resources

About