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A compressed sensing perspective of hippocampal function.

Petrantonakis PC, Poirazi P - Front Syst Neurosci (2014)

Bottom Line: Input from the cortex passes through convergent axon pathways to the downstream hippocampal subregions and, after being appropriately processed, is fanned out back to the cortex.In this work, hippocampus related regions and their respective circuitry are presented as a CS-based system whose different components collaborate to realize efficient memory encoding and decoding processes.This proposition introduces a unifying mathematical framework for hippocampal function and opens new avenues for exploring coding and decoding strategies in the brain.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Lab, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas Heraklion, Greece.

ABSTRACT
Hippocampus is one of the most important information processing units in the brain. Input from the cortex passes through convergent axon pathways to the downstream hippocampal subregions and, after being appropriately processed, is fanned out back to the cortex. Here, we review evidence of the hypothesis that information flow and processing in the hippocampus complies with the principles of Compressed Sensing (CS). The CS theory comprises a mathematical framework that describes how and under which conditions, restricted sampling of information (data set) can lead to condensed, yet concise, forms of the initial, subsampled information entity (i.e., of the original data set). In this work, hippocampus related regions and their respective circuitry are presented as a CS-based system whose different components collaborate to realize efficient memory encoding and decoding processes. This proposition introduces a unifying mathematical framework for hippocampal function and opens new avenues for exploring coding and decoding strategies in the brain.

No MeSH data available.


Related in: MedlinePlus

The EC-DG-CA3-CA1 circuit and the conceptual parallelism with the CS framework (top: hippocampal structure; bottom: abstract graphical representation of hippocampal structure and conceptual parallelism with CS framework). The EC projection to DG and CA3, via the perforant path (PP), represents the matrix A of the CS framework. It is analyzed to matrices Φ and Ψ that are linked with subregions LEC and MEC, respectively. Vector x corresponds to the activity of DG afferents projecting to CA3 (mossy fibers, MF). Input to the CA3 from PP and MF lead to the activation of a CA3 population, the vector y. Backprojection from CA3 to DG performs error correction tasks in order to transform a noisy version of the vector y′ to y while recurrent collaterals within CA3 are assumed to perform association tasks. CA3 projects to CA1 via the Schaffer Collaterals (SC) and the information loop closes by the CA1 feedback to the EC, which also receives the EC input via the Temporoammonic pathway (TA).
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Figure 4: The EC-DG-CA3-CA1 circuit and the conceptual parallelism with the CS framework (top: hippocampal structure; bottom: abstract graphical representation of hippocampal structure and conceptual parallelism with CS framework). The EC projection to DG and CA3, via the perforant path (PP), represents the matrix A of the CS framework. It is analyzed to matrices Φ and Ψ that are linked with subregions LEC and MEC, respectively. Vector x corresponds to the activity of DG afferents projecting to CA3 (mossy fibers, MF). Input to the CA3 from PP and MF lead to the activation of a CA3 population, the vector y. Backprojection from CA3 to DG performs error correction tasks in order to transform a noisy version of the vector y′ to y while recurrent collaterals within CA3 are assumed to perform association tasks. CA3 projects to CA1 via the Schaffer Collaterals (SC) and the information loop closes by the CA1 feedback to the EC, which also receives the EC input via the Temporoammonic pathway (TA).

Mentions: Memory formation in the hippocampus depends on processing of information from distinct subregions. Information passes from one subregion to the other toward a more efficient encoding (Amaral, 1993). We assume that in the EC-DG-CA3 circuit (Figure 4), information to be stored/processed is transferred from EC to CA3 via the DG to achieve an orthogonal encoding, i.e., more distinguishable from other memory entities, and more information-rich (compressed) than in its origin. Having established the conceptual framework of CS, we propose that an equivalent transformation of information is performed in the hippocampus. Specifically, in the CS framework, the representation of a signal f, via a basis set Ψ, is actually “summarized” by the encoded, compressed version y through a measurement/sensing procedure, hence, the term CS. Expanding on this parallelism, we next search for basis and measurement sets in the hippocampus and consider their conformation with the basic constraints (see Section Encoding) of the CS theory.


