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Related Experiment Video

Updated: Apr 22, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Channel identification machines for multidimensional receptive fields.

Aurel A Lazar¹, Yevgeniy B Slutskiy¹

¹Bionet Group, Department of Electrical Engineering, Columbia University in the City of New York New York, NY, USA.

Frontiers in Computational Neuroscience

|October 14, 2014

Summary

This summary is machine-generated.

This study introduces algorithms to identify multidimensional receptive fields from neuron model spike trains. Researchers found that only the receptive field projection onto the stimulus space can be perfectly identified.

Keywords:

RKHS biophysical neuron models channel identification machines multidimensional receptive fields spiking neural circuits system identification time encoding

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Last Updated: Apr 22, 2026

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Published on: August 1, 2018

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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Published on: February 15, 2017

Area of Science:

Computational Neuroscience
Systems Neuroscience

Background:

Understanding neural receptive fields is crucial for deciphering sensory processing.
Biophysically-grounded neuron models offer a framework for studying neural computation.

Purpose of the Study:

To develop algorithms for identifying multidimensional receptive fields from spike train data.
To determine the conditions under which receptive field identification is possible.

Main Methods:

Development of novel algorithms for receptive field identification.
Analysis of spike trains generated by biophysically-grounded neuron models.
Derivation of theoretical conditions for perfect identification.

Main Results:

Algorithms can identify multidimensional receptive fields directly from spike trains.
Only the projection of the receptive field onto the input stimulus space is perfectly identifiable.
Conditions for successful identification were derived.

Conclusions:

The presented algorithms enable direct identification of receptive field projections.
The findings provide insights into the limitations and possibilities of inferring neural representations from spike data.
Demonstrated successful identification in neural circuits with complex receptive fields.