Excitatory and inhibitory STDP cooperatively shape structured lateral connections | The synaptic weight dynamics of lateral excitatory and inhibitory connections are approximately given as |
Lateral inhibition enhances minor source detection by STDP | Initially, in all output neurons, synaptic weights from A-neurons (blue triangles in Fig 2A) become larger because A-neurons are more strongly correlated with one another than B-neurons are. |
Lateral inhibition enhances minor source detection by STDP | As a result, synaptic weights from A-neurons to group 1 become weaker, and group 1 neurons eventually become selective for the minor source B (Fig 2C right). |
Lateral inhibition should be strong, fast, and sharp | In this approximation, by inserting Eq (32) into Eq (29), the mean synaptic weight changes of feedforward connections follow |
Model | For STDP, we used pairwise log-STDP (Fig 1B) [31], which replicates the experimentally observed long-tailed synaptic weight distribution [32,33]. |
Model | yj(t) The spiking activity of output neuron j uk’(t) Membrane potential of inhibitory neuron k zk(t) The spiking activity of inhibitory neuron k wjix The synaptic weight of a feed-forward excitatory connection from ito j qifl Response probability of input neuron ito external source p 11X, 12X The correlation kernel functions used the gamma distribution With shape parameter kg = 3 in order to reproduce broad spike correlations typically observed in cortical neurons [36,37]. |
Model | Synaptic weight dynamics by STDP is written as ods for details), the weight change of the feedforward connection WX can be approximated as |
dev Ma Ma d: g ZLanfv’ VEGA? Z qquv’p — NaWZMa WYZL“ Wig", vi Z qquV/p v,=1 p v’=1 P | Note that because of the mutual inhibition, the synaptic weight from A-neuron is smaller when both groups learn A than it is when only group 1 learns A. |
Effect of Dopamine on the DTT | Dopamine affects striatal function by modulating the intrinsic excitability of the MSNs and synaptic weights [24] and synaptic plasticity [25, 26] of the cortico-striatal projections. |
Implications for the understanding brain disorders involving the basal ganglia | We arrived at this functional description of the striatum as a DTT by considering the striatal network dynamics emerging from including low-level properties such as synaptic weights and connection probabilities. |
Introduction | In such single population models, the striatal output is controlled by the strength of cortico-striatal synaptic weights . |