Analysis of Hippocampal Memory Replay Using Neural Population Decoding

2011 ◽  
pp. 259-282 ◽  
Author(s):  
Fabian Kloosterman
Keyword(s):  
Neural Population ◽  
Population Decoding

Population Coding with Correlation and an Unfaithful Model

2001 ◽  
Vol 13 (4) ◽  
pp. 775-797 ◽  
Author(s):  
Si Wu ◽  
Hiroyuki Nakahara ◽  
Shun-ichi Amari
Keyword(s):  
Maximum Likelihood ◽  
Center Of Mass ◽  
Computational Cost ◽  
Limited Range ◽  
Recurrent Network ◽  
Population Coding ◽  
Likelihood Inference ◽  
Neural Population ◽  
Asymptotically Efficient ◽  
Population Decoding

This study investigates a population decoding paradigm in which the maximum likelihood inference is based on an unfaithful decoding model (UMLI). This is usually the case for neural population decoding because the encoding process of the brain is not exactly known or because a simplified decoding model is preferred for saving computational cost. We consider an unfaithful decoding model that neglects the pair-wise correlation between neuronal activities and prove that UMLI is asymptotically efficient when the neuronal correlation is uniform or of limited range. The performance of UMLI is compared with that of the maximum likelihood inference based on the faithful model and that of the center-of-mass decoding method. It turns out that UMLI has advantages of decreasing the computational complexity remarkably and maintaining high-leveldecoding accuracy. Moreover, it can be implemented by a biologically feasible recurrent network (Pouget, Zhang, Deneve, & Latham, 1998). The effect of correlation on the decoding accuracy is also discussed.

Seeing White: Qualia in the Context of Decoding Population Codes

1999 ◽  
Vol 11 (6) ◽  
pp. 1261-1280 ◽  
Author(s):  
Sidney R. Lehky ◽  
Terrence J. Sejnowski
Keyword(s):  
Nervous System ◽  
Input Parameter ◽  
Color Space ◽  
Neural Population ◽  
Physical Parameters ◽  
Representational Space ◽  
Population Codes ◽  
Single Input ◽  
High Level ◽  
Population Decoding

When the nervous system is presented with multiple simultaneous inputs of some variable, such as wavelength or disparity, they can be combined to give rise to qualitatively new percepts that cannot be produced by any single input value. For example, there is no single wavelength that appears white. Many models of decoding neural population codes have problems handling multiple inputs, either attempting to extract a single value of the input parameter or, in some cases, registering the presence of multiple inputs without synthesizing them into something new. These examples raise a more general issue regarding the interpretation of population codes. We propose that population decoding involves not the extraction of specific values of the physical inputs, but rather a transformation from the input space to some abstract representational space that is not simply related to physical parameters. As a specific example, a four-layer network is presented that implements a transformation from wavelength to a high-level hue-saturation color space.

scholarly journals Neural Population Decoding Reveals the Intrinsic Positivity of the Self

2016 ◽  
Author(s):  
Robert S. Chavez ◽  
Todd F. Heatherton ◽  
Dylan D. Wagner
Keyword(s):  
The Self ◽  
Neural Population ◽  
Population Decoding

scholarly journals Dimensionality reduction for neural population decoding

2021 ◽  
Author(s):  
Charles R Heller ◽  
Stephen V David
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Population Coding ◽  
Neural Population ◽  
Neural Decoding ◽  
Population Activity ◽  
Neural Recordings ◽  
Analytical Tools ◽  
Population Decoding

Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.

scholarly journals Multiplicative mixing of object identity and image attributes in single inferior temporal neurons

2018 ◽  
Vol 115 (14) ◽  
pp. E3276-E3285 ◽  
Author(s):  
N. Apurva Ratan Murty ◽  
S. P. Arun
Keyword(s):  
Deep Neural Networks ◽  
The Other ◽  
Neural Population ◽  
Object Identity ◽  
Single Neurons ◽  
Neural Responses ◽  
Subtle Effect ◽  
Visual Areas ◽  
High Level ◽  
Population Decoding

Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively.

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