1 / 12

Eye hand coordination

Spatiotemporal emergence of movement plans in the posterior parietal cortex during eye-hand coordination Arpan Banerjee NYU Sloan-Swartz Meeting 2008. Eye hand coordination. Stimulus onset. Target selection time. Reaction time. Plan. Time. Working Hypotheses.

davida
Télécharger la présentation

Eye hand coordination

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Spatiotemporal emergence of movement plans in the posterior parietal cortex during eye-hand coordinationArpan BanerjeeNYUSloan-Swartz Meeting 2008

  2. Eye hand coordination Stimulus onset Target selection time Reaction time Plan Time

  3. Working Hypotheses Either, eye-hand coordination requires shared target representations for saccades and reaches which may lead to correlation in target selection times across brain areas Or, coordination is primarily mediated by coupling between the movement plans after the target is selected

  4. Recordings of spiking and LFP activity Spikes Rate (Hz) Time (msec) Fields Voltage Parietal reach region (PRR) Lateral intra-parietal area (LIP) Dean (In progress) Lazzaro (In progress)

  5. Overview Primary Goal: • Unified analysis of spikes and fields to detect target selection times. Methods • Statistical modeling of the specific patterns (variates) encoding movement directions. • Decode the movement direction (and Target selection time) from the pattern (variate) given new data via likelihood ratio tests Future direction: RT

  6. Modelling of LFP activity Neural recording area Autoregressive modelling with external input

  7. Decoding Input • Obtain the a’s (AR coefficients) by Burg’s algorithm • Model order (p) selection using Akaike criterion • Input decoded • Amplitude and latency estimated trial by trial by maximizing the posterior

  8. Likelihood estimation • Neural signal modeling for each direction of movement • Accumulation of logarithm of likelihood ratio is obtained

  9. Target selection time estimation • Accumulated log-likelihood ratios Fields 104 msec

  10. Future directions • Application of Sequential probability ratio tests to determine the target selection time from accumulated ratios Wald 1940 • Time variation of A R coefficients. • Conditional intensity process will be considered for spike activity • Extension to multivariate recordings

  11. Summary • Target selection time can be computed simultaneously from fields and spikes in a unified framework. • Covariation of target selection times across different brain areas and with saccade and reach reaction times can be used to understand the coupling between eye and hand movements.

  12. Acknowledgements • Dr. Bijan Pesaran • Stephanie Lazzaro • Dr. Heather Dean • Boris Revechkis • Sam Gershman • Eva Tsui • Adam Weiss • The Swartz Foundation Thank you!

More Related