68 citations · 166 across the 14 of their papers we have counts for
15 papers
Spatio-temporal coarse-grained Hawkes processes
Tomoya Uda, Shinsuke Koyama
Spatio-temporal Hawkes processes are widely used to model self-exciting event data, but conventional inference methods require access to the occurrence time and location of every e…
Data-driven inference of Hopf normal form representations from oscillatory time series
Shinsuke Koyama, Ryota Kobayashi
We introduce a data-driven framework that maps noisy oscillatory time series directly onto the Hopf normal form, enabling inference of underlying dynamics without knowledge of gove…
Mechanical Optimization of Skateboard Pumping
Florian Kogelbauer, Shinsuke Koyama, Daniel E. Callan +1
Skateboarders perform a reciprocating motion on a curved ramp, called pumping, by moving their bodies up and down perpendicular to the ramp's surface. We propose a simple mechanica…
The statistical physics of discovering exogenous and endogenous factors in a chain of events
Shinsuke Koyama, Shigeru Shinomoto
Event occurrence is not only subject to the environmental changes, but is also facilitated by the events that have occurred in a system. Here, we develop a method for estimating su…
Identifying exogenous and endogenous activity in social media
Kazuki Fujita, Alexey Medvedev, Shinsuke Koyama +2
The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a ra…
Modeling event cascades using networks of additive count sequences
Shinsuke Koyama, Yoshi Fujiwara
We propose a statistical model for networks of event count sequences built on a cascade structure. We assume that each event triggers successor events, whose counts follow additive…
Projection-based filtering for stochastic reaction networks
Shinsuke Koyama
This study concerns online inference (i.e., filtering) on the state of reaction networks, conditioned on noisy and partial measurements. The difficulty in deriving the equation tha…
Macroscopic quantum information processing using spin coherent states
Tim Byrnes, Daniel Rosseau, Megha Khosla +6
Previously a new scheme of quantum information processing based on spin coherent states of two component Bose-Einstein condensates was proposed (Byrnes {\it et al.} Phys. Rev. A 85…
On the spike train variability characterized by variance-to-mean power relationship
Shinsuke Koyama
We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that t…
Fluctuation scaling in neural spike trains
Shinsuke Koyama, Ryota Kobayashi
Fluctuation scaling has been observed universally in a wide variety of phenomena. In time series that describe sequences of events, fluctuation scaling is expressed as power functi…
Temporal fluctuation scaling in nonstationary counting processes
Shinsuke Koyama
The fluctuation scaling law has universally been observed in a wide variety of phenomena. For counting processes describing the number of events occurred during time intervals, it…
Neural networks using two-component Bose-Einstein condensates
Tim Byrnes, Shinsuke Koyama, Kai Yan +1
The authors previously considered a method solving optimization problems by using a system of interconnected network of two component Bose-Einstein condensates (Byrnes, Yan, Yamamo…
On the Relation between Encoding and Decoding of Neuronal Spikes
Shinsuke Koyama
Neural coding is a field of study that concerns how sensory information is represented in the brain by networks of neurons. The link between external stimulus and neural response c…
Approximate Methods for State-Space Models
Shinsuke Koyama, Lucia Castellanos Pérez-Bolde, Cosma Rohilla Shalizi +1
State-space models provide an important body of techniques for analyzing time-series, but their use requires estimating unobserved states. The optimal estimate of the state is its…
Storage Capacity of Two-dimensional Neural Networks
Shinsuke Koyama
We investigate the maximum number of embedded patterns in the two-dimensional Hopfield model. The grand state energies of two specific network states, namely, the energies of the p…