Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning
Fig 3
Schematic of recurrent neural network performing center-out reaching.
(a) Schematic of RNN, with input layer, hidden layer, and output layer. The three inputs were a condition-independent hold signal that was released at the go cue and two inputs representing the target angle. The two outputs were a linear combination of the internal neurons and read out velocity in the x and y direction. All weights were modified during training. The network received no feedback from the output layer. (b) Output velocity profiles produced by the RNN compared with target velocity used in training. The normalized error was less than 0.1%. (c) Simulated kinematics produced by integrating the velocity profiles over time, with corresponding targets for illustration.