Temporal correlation based learning in neuron models
| dc.creator | Jost, Juergen | |
| dc.date | 2005-11-11 | |
| dc.date.accessioned | 2026-07-07T06:52:33Z | |
| dc.date.available | 2026-07-07T06:52:33Z | |
| dc.description | We study a learning rule based upon the temporal correlation (weighted by a learning kernel) between incoming spikes and the internal state of the postsynaptic neuron, building upon previous studies of spike timing dependent synaptic plasticity (\cite{KGvHW,KGvH1,vH}). Our learning rule for the synaptic weight $w_{ij}$ is $$ \dot w_{ij}(t)= ε\int_{-\infty}^\infty \frac{1}{T_l} \int_{t-T_l}^t \sum_μδ(τ+s-t_{j,μ}) u(τ) dτ Γ(s)ds $$ where the $t_{j,μ}$ are the arrival times of spikes from the presynaptic neuron $j$ and the function $u(t)$ describes the state of the postsynaptic neuron $i$. Thus, the spike-triggered average contained in the inner integral is weighted by a kernel $Γ(s)$, the learning window, positive for negative, negative for positive values of the time diffence $s$ between post- and presynaptic activity. An antisymmetry assumption for the learning window enables us to derive analytical expressions for a general class of neuron models and to study the changes in input-output relationships following from synaptic weight changes. This is a genuinely non-linear effect (\cite{SMA}). | |
| dc.identifier | https://arxiv.org/abs/q-bio/0511012 | |
| dc.identifier | http://arxiv.org/abs/q-bio/0511012 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/105336 | |
| dc.subject | Neurons and Cognition | |
| dc.title | Temporal correlation based learning in neuron models | |
| dc.type | text |