BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook 12.0 MIMEDIR//EN
VERSION:2.0
METHOD:PUBLISH
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VTIMEZONE
TZID:GMT Standard Time
BEGIN:