scholarly journals Linear-nonlinear cascades capture synaptic dynamics

2021 ◽  
Vol 17 (3) ◽  
pp. e1008013
Author(s):  
Julian Rossbroich ◽  
Daniel Trotter ◽  
John Beninger ◽  
Katalin Tóth ◽  
Richard Naud
Keyword(s):  
Neural Networks ◽  
Maximum Likelihood ◽  
Action Potentials ◽  
Mathematical Framework ◽  
Short Term ◽  
Maximum Likelihood Approach ◽  
Different Types ◽  
Likelihood Approach ◽  
Synaptic Dynamics ◽  
Nonlinear Operation

Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.

scholarly journals Synaptic Dynamics as Convolutional Units

2020 ◽  
Author(s):  
Julian Rossbroich ◽  
Daniel Trotter ◽  
Katalin Tóth ◽  
Richard Naud
Keyword(s):  
Neural Networks ◽  
Action Potentials ◽  
Computational Models ◽  
Mathematical Framework ◽  
Challenging Problem ◽  
Maximum Likelihood Approach ◽  
Different Types ◽  
Likelihood Approach ◽  
Synaptic Dynamics ◽  
Nonlinear Operation

AbstractSynaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.

Emotion detection in infants² cries based on a maximum likelihood approach

2006 ◽  
Author(s):  
S. Matsunaga ◽  
S. Sakaguchi ◽  
M. Yamashita ◽  
S. Miyahara ◽  
S. Nishitani ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Emotion Detection ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach
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