  Synaptic efficacy between neurons is known to change within a short time
scale dynamically. Neurophysiological experiments show that high-frequency
presynaptic inputs decrease synaptic efficacy between neurons. This phenomenon
is called synaptic depression, a short term synaptic plasticity. Many
researchers have investigated how the synaptic depression affects the memory
storage capacity. However, the noise has not been taken into consideration in
their analysis. By introducing "temperature", which controls the level of the
noise, into an update rule of neurons, we investigate the effects of synaptic
depression on the memory storage capacity in the presence of the noise. We
analytically compute the storage capacity by using a statistical mechanics
technique called Self Consistent Signal to Noise Analysis (SCSNA). We find that
the synaptic depression decreases the storage capacity in the case of finite
temperature in contrast to the case of the low temperature limit, where the
storage capacity does not change.
