PEARLThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.http://pearl.plymouth.ac.uk:802017-02-20T17:40:25Z2017-02-20T17:40:25ZEasy Grocery: 3D visualization in e-grocerySomerville, JStuart, LJBarlow, Nhttp://hdl.handle.net/10026.1/85482017-02-20T14:44:31Z2006-01-01T00:00:00ZEasy Grocery: 3D visualization in e-grocery
Somerville, J; Stuart, LJ; Barlow, N
2006-01-01T00:00:00ZVisualisation of synchronous firing in multi-dimensional spike trains.Stuart, LWalter, MBorisyuk, Rhttp://hdl.handle.net/10026.1/85472017-02-20T14:02:23Z2002-10-01T00:00:00ZVisualisation of synchronous firing in multi-dimensional spike trains.
Stuart, L; Walter, M; Borisyuk, R
The gravity transform algorithm is used to study the dependencies in firing of multi-dimensional spike trains. The pros and cons of this algorithm are discussed and the necessity for improved representation of output data is demonstrated. Parallel coordinates are introduced to visualise the results of the gravity transform and principal component analysis (PCA) is used to reduce the quantity of data represented whilst minimising loss of information.
2002-10-01T00:00:00ZThe Representation of Neural Data using VisualizationStuart, EJhttp://hdl.handle.net/10026.1/85462017-02-20T14:01:48Z2004-06-10T00:00:00ZThe Representation of Neural Data using Visualization
Stuart, EJ
2004-06-10T00:00:00ZThe correlation grid: analysis of synchronous spiking in multi-dimensional spike train data and identification of feasible connection architectures.Stuart, LWalter, MBorisyuk, Rhttp://hdl.handle.net/10026.1/85452017-02-20T14:01:04Z2005-01-01T00:00:00ZThe correlation grid: analysis of synchronous spiking in multi-dimensional spike train data and identification of feasible connection architectures.
Stuart, L; Walter, M; Borisyuk, R
This paper presents a visualization technique specifically designed to support the analysis of synchronous firings in multiple, simultaneously recorded, spike trains. This technique, called the correlation grid, enables investigators to identify groups of spike trains, where each pair of spike trains has a high probability of generating spikes approximately simultaneously or within a constant time shift. Moreover, the correlation grid was developed to help solve the following reverse problem: identification of the connection architecture between spike train generating units, which may produce a spike train dataset similar to the one under analysis. To demonstrate the efficacy of this approach, results are presented from a study of three simulated, noisy, spike train datasets. The parameters of the simulated neurons were chosen to reflect the typical characteristics of cortical pyramidal neurons. The schemes of neuronal connections were not known to the analysts. Nevertheless, the correlation grid enabled the analysts to find the correct connection architecture for each of these three data sets.
2005-01-01T00:00:00Z