Working Memory: From Neural Activity to the Sentient Mind

Jaffe, Russell J.; Constantinidis, Christos

doi:10.1002/cphy.c210005

Cited by 17 publications

(12 citation statements)

References 536 publications

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“…The rapid firing of these neurons after the TMS pulse, which is followed by a refractory period, disrupts the cortical network, resetting the stimulated region (Murphy et al, 2016; Pashut et al, 2014). Our results could therefore also be speculated to be explained by a subthreshold attractor model (Christophel et al, 2018; Jaffe and Constantinidis, 2021). The subthreshold attractor model suggests that WM-related persistent activity tends to be attracted to a bump state that emerges in varying locations across this network (Itskov et al, 2011; Wimmer et al, 2014).…”

Section: Discussionmentioning

confidence: 64%

Decoding auditory working memory content from EEG aftereffects of auditory-cortical TMS

Uluç,

Daneshzand,

Jas

et al. 2024

Preprint

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Working memory (WM), short term maintenance of information for goal directed behavior, is essential to human cognition. Identifying the neural mechanisms supporting WM is a focal point of neuroscientific research. One prominent theory hypothesizes that WM content is carried in a dynamic fashion, involving an activity-silent brain state based on synaptic facilitation. Information carried in such activity-silent brain states could be decodable from content-specific changes in responses to unrelated "impulse stimuli". A potential method for such impulses is single-pulse transcranial magnetic stimulation (TMS) with its focal, precise nature. Here, we tested the activity-silent model by combining TMS/EEG and multivariate pattern analysis (MVPA) with a non-conceptual auditory WM task that employed parametric ripple sound stimuli and a retro-cue design. Our MVPA employed between-subject cross-validation and robust non-parametric permutation testing. The decoding accuracy of WM content significantly increased after a single pulse TMS was delivered to the posterior superior temporal cortex during WM maintenance. Our results are compatible with the theory that WM maintenance involves brain states which are effectively "activity-silent" relative to other intrinsic processes visible in the EEG signal. Single-pulse TMS combined with MVPA could provide a powerful way to decode information content of activity-silent brain states involved in WM.

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Section: Discussionmentioning

confidence: 64%

Decoding auditory working memory content from EEG aftereffects of auditory-cortical TMS

Uluç,

Daneshzand,

Jas

et al. 2024

Preprint

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“…The PPC and dlPFC are part of a broader network that processes visuospatial information and spatial working memory ( Jaffe and Constantinidis, 2021 ). Tested with the ODR task, virtually identical proportions of neurons exhibiting working memory responses are found in posterior parietal and dorsolateral prefrontal areas ( Chafee and Goldman-Rakic, 1998 ).…”

Section: Discussionmentioning

confidence: 99%

More Prominent Nonlinear Mixed Selectivity in the Dorsolateral Prefrontal than Posterior Parietal Cortex

Dang

et al. 2022

eNeuro

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Neurons in the dorsolateral prefrontal cortex (dlPFC) and posterior parietal cortex (PPC) are activated by different cognitive tasks and respond differently to the same stimuli depending on task. The conjunctive representations of multiple tasks in nonlinear fashion in single neuron activity, is known as nonlinear mixed selectivity (NMS). Here, we compared NMS in a working memory task in areas 8a and 46 of the dlPFC and 7a and lateral intraparietal cortex (LIP) of the PPC in macaque monkeys. NMS neurons were more frequent in dlPFC than in PPC and this was attributed to more cells gaining selectivity in the course of a trial. Additionally, in our task, the subjects’ behavioral performance improved within a behavioral session as they learned the session-specific statistics of the task. The magnitude of NMS in the dlPFC also increased as a function of time within a single session. On the other hand, we observed minimal rotation of population responses and no appreciable differences in NMS between correct and error trials in either area. Our results provide direct evidence demonstrating a specialization in NMS between dlPFC and PPC and reveal mechanisms of neural selectivity in areas recruited in working memory tasks.

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“…We additionally relied on a small network of 256 units for these simulations, which greatly underestimates the complexity of the prefrontal cortex. Millions of neurons make up the real network, which is additionally organized in several subregions with distinct properties and capacity for plasticity (Riley et al, 2017(Riley et al, , 2018, and encompasses areas beyond the prefrontal cortex (Jaffe and Constantinidis, 2021). Responses across trials were also highly stereotypical and lacked the variability present in prefrontal discharges.…”

Section: Model Limitationsmentioning

confidence: 99%

Neural Mechanisms of Working Memory Accuracy Revealed by Recurrent Neural Networks

Xie

Liu

Constantinidis

et al. 2022

Front. Syst. Neurosci.

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Understanding the neural mechanisms of working memory has been a long-standing Neuroscience goal. Bump attractor models have been used to simulate persistent activity generated in the prefrontal cortex during working memory tasks and to study the relationship between activity and behavior. How realistic the assumptions of these models are has been a matter of debate. Here, we relied on an alternative strategy to gain insights into the computational principles behind the generation of persistent activity and on whether current models capture some universal computational principles. We trained Recurrent Neural Networks (RNNs) to perform spatial working memory tasks and examined what aspects of RNN activity accounted for working memory performance. Furthermore, we compared activity in fully trained networks and immature networks, achieving only imperfect performance. We thus examined the relationship between the trial-to-trial variability of responses simulated by the network and different aspects of unit activity as a way of identifying the critical parameters of memory maintenance. Properties that spontaneously emerged in the artificial network strongly resembled persistent activity of prefrontal neurons. Most importantly, these included drift of network activity during the course of a trial that was causal to the behavior of the network. As a consequence, delay period firing rate and behavior were positively correlated, in strong analogy to experimental results from the prefrontal cortex. These findings reveal that delay period activity is computationally efficient in maintaining working memory, as evidenced by unbiased optimization of parameters in artificial neural networks, oblivious to the properties of prefrontal neurons.

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Working Memory: From Neural Activity to the Sentient Mind

Cited by 17 publications

References 536 publications

Decoding auditory working memory content from EEG aftereffects of auditory-cortical TMS

Decoding auditory working memory content from EEG aftereffects of auditory-cortical TMS

More Prominent Nonlinear Mixed Selectivity in the Dorsolateral Prefrontal than Posterior Parietal Cortex

Neural Mechanisms of Working Memory Accuracy Revealed by Recurrent Neural Networks

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