The top row shows the weight vectors of two typical output neurons that develop when the input neurons have peaked eye-position gain modulation and the network is trained with either the Hebbian learning rule (A) or the trace learning rule (B). The bottom row shows the weight vectors of two typical output neurons when the input neurons have monotonic eye-position gain and the network is trained with either the Hebbian learning rule (C) or the standard trace learning rule (D).</p
<p>Simulations exploring the effects of varying the length of the activation time constants and the...
The figure shows the firing rate responses of output neuron #168 before training (A) and after train...
<p>(a) Regression based analysis of neuronal learning. Each row in the colormap shows an individual ...
The figure shows the firing rate responses of output neurons before and after training with the stan...
<p>These results are from a simulation with an axonal conduction delay of 100 ms, and a rotational v...
<p>The development of the firing responses and synaptic weights of output neuron #79 before and afte...
The figure shows population analyses of the response properties of output neurons in the manually pr...
<p>Plots A-D show the connection weights from neurons representing each cue (i.e., red and green) to...
The figure shows the firing rate responses of output neuron #328 before training (A) and after train...
The scatter plot shows the reference frame response characteristics of all output neurons before and...
The figure shows the strengths of the afferent synapses from the input population to output neuron #...
To deepen the understanding of the human brain, many researchers have created a new way of analyzing...
The scatter plot shows the eye-centredness and head-centredness values of all output neurons from fo...
Results are shown for the model with peaked eye-position gain modulation before training (A) and aft...
The figure shows the firing rate responses of output neuron #223 before training (A) and after train...
<p>Simulations exploring the effects of varying the length of the activation time constants and the...
The figure shows the firing rate responses of output neuron #168 before training (A) and after train...
<p>(a) Regression based analysis of neuronal learning. Each row in the colormap shows an individual ...
The figure shows the firing rate responses of output neurons before and after training with the stan...
<p>These results are from a simulation with an axonal conduction delay of 100 ms, and a rotational v...
<p>The development of the firing responses and synaptic weights of output neuron #79 before and afte...
The figure shows population analyses of the response properties of output neurons in the manually pr...
<p>Plots A-D show the connection weights from neurons representing each cue (i.e., red and green) to...
The figure shows the firing rate responses of output neuron #328 before training (A) and after train...
The scatter plot shows the reference frame response characteristics of all output neurons before and...
The figure shows the strengths of the afferent synapses from the input population to output neuron #...
To deepen the understanding of the human brain, many researchers have created a new way of analyzing...
The scatter plot shows the eye-centredness and head-centredness values of all output neurons from fo...
Results are shown for the model with peaked eye-position gain modulation before training (A) and aft...
The figure shows the firing rate responses of output neuron #223 before training (A) and after train...
<p>Simulations exploring the effects of varying the length of the activation time constants and the...
The figure shows the firing rate responses of output neuron #168 before training (A) and after train...
<p>(a) Regression based analysis of neuronal learning. Each row in the colormap shows an individual ...