The workshop ended successfully. Thank you for participating intensive
discussion (pictures below).
- Date: June 20th (Wed.) 2012
- Place: Faculty of Science building #5, Room #115, Kyoto University
Shigeru Shinomoto (Dept Physics, Kyoto University)
- Participation to the workshop is free.
- Participation to the dinner should be registered to Yasuhiro Mochizuki (Dept Physics, Kyoto University).
This workshop is cosponsored by a Grant-in-Aid for Scientific Research
on Mesoscopic neurocircuitry: towards understanding of the functional and
structural basis of brain information processing.
Workshop program June 20th (Wed.) 2012
- ♦Rob Kass (CMU)
- A Framework for Evaluating Pairwise and
Multiway Synchrony Among Stimulus-Driven Neurons
- [Abstract] Several authors have discussed previously the use of loglinear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual loglinear modeling techniques, however, do not allow for time-varying firing rates that typically appear in stimulus-driven (or action-driven) neurons, nor do they incorporate non-Poisson history effects or covariate effects. We generalize the usual approach, combining point process regression models of individual-neuron activity with loglinear models of multiway synchronous interaction. The methods are illustrated with results found in spike trains recorded simultaneously from primary visual cortex. We then go on to assess the amount of data needed to reliably detect multiway spiking. We also show how more than 2,000 pairs of well-isolated neurons from a Utah array may be screened to find reliable instances of synchrony. This is joint work with Ryan Kelly (Google), James Scott (U Texas), and Matt Smith (U Pittsburgh).
- ♦Hideaki Shimazaki (RIKEN)
- Tracking Dynamic Neural Interactions in
Awake Behaving Animals
- [Abstract] Neurons embedded in a network are correlated, and can produce
synchronous spiking activities with millisecond precision. It is likely
that these correlated activities organize dynamically during behavior and
cognition, and this may be independent from spike rates of individual neurons.
Consequently current analysis tools must be extended so that they can directly
estimate time-varying neural interactions. The log-linear model is known
to be useful for analysis of the correlated spiking activities but is limited
to stationary data. In our approach, we developed a `state-space log-linear
modelfthat can estimate ever-changing neural interactions: this method
is an extension of the familiar Kalman filter which can track systemfs
parameters as used in, e.g., automotive navigation systems. We applied
this method to three neurons recorded from the primary motor cortex of
a monkey engaged in a delayed motor task (data from Riehle et al., Science,1997).
We found that depending on the behavioral demands of the task these neurons
dynamically organized into a group which was characterized by the presence
of higher-order (triple-wise) interaction. There was, however, no noticeable
change in their firing rates. These results demonstrate that time-varying
higher-order analysis allows us to detect subsets of correlated neurons
that may belong to a larger set of neurons comprising a cell assembly.
This is a collaboration work with Shun-ichi Amari (RIKEN), Emery N. Brown
(MIT), and Sonja Gruen (Julich). Original paper: Shimazaki et al., PLoS
CB 8(3): e1002385