Information Integration
information integration
Estimating repetitive spatiotemporal patterns from resting-state brain activity data
Repetitive spatiotemporal patterns in resting-state brain activities have been widely observed in various species and regions, such as rat and cat visual cortices. Since they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. Moreover, spatiotemporal patterns involving whole-brain activities may also reflect a process that integrates information distributed over the entire brain, such as motor and visual information. Therefore, revealing such patterns may elucidate how the information is integrated to generate consciousness. In this talk, I will introduce our proposed method to estimate repetitive spatiotemporal patterns from resting-state brain activity data and show the spatiotemporal patterns estimated from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. Our analyses suggest that the patterns involved whole-brain propagating activities that reflected a process to integrate the information distributed over frequencies and networks. I will also introduce our current attempt to reveal signal flows and their roles in the spatiotemporal patterns using a big dataset. - Takeda et al., Estimating repetitive spatiotemporal patterns from resting-state brain activity data. NeuroImage (2016); 133:251-65. - Takeda et al., Whole-brain propagating patterns in human resting-state brain activities. NeuroImage (2021); 245:118711.
Lateral entorhinal cortex directly influences medial entorhinal cortex through synaptic connections in layer 1
Standard models of episodic memory suggest that lateral (LEC) and medial entorhinal cortex (MEC) send independent inputs to the hippocampus, each carrying different types of information. Here, we describe a pathway by which information is integrated between LEC and MEC prior to reaching hippocampus. We demonstrate that LEC sends strong projections to MEC arising from neurons that receive neocortical inputs. Activation of LEC inputs drives excitation of hippocampal-projecting neurons in MEC layer 2, typically followed by inhibition that is accounted for by parallel activation of local inhibitory neurons. We therefore propose that local circuits in MEC may support integration of ‘what’ and ‘where’ information.
Coarse-to-fine information integration in human vision
Data spaces: category (sheaf) theory and phenomenology
In this talk, I’ll introduce the formal concept of a (pre)sheaf as data attached to a topological space. Sheaves capture the notion of patching local sources of information to form a global whole, e.g., the binding of visual features such as colour and shape. The formal theory appears to be closely related to the foundational properties asserted by the Information Integration Theory (IIT) for phenomenology. A comparison is intended to engender discussion on ways that phenomenology may benefit from a sheaf theory, or (more generally) a category theory approach.
Causal role of human frontopolar cortex in information integration during complex decision making
Bernstein Conference 2024