Effective implementation of [Formula: see text]-regularised compressed sensing with chaotic-amplitude-controlled coherent Ising machines.

Coherent Ising machine (CIM) is a network of optical parametric oscillators that can solve large-scale combinatorial optimisation problems by finding the ground state of an Ising Hamiltonian. As a practical application of CIM, Aonishi et al., proposed a quantum-classical hybrid system to solve optimisation problems of [Formula: see text]-regularisation-based compressed sensing.

Integrated analysis of cell shape and movement in moving frame.

The cell's movement and morphological change are two interrelated cellular processes. An integrated analysis is needed to explore the relationship between them. However, it has been challenging to investigate them as a whole.

Statistical mechanics of an error correcting code using monotonic and nonmonotonic treelike multilayer perceptrons.

An error correcting code using a treelike multilayer perceptron is proposed. An original message s0 is encoded into a codeword y0 using a treelike committee machine (committee tree) or a treelike parity machine (parity tree). Based on these architectures, several schemes featuring monotonic or nonmonotonic units are introduced.

Statistical mechanics of lossy compression for nonmonotonic multilayer perceptrons.

A lossy data compression scheme for uniformly biased Boolean messages is investigated via statistical mechanics techniques. We utilize a treelike committee machine (committee tree) and a treelike parity machine (parity tree) whose transfer functions are nonmonotonic. The scheme performance at the infinite code length limit is analyzed using the replica method.

Statistical mechanics of lossy compression using multilayer perceptrons.

Statistical mechanics is applied to lossy compression using multilayer perceptrons for unbiased Boolean messages. We utilize a treelike committee machine (committee tree) and treelike parity machine (parity tree) whose transfer functions are monotonic. For compression using a committee tree, a lower bound of achievable distortion becomes small as the number of hidden units K increases.

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.