scholarly journals Inferred Model of the Prefrontal Cortex Activity Unveils Cell Assemblies and Memory Replay

2015 ◽  
Author(s):  
Gaia Tavoni ◽  
Ulisse Ferrari ◽  
Francesco Paolo Battaglia ◽  
Simona Cocco ◽  
Rémi Monasson

Cell assemblies are thought to be the units of information representation in the brain, yet their detection from experimental data is arduous. Here, we propose to infer effective coupling networks and model distributions for the activity of simultaneously recorded neurons in prefrontal cortex, during the performance of a decision-making task, and during preceding and following sleep epochs. Our approach, inspired from statistical physics, allows us to define putative cell assemblies as the groups of co-activated neurons in the models of the three recorded epochs. It reveals the existence of task-related changes of the effective couplings between the sleep epochs. The assemblies which strongly coactivate during the task epoch are found to replay during subsequent sleep, in correspondence to the changes of the inferred network. Across sessions, a variety of different network scenarios is observed, providing insight in cell assembly formation and replay.

1999 ◽  
Vol 22 (2) ◽  
pp. 284-284 ◽  
Author(s):  
Chris Code

Holistically ignited Hebbian models are fundamentally different from the serially organized connectionist implementations of language. This may be important for the recovery of language after injury, because connectionist models have provided useful insights into recovery of some cognitive functions. I ask whether cell assembly modelling can make an important contribution and whether the apparent incompatibility with successful connectionist modelling is a problem.


2021 ◽  
Author(s):  
Paul Gomez

In this research we explore in detail how a phenomenon called sustained persistent activity is achieved by circuits of interconnected neurons. Persistent activity is a phenomenon that has been extensively studied (Papoutsi et al. 2013; Kaminski et. al. 2017; McCormick et al. 2003; Rahman, and Berger, 2011). Persistent activity consists in neuron circuits whose spiking activity remains even after the initial stimuli are removed. Persistent activity has been found in the prefrontal cortex (PFC) and has been correlated to working memory and decision making (Clayton E. Curtis and Daeyeol Lee, 2010). We go beyond the explanation of how persistent activity happens and show how arrangements of those basic circuits encode and store data and are used to perform more elaborated tasks and computations. The purpose of the model we propose here is to describe the minimum number of neurons and their interconnections required to explain persistent activity and how this phenomenon is actually a fast storage mechanism required for implementing working memory, task processing and decision making.


2021 ◽  
Vol 15 ◽  
Author(s):  
Noriyuki Narita ◽  
Kazunobu Kamiya ◽  
Sunao Iwaki ◽  
Tomohiro Ishii ◽  
Hiroshi Endo ◽  
...  

BackgroundThe differences in the brain activities of the insular and the visual association cortices have been reported between oral and manual stereognosis. However, these results were not conclusive because of the inherent differences in the task performance-related motor sequence conditions. We hypothesized that the involvement of the prefrontal cortex may be different between finger and oral shape discrimination. This study was conducted to clarify temporal changes in prefrontal activities occurring in the processes of oral and finger tactual shape discrimination using prefrontal functional near-infrared spectroscopy (fNIRS).MethodsSix healthy right-handed males [aged 30.8 ± 8.2 years (mean ± SD)] were enrolled. Measurements of prefrontal activities were performed using a 22-channel fNIRS device (ETG-100, Hitachi Medical Co., Chiba, Japan) during experimental blocks that included resting state (REST), nonsense shape discrimination (SHAM), and shape discrimination (SHAPE).ResultsNo significant difference was presented with regard to the number of correct answers during trials between oral and finger SHAPE discrimination. Additionally, a statistical difference for the prefrontal fNIRS activity between oral and finger shape discrimination was noted in CH 1. Finger SHAPE, as compared with SHAM, presented a temporally shifting onset and burst in the prefrontal activities from the frontopolar area (FPA) to the orbitofrontal cortex (OFC). In contrast, oral SHAPE as compared with SHAM was shown to be temporally overlapped in the onset and burst of the prefrontal activities in the dorsolateral prefrontal cortex (DLPFC)/FPA/OFC.ConclusionThe prefrontal activities temporally shifting from the FPA to the OFC during SHAPE as compared with SHAM may suggest the segregated serial prefrontal processing from the manipulation of a target image to the decision making during the process of finger shape discrimination. In contrast, the temporally overlapped prefrontal activities of the DLPFC/FPA/OFC in the oral SHAPE block may suggest the parallel procession of the repetitive involvement of generation, manipulation, and decision making in order to form a reliable representation of target objects.