A compressed sensing perspective of hippocampal function.

Petrantonakis PC, Poirazi P - Front Syst Neurosci (2014)

The EC-DG-CA3-CA1 circuit and the conceptual parallelism with the CS framework (top: hippocampal structure; bottom: abstract graphical representation of hippocampal structure and conceptual parallelism with CS framework). The EC projection to DG and CA3, via the perforant path (PP), represents the matrix A of the CS framework. It is analyzed to matrices Φ and Ψ that are linked with subregions LEC and MEC, respectively. Vector x corresponds to the activity of DG afferents projecting to CA3 (mossy fibers, MF). Input to the CA3 from PP and MF lead to the activation of a CA3 population, the vector y. Backprojection from CA3 to DG performs error correction tasks in order to transform a noisy version of the vector y′ to y while recurrent collaterals within CA3 are assumed to perform association tasks. CA3 projects to CA1 via the Schaffer Collaterals (SC) and the information loop closes by the CA1 feedback to the EC, which also receives the EC input via the Temporoammonic pathway (TA).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126371&req=5

Figure 4: The EC-DG-CA3-CA1 circuit and the conceptual parallelism with the CS framework (top: hippocampal structure; bottom: abstract graphical representation of hippocampal structure and conceptual parallelism with CS framework). The EC projection to DG and CA3, via the perforant path (PP), represents the matrix A of the CS framework. It is analyzed to matrices Φ and Ψ that are linked with subregions LEC and MEC, respectively. Vector x corresponds to the activity of DG afferents projecting to CA3 (mossy fibers, MF). Input to the CA3 from PP and MF lead to the activation of a CA3 population, the vector y. Backprojection from CA3 to DG performs error correction tasks in order to transform a noisy version of the vector y′ to y while recurrent collaterals within CA3 are assumed to perform association tasks. CA3 projects to CA1 via the Schaffer Collaterals (SC) and the information loop closes by the CA1 feedback to the EC, which also receives the EC input via the Temporoammonic pathway (TA).
Mentions: Memory formation in the hippocampus depends on processing of information from distinct subregions. Information passes from one subregion to the other toward a more efficient encoding (Amaral, 1993). We assume that in the EC-DG-CA3 circuit (Figure 4), information to be stored/processed is transferred from EC to CA3 via the DG to achieve an orthogonal encoding, i.e., more distinguishable from other memory entities, and more information-rich (compressed) than in its origin. Having established the conceptual framework of CS, we propose that an equivalent transformation of information is performed in the hippocampus. Specifically, in the CS framework, the representation of a signal f, via a basis set Ψ, is actually “summarized” by the encoded, compressed version y through a measurement/sensing procedure, hence, the term CS. Expanding on this parallelism, we next search for basis and measurement sets in the hippocampus and consider their conformation with the basic constraints (see Section Encoding) of the CS theory.

Bottom Line: Input from the cortex passes through convergent axon pathways to the downstream hippocampal subregions and, after being appropriately processed, is fanned out back to the cortex.In this work, hippocampus related regions and their respective circuitry are presented as a CS-based system whose different components collaborate to realize efficient memory encoding and decoding processes.This proposition introduces a unifying mathematical framework for hippocampal function and opens new avenues for exploring coding and decoding strategies in the brain.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Lab, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas Heraklion, Greece.

ABSTRACT
Hippocampus is one of the most important information processing units in the brain. Input from the cortex passes through convergent axon pathways to the downstream hippocampal subregions and, after being appropriately processed, is fanned out back to the cortex. Here, we review evidence of the hypothesis that information flow and processing in the hippocampus complies with the principles of Compressed Sensing (CS). The CS theory comprises a mathematical framework that describes how and under which conditions, restricted sampling of information (data set) can lead to condensed, yet concise, forms of the initial, subsampled information entity (i.e., of the original data set). In this work, hippocampus related regions and their respective circuitry are presented as a CS-based system whose different components collaborate to realize efficient memory encoding and decoding processes. This proposition introduces a unifying mathematical framework for hippocampal function and opens new avenues for exploring coding and decoding strategies in the brain.

No MeSH data available.


Related in: MedlinePlus