Data for Brain Reference
Architecture of
TN24HippocampalFormation
July 16, 2024
Takeshi Nakashima (Ritsumeikan University),

Akira Taniguchi (Ritsumeikan University),
Ayako Fukawa (The Whole Brain Architecture Initiative)
Hiroshi Yamakawa (The Whole Brain Architecture Initiative, The University of Tokyo)
1Outline• Introduction• SCID method• Region of interest• Brain Information flow
• Hypothetical component diagram• Function realization graph• Research based on the BRA data
• Conclusion2

Outline• Introduction• SCID method• Region of interest• Brain Information flow
• Hypothetical component diagram• Function realization graph• Research based on the BRA data
• Conclusion
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Introduction
For organisms, it is important to acquire internal representation that enable
flexible behavior in complex and dynamic environments.

ResearchQuestion

What kind of internal representation should be acquired?

Approach

The spatial cognition computational model inspired by brain.
4• Introduction• SCID method• Region of interest• Brain Information flow
• Hypothetical component diagram• Function realization graph• Research based on the BRA data
• Conclusion
5

SCID method
Structure-constrained interface decomposition (SCID) method[1]:This method enables the hypothetical construction of software architecture inspired by the brain,even though our neuroscience knowledge is still insufficient to elucidate the whole picture.

[1] Yamakawa, H. (2021). The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain. Neural Networks, 144 , 478–495.

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Region of interest(ROI)
Spatial cognition has been studied in the fields of cognitive science and neuroscience.
• Cognitive map[2]• Place cell[3] / Grid cell[4]Place cell

Rats do not learn a simple stimulus-response mapping,but spatial representation that enable flexible behavior.

Grid cell

Place cell in the hippocampus and grid cell in the
medial entorhinal cortex(MEC) as cognitive map.

The hippocampal formation is a crucial part of spatial cognition.
[2]Edward C Tolman. Cognitive maps in rats and men. Psychological review, Vol. 55, No. 4, p. 189, 1948.
[3]O’Keefe J. Place units in the hippocampus of the freely moving rat. Exp. Neurol. 51:78–109,1976[4]Moser, E. I.. "Microstructure of a spatial map in the entorhinal cortex". Nature. 436 (7052): 801–806.2005

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Region of interest(ROI)
ROI of this study is hippocampal formation including :••
••
•
•••
•

dentate gyrus (DG)
cornu ammonis-1 and - 3 (CA1, CA3)
lateral entorhinal cortex (LEC)
medial entorhinal cortex (MEC)
subiculum (Sb)parasubiculum (ParaSb)
perirhinal cortex (PER)
postrhinal cortex (POR)
retrosplenial cortex (RSC)

hippocampus

temporoammonicpathway(EC →CA1)

recurrent collateral pathway(CA3 →CA3)

trisynaptic loop
(EC→DG →CA3 →CA1)[5]E. T. Rolls, Brain computations and connectivity. Oxford University Press, 2023.

Connections within the hippocampus [5]8Brain Information Flow (BIF)[6]• Rectangles represent
brain regions andArrows represent
projections.

• Input/Output
Input:
POR, PER, and RSC

Output:the predicted values of POR
and PER at the next time
step[6] Taniguchi, A., Fukawa, A., & Yamakawa, H. (2022). Hippocampal formation-inspired probabilistic generative model. Neural Networks, 151 , 317-335.

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Hypothetical component diagram (HCD)
Probabilistic Generative Model in brain-inspired computational model enable :• easy integration of PGM modules [7].
• creation of models incorporating the Bayesian brain hypothesis[8], the freeenergy principle[9], and the predictive coding hypothesis.
Constructing the HDC requires performing Generation-inference process allocation(GIPA)[10] which involves :• assigning the generative or inference models to projections.
• introduction time delay in the brain's loop circuits to ensure PGMs are directed
acyclic graphs.

[7] Taniguchi, Tadahiro, et al. "Neuro-serket: development of integrative cognitive system through the composition of deep probabilistic generative models." New Generation Computing 38 (2020): 23-48.
[8] Doya, Kenji, ed. Bayesian brain: Probabilistic approaches to neural coding. MIT press, 2007.
[9] Karl Friston, et al. "Free energy, value, and attractors.“ Computational and mathematical methods in medicine 2012.
[10] Taniguchi, Tadahiro, et al. "A whole brain probabilistic generative model: Toward realizing cognitive architectures for developmental robots." Neural Networks 150 (2022): 293-312.

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Hypothetical component diagram (HCD)
Egocentric and allocentric representations of space are handled by theLEC and MEC, respectively[11].

[12][11] Wang, C., Chen, X., and Knierim, J. J. (2020). Egocentric and allocentric representations of space in the rodent brain. Current Opinion in Neurobiology 60, 12–20[12] Ekstrom, A. D., Arnold, A. E. G. F., and Iaria, G. (2014). A critical review of the allocentric spatial representation and its neural underpinnings: toward a network-based perspective. Frontiers in Human Neuroscience 811Hypothetical component diagram (HCD)
Abstracted local information(Spatial categorization)

Integrated semantic memory(representation of space)

Path integration(self-localization)

Allocentric
information

Egocentric
information
movement
information[6] Taniguchi, A., Fukawa, A., & Yamakawa, H. (2022). Hippocampal formation-inspired probabilistic generative model. Neural Networks, 151 , 317-335.

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Function realization graph (FRG)
The top-level function is spatial cognition, supported by spatial categorization andself-localization. Spatial categorization (Egocentric) is primarily supported by the LEC,
while self-localization (Allocentric) is mainly supported by the MEC. These functions areintegrated by the hippocampus.
Spatial cognition

Self-localization

Place categorization

LEChippocampus

MEC13

Outline• Introduction• SCID method• Region of interest• Brain Information flow
• Hypothetical component diagram• Research based on the BRA data
• Conclusion
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Research based on the BRA data
We constructed and evaluated a spatial cognition model based on thisData for Brain Reference Architecture, and it was accepted for thejournal Frontiers in Computational Neuroscience last month[13].
Grand truth
Estimation by RSSM module
Estimation by Model

The Hippocampal formation-inspired
model showed adaptability even inthe sudden change of teleportation.

[13] Nakashima, Takeshi, et al. "Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective." Frontiers in Computational
Neuroscience 18: 1398851.

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Outline• Introduction• SCID method• Region of interest• Brain Information flow
• Hypothetical component diagram• Research based on the BRA data
• Conclusion
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Conclusion◼The BIF and HDC of hippocampal formation inspired spatial
cognition model is constructed using the SCID method.
In this process, the GIPA is used to create the HDC from theBIF for building the spatial cognition model with aprobabilistic generative model.
◼The spatial cognition model is being created based on theBRA data and shows higher adaptability in the suddenchange situation.
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