Cortical hierarchy

Cortical information processing is structurally and functionally organized into hierarchical pathways, with primary sensory cortical regions providing modality specific information and associative cortical regions playing a more integrative role, cortical hierarchy. Historically, there has been debate as to whether primary cortical regions mature earlier than associative cortical regions, or whether both primary and associative cortical regions mature simultaneously. Identifying whether primary and associative cortical regions mature hierarchically or simultaneously will not only deepen our understanding of the mechanisms that regulate brain maturation, but it will also provide fundamental insight into aspects of adolescent behavior, cortical hierarchy, neurodevelopmental disorders and computational models of neural processing. This mini-review article summarizes the current evidence cortical hierarchy the sequential and hierarchical nature of cortical maturation, and then proposes a new cellular model underlying this process, cortical hierarchy.

Hierarchical cortical organization is found in all sensory systems, in the reward system, and in the memory systems. Adjacent cortical areas in the hierarchy are connected by strong forward connections, and weaker backprojections which have synapses in cortical layer 1. There is convergence from cortical area to cortical area, in that neurons in a cortical area receive inputs from a limited region topologically of the preceding cortical area. This enables neurons to operate with the number of synapses from the preceding cortical area received by a neuron limited to in the order of 10, synapses. This is a major cortical principle of operation, for if each processing system consisted of only an input and an output cortical area, any neuron in the output area would need to receive the biologically implausible number of tens of millions of synapses to cover the whole space of the input cortical area. The convergence from cortical area to cortical area is such that after approximately at most four areas or stages of cortical processing, the convergence is sufficient to enable a single neuron at the top of the hierarchy to receive input from anywhere in the first cortical area, as illustrated in Fig.

Cortical hierarchy

A fundamental aspect of human experience is that it is segmented into discrete events. This may be underpinned by transitions between distinct neural states. Using an innovative data-driven state segmentation method, we investigate how neural states are organized across the cortical hierarchy and where in the cortex neural state boundaries and perceived event boundaries overlap. Our results show that neural state boundaries are organized in a temporal cortical hierarchy, with short states in primary sensory regions, and long states in lateral and medial prefrontal cortex. State boundaries are shared within and between groups of brain regions that resemble well-known functional networks. Perceived event boundaries overlap with neural state boundaries across large parts of the cortical hierarchy, particularly when those state boundaries demarcate a strong transition or are shared between brain regions. Taken together, these findings suggest that a partially nested cortical hierarchy of neural states forms the basis of event segmentation. This article addresses the question of how the brain segments naturalistic events and the relationship between perceived event boundaries and neural pattern shifts. By applying an innovative analysis to a large, publicly available dataset, they observe evidence of different timescales of neural state shifts that correspond with perceived event bounds. These results will be of interest to cognitive neuroscientists investigating the relationship between neural states and event segmentation. Segmentation of information into meaningful units is a fundamental feature of our conscious experience in real-life contexts. Spatial information processing is characterized by segmenting spatial regions into objects e. In a similar way, temporal information processing is characterized by segmenting our ongoing experience into separate events Kurby and Zacks, ; Newtson et al. Segmentation improves our understanding of ongoing perceptual input Zacks et al. Recent work has shown that the end of an event triggers an evoked response in the hippocampus Baldassano et al.

We define boundary strength as the Pearson correlation distance between the neural activity patterns of consecutive neural states.

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Brains are composed of anatomically and functionally distinct regions performing specialized tasks, but regions do not operate in isolation.

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Cortical hierarchy

Federal government websites often end in. The site is secure. Preview improvements coming to the PMC website in October Learn More or Try it out now. Concepts shape the interpretation of facts. However, this concept has been interpreted in many different ways, which are not well aligned. This observation suggests that the concept is ill defined. Hierarchy is one of the most popular terms in current network and systems neuroscience. Failure to do so is bound to result in confusion.

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Appendix 2 Neuronal network models. We were able to address this issue by allowing the algorithm to place two boundaries at a time. Self-help and Personal Development. Astronomy and Astrophysics. To further investigate this potential confound, we computed the correlation between scan to scan head motion and state boundaries for each searchlight within each of the 15 groups of participants. Neuroimage, 31, Google Scholar Dahmen, D. This resulted in strongly anticorrelated states see Appendix 1—figure 3A. To make sure differences in number of states between methods did not impact our results, we fixed the number of state boundaries to 18 or Please refer to the comments below for these notes. Due to the slow decay of the GCaMP6s calcium sensor, the magnitude but not the precise timing of neural activity obscured by photostimulation artifact was clearly apparent in the ms post-stimulus period Fig. In general, justification is provided for the different analysis choices in the manuscript. A Neural state boundaries are shown for each community per timepoint for each searchlight, grouped in functional networks. See below.

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Expand Front Matter. Moore, J. Business and the Environment. Minshew, N. Expand End Matter. Parts of the correlation matrices for two selected searchlights are shown in the insets for each of the groups, representing approximately 1. Rolls Edmund T. The peak delays varied between 4. Nature , — Appendix 3 Information theory, and neuronal encoding.