Biomedical subjects

Tomoyuki Kimoto

Publications and source records attributed to Tomoyuki Kimoto.

3 recordsLinked to original sources

PubMed2004

Coexistence of memory patterns and mixed states in a sparsely encoded associative memory model storing ultrametric patterns.

Analyzing the coexistence of memory patterns and mixed states gives important information for constructing a model for the face responsive neurons of the monkey inferior-temporal cortex. We analyzed whether the memory patterns coexist with mixed states when the sparse coding scheme is used for the associative memory model storing ultrametric patterns. For memory patterns and mixed states to coexist, there must be sufficient capacity for storing them and their threshold values must be the same. We determined that the storage capacities for all mixed states composed of correlated memory patterns diverge as 1/| f log f| (where f is the firing rate) even when the correlation of the memory patterns is infinitesimally small. We also determined that the memory patterns and the mixed states can become the equilibrium state of the model in the same threshold value. These results mean that they can coexist in this model. These findings should contribute to research on face responsive neurons in the monkey inferior-temporal cortex.

Action Potentials↗

PubMed2004

Mixed states on neural network with structural learning.

We investigated the properties of mixed states in a sparsely encoded associative memory model with a structural learning method. When mixed states are made of s memory patterns, s types of mixed states, which become equilibrium states of the model, can be generated. To investigate the properties of s types of the mixed states, we analyzed them using the statistical mechanical method. We found that the storage capacity of the memory pattern and the storage capacity of only a particular mixed state diverge at the sparse limit. We also found that the threshold value needed to recall the memory pattern is nearly equal to the threshold value needed to recall the particular mixed state. This means that the memory pattern and the particular mixed state can be made to easily coexist at the sparse limit. The properties of the model obtained by the analysis are also useful for constructing a transform-invariant recognition model.

Algorithms↗

PubMed2003

Synapse efficiency diverges due to synaptic pruning following overgrowth.

In the development of the brain, it is known that synapses are pruned following overgrowth. This pruning following overgrowth seems to be a universal phenomenon that occurs in almost all areas-visual cortex, motor area, association area, and so on. It has been shown numerically that the synapse efficiency is increased by systematic deletion. We discuss the synapse efficiency to evaluate the effect of pruning following overgrowth, and analytically show that the synapse efficiency diverges as O(|ln c|) at the limit where connecting rate c is extremely small. Under a fixed synapse number criterion, the optimal connecting rate, which maximizes memory performance, exists.

Animals↗