2021 ◽  
Author(s):  
Daniel B. Ehrlich ◽  
John D. Murray

Real-world tasks require coordination of working memory, decision making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. Our experiments revealed that human behavior is consistent with contingency representations, and not with traditional sensory models of working memory. In task-optimized recurrent neural networks we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from prefrontal cortex during working memory tasks. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision making.


Author(s):  
Salim Lahmiri

How diverse regions of the brain are coordinated to produce objective-directed decision is the essence of neuroeconomics. Indeed, the latter is a formal framework to describe the involvement of numerous brain regions including frontal, cingulate, parietal cortex, and striatum in economic and financial decision-making process. The purpose of this chapter is to explain the relationship between economic decision making and emotion on one hand, and the relationship between economic decision making and prefrontal cortex on the other hand.


Author(s):  
Xiao-Jing Wang

The prefrontal cortex (PFC) circuits are characterized by several distinct features. First, the input–output connections of a PFC circuit with the rest of the brain are extraordinarily extensive. In the primates, pyramidal neurons in PFC are greatly more spinous than in the primary sensory areas, so they have a much larger capacity for synaptic integration. Second, PFC areas are endowed with strong intrinsic recurrent connections that are sufficient to generate reverberatory activity underlying working memory and decision-making. Third, excitation and inhibition are balanced dynamically. Unlike early sensory cortical areas, in the frontal areas of both monkey and mouse, the synaptic inhibitory circuit is predominated by GABAergic cell subclasses that are dedicated to controlling inputs to, rather than outputs from, pyramidal neurons, likely reflecting the functional demand of selectively gating input pathways into the PFC in accordance with the behavioral context and goals.


2018 ◽  
Author(s):  
Martijn E. Wokke ◽  
Tony Ro

AbstractFrequent experience with regularities in our environment allows us to use predictive information to guide our decision process. However, contingencies in our environment are not always explicitly present and sometimes need to be inferred. Heretofore, it remained unknown how predictive information guides decision-making when explicit knowledge is absent and how the brain shapes such implicit inferences. In the present experiment, participants performed a discrimination task in which a target stimulus was preceded by a predictive cue. Critically, participants had no explicit knowledge that some of the cues signaled an upcoming target, allowing us to investigate how implicit inferences emerge and guide decision-making. Despite unawareness of the cue-target contingencies, participants were able to use implicit information to improve performance. Concurrent EEG recordings demonstrate that implicit inferences rely upon interactions between internally and externally oriented networks, whereby anterior prefrontal regions inhibit right parietal cortex under internal implicit control.SignificanceRegularities in our environment can guide our behavior providing information about upcoming events. Interestingly, such predictive information does not need to be explicitly represented in order to effectively guide our decision process. Here, we show how the brain engages in such real-world ‘data mining’ and how implicit inferences emerge. We employed a contingency cueing task and demonstrate that implicit inferences influenced responses to subsequent targets despite a lack of awareness of cue-target contingencies. Further, we show that these implicit inferences emerge through interactions between internally- and externally-oriented neural networks. The current results highlight the importance of the anterior prefrontal cortex in transforming external events into predictive internalized models of the world.


