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Updated: Feb 3, 2026

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

New discretization method applied to NBV problem: Semioctree.

L M González-deSantos^1,2, J Martínez-Sánchez^1,2, H González-Jorge^1,2

¹Applied Geotechnologies Group, Departament of Natural Resources and Environmental Engineering, University of Vigo, Vigo, Spain.

|November 2, 2018

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This study introduces a novel hybrid discretization for the Next Best View (NBV) problem, improving scan efficiency. The new method significantly speeds up 3D scanning processes by optimizing data structures.

Area of Science:

Computer Vision
Computational Geometry
Robotics

Background:

The Next Best View (NBV) problem is crucial for efficient 3D data acquisition.
Existing octree structures face challenges with dynamic point cloud growth in NBV.
Homogenous voxelization can be computationally intensive.

Purpose of the Study:

To develop a novel discretization methodology for the NBV problem.
To combine the benefits of homogenous voxelization and octree structures.
To improve the efficiency and speed of 3D scanning processes.

Main Methods:

A hybrid approach combining coarse voxelization with an octree structure.
Ensuring consistent discretization limits and positions across scans.
Adapting a previous NBV methodology to the new hybrid structure.

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A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

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Main Results:

The hybrid method is three times faster than homogenous voxelization at 0.2m resolution.
Achieved a 400% reduction in discretization elements (voxels/octants).
Successfully transferred information between successive scans.

Conclusions:

The proposed hybrid discretization offers significant speed improvements for NBV.
This method effectively addresses the limitations of traditional octree structures in dynamic scanning.
The approach enhances the overall efficiency of 3D reconstruction and data acquisition.