Influence of Synaptic Depression on Memory Storage Capacity

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.

[1]  M. Shiino,et al.  Self-consistent signal-to-noise analysis and its application to analogue neural networks with asymmetric connections , 1992 .

[2]  Masato Okada,et al.  Instabilities in Associative Memory Model with Synaptic Depression and Switching Phenomena among Attractors , 2010 .

[3]  Hilbert J. Kappen,et al.  Associative Memory with Dynamic Synapses , 2002, Neural Computation.

[4]  L. Abbott,et al.  Synaptic Depression and Cortical Gain Control , 1997, Science.

[5]  Masato Okada,et al.  Retrieval Property of Attractor Network with Synaptic Depression(General) , 2007 .

[6]  D. Amit,et al.  Statistical mechanics of neural networks near saturation , 1987 .

[7]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Lovorka Pantic,et al.  Storage capacity of attractor neural networks with depressing synapses. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Joaquín J. Torres,et al.  Maximum Memory Capacity on Neural Networks with Short-Term Synaptic Depression and Facilitation , 2009, Neural Computation.

[10]  Mark C. W. van Rossum,et al.  Recurrent networks with short term synaptic depression , 2009, Journal of Computational Neuroscience.

[11]  J. Deuchars,et al.  Temporal and spatial properties of local circuits in neocortex , 1994, Trends in Neurosciences.

[12]  L. Abbott,et al.  Limits on the memory storage capacity of bounded synapses , 2007, Nature Neuroscience.

[13]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.