2020 ◽  
Author(s):  
Priyanka S. Mehta ◽  
Seng Bum Michael Yoo ◽  
Benjamin Y. Hayden

ABSTRACTBehavioral neuroscience almost exclusively studies behavior during tasks and ignores the unstructured inter-trial interval (ITI). However, it is unlikely that the ITI is simply an idling or paused mode; instead, it is a likely time for globally focused cognition, in which attention is disengaged from the task at hand and oriented more broadly. To gain insight into the computational underpinnings of globally focused cognition, we recorded from neurons in a core decision-making region, area 14 of ventromedial prefrontal cortex (vmPFC), as macaques performed a foraging search task with long inter-trial intervals (ITIs). We find that during the ITI, ensemble firing is associated with increased discriminability of a key mnemonic variable, recent reward rate, which in turn predicts upcoming search strategy. ITI activity is also associated with increased ensemble dimensionality and faster subspace reorganization, presumed markers of processing complexity. These results demonstrate the flexible nature of mnemonic processing and support the idea that the brain makes use of ostensible downtime to engage in complex processing.


2020 ◽  
Author(s):  
Ingrid Johnsen Haas ◽  
Clarisse Warren ◽  
Samantha J. Lauf

Recent research in political psychology and biopolitics has begun to incorporate theory and methods from cognitive neuroscience. The emerging interdisciplinary field of political neuroscience (or neuropolitics) is focused on understanding the neural mechanisms underlying political information processing and decision making. Most of the existing work in this area has utilized structural magnetic resonance imaging, functional magnetic resonance imaging, or electroencephalography, and focused on understanding areas of the brain commonly implicated in social and affective neuroscience more generally. This includes brain regions involved in affective and evaluative processing, such as the amygdala, insula, anterior cingulate, and orbitofrontal cortex, as well as regions involved in social cognition (e.g., medial prefrontal cortex), decision making (e.g., dorsolateral prefrontal cortex), and reward processing (e.g., ventral striatum). Existing research in political neuroscience has largely focused on understanding candidate evaluation, political participation, and ideological differences. Early work in the field focused simply on examining neural responses to political stimuli, whereas more recent work has begun to examine more nuanced hypotheses about how the brain engages in political cognition and decision making. While the field is still relatively new, this work has begun to improve our understanding of how people engage in motivated reasoning about political candidates and elected officials and the extent to which these processes may be automatic versus relatively more controlled. Other work has focused on understanding how brain differences are related to differences in political opinion, showing both structural and functional variation between political liberals and political conservatives. Neuroscientific methods are best used as part of a larger, multimethod research program to help inform theoretical questions about mechanisms underlying political cognition. This work can then be triangulated with experimental laboratory studies, psychophysiology, and traditional survey approaches and help to constrain and ensure that theory in political psychology and political behavior is biologically plausible given what we know about underlying neural architecture. This field will continue to grow, as interest and expertise expand and new technologies become available.


2018 ◽  
Author(s):  
Zhongqiao Lin ◽  
Chechang Nie ◽  
Yuanfeng Zhang ◽  
Yang Chen ◽  
Tianming Yang

AbstractValue-based decision making is a process in which humans or animals maximize their gain by selecting appropriate options and performing the corresponding actions to acquire them. Whether the evaluation process of the options in the brain can be independent from their action contingency has been hotly debated. To address the question, we trained rhesus monkeys to make decisions by integrating evidence and studied whether the integration occurred in the stimulus or the action domain in the brain. After the monkeys learned the task, we recorded both from the orbitofrontal (OFC) and dorsolateral prefrontal (DLPFC) cortices. We found that the OFC neurons encoded the value associated with the single piece of evidence in the stimulus domain. Importantly, the representations of the value in the OFC was transient and the information was not integrated across time for decisions. The integration of evidence was observed only in the DLPFC and only in the action domain. We further used a neural network model to show how the stimulus-to-action transition of value information may be computed in the DLPFC. Our results indicated that the decision making in the brain is computed in the action domain without an intermediate stimulus-based decision stage.


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