Spatial encoding in dorsomedial prefrontal cortex and hippocampus is related during deliberation

Hasz, Brendan M.; Redish, A. David

doi:10.1002/hipo.23250

Cited by 31 publications

(27 citation statements)

References 97 publications

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“…The central role of the hippocampus for orchestrating goal-oriented behavior is further suggested by its involvement in “vicarious trial-and-error” behavior [ 138 ], as well as by the centrality of theta rhythms for intentional control [ 126 , 131 , 139 , 140 ]. Additional supporting evidence can be found in hippocampally-mediated orchestration of counterfactual inferences in other domains, ranging from predictive information over likely trajectories for locomoting rodents [ 141 , 142 ] to the simulation of alternative perspectives by imagining humans [ 143 , 144 ].…”

Section: From Action To Attention and Back Againmentioning

confidence: 98%

The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again

Safron

2021

Entropy

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Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both “Cartesian theaters” and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals).

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Section: From Action To Attention and Back Againmentioning

confidence: 98%

The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again

Safron

2021

Entropy

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“…Theta oscillations are prominent in CA1 during navigation, and CA1 cell assemblies are organized into theta sequences within single oscillation cycles. While it is not known whether ordered sequences of PFC cells occur during hippocampal theta oscillations, previous studies have shown that PFC cells phase-lock and phase-precess (i.e., spikes of a cell occur at progressively earlier theta phases as an animal move through its spatial field) to hippocampal theta oscillations (Jadhav et al, 2016;Jones and Wilson, 2005a;Siapas et al, 2005;Sigurdsson et al, 2010); further, theta-frequency synchrony and coherent spatial coding between the hippocampus and PFC is prominent during memory-guided navigation (Benchenane et al, 2010;Gordon, 2011;Hasz and Redish, 2020;Jones and Wilson, 2005b;Sigurdsson et al, 2010;Zielinski et al, 2019).…”

Section: Fast Theta Sequences In Ca1 and Pfcmentioning

confidence: 99%

Multiple time-scales of decision-making in the hippocampus and prefrontal cortex

Tang

Shin

Jadhav

2021

eLife

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The prefrontal cortex and hippocampus are crucial for memory-guided decision-making. Neural activity in the hippocampus exhibits place-cell sequences at multiple timescales, including slow behavioral sequences (~seconds) and fast theta sequences (~100-200 ms) within theta oscillation cycles. How prefrontal ensembles interact with hippocampal sequences to support decision-making is unclear. Here, we examined simultaneous hippocampal and prefrontal ensemble activity in rats during learning of a spatial working-memory decision task. We found clear theta sequences in prefrontal cortex, nested within its behavioral sequences. In both regions, behavioral sequences maintained representations of current choices during navigation. In contrast, hippocampal theta sequences encoded alternatives for deliberation, and were coordinated with prefrontal theta sequences that predicted upcoming choices. During error trials, these representations were preserved to guide ongoing behavior, whereas replay sequences during inter-trial periods were impaired prior to navigation. These results establish cooperative interaction between hippocampal and prefrontal sequences at multiple timescales for memory-guided decision-making.

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“…In our opinion, however, it is more likely that examining HPC interactions with other areas, such as the medial prefrontal cortex (mPFC). (Brown et al, 2016;Voss and Cohen, 2017;Schmidt et al, 2019;Hasz and Redish, 2020;Kidder et al, 2021) (Jones and Wilson, 2005;Benchenane et al, 2010), suggestive of cross-regional communication (Fries, 2005(Fries, , 2015. Finally, experiments using optogenetics to perturb the mPFC in a taskepoch-specific manner during the SDA task showed that stimulation decreased the proportion of VTEs rats engaged in, with a trend toward choice epoch mPFC disruption having a greater effect than stimulation in other epochs (Kidder et al, 2021).…”

Section: Discussionmentioning

confidence: 94%

“…We suspect part of the reason they have not received more attention is that VTE trajectories can be highly variable, which makes it difficult to identify them algorithmically (Goss and Wischner, 1956). The Redish lab has proposed the IdPhi metric, which quantifies changes in heading angles as rats traverse choice points, for identifying VTEs (Papale et al, 2012(Papale et al, , 2016Redish, 2016;Redish, 2016, 2018;Schmidt et al, 2019;Hasz and Redish, 2020). While successful in their hands, IdPhi, admittedly, "does not provide a sharp boundary between VTE and not" (Papale et al, 2016), Supplemental Material: Experimental Procedures).…”

Section: Background and Introductionmentioning

confidence: 99%

A machine learning approach for detecting vicarious trial and error behaviors

Miles

Kidder

Wang

et al. 2021

Preprint

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Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In order to understand the neural underpinnings of these putative bridges between behavior and neural dynamics, researchers need the ability to readily distinguish VTEs from non-VTEs. Here we utilize a small set of trajectory-based features and standard machine learning classifiers to identify VTEs from non-VTEs for rats performing a spatial delayed alternation task (SDA) on an elevated plus maze. We also show that previously reported features of the hippocampal field potential oscillation can be used in the same types of classifiers to separate VTEs from non-VTEs with above chance performance. However, we caution that the modest classifier success using hippocampal population dynamics is not sufficient for identifying trials where VTEs occur, and show that combining oscillation-based features with trajectory-based features degrades classifier performance compared to trajectory-based features alone. Overall, we propose a standard set of features useful for trajectory-based VTE classification and support previous suggestions that VTEs are supported by a network including, but likely extending beyond, the hippocampus.

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Spatial encoding in dorsomedial prefrontal cortex and hippocampus is related during deliberation

Cited by 31 publications

References 97 publications

The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again

The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again

Multiple time-scales of decision-making in the hippocampus and prefrontal cortex

A machine learning approach for detecting vicarious trial and error behaviors

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