Thus, our model ensures cross-modal generation from RGC spike signals to stimuli and vice versa. In our framework, the generation from stimuli to RGC spike signals is equivalent to neural encoding ...

Motivated by this, we propose a new supervised learning method that can train multilayer spiking neural networks to solve classification problems based on a rapid, first-to-spike decoding strategy ...

Our model accounts ... below. Decoding: readout of decision-related information from LIP spike trains Encoding models specify an explicit probability distribution over neural activity given ...

Descriptive statistical models of neural responses ... from stimuli to spike responses while ignoring biophysical details of the encoding process. Here, we introduce an alternative approach, the ...

These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output.

"Decoding Finger Velocity from Cortical Spike ... spiking neural network (SNN) models for the decoding of hand kinematics from neural activity data. The two models serve different purposes: bigRSNN: a ...

Thus, our model ensures cross-modal generation from RGC spike signals to stimuli and vice versa. In our framework, the generation from stimuli to RGC spike signals is equivalent to neural encoding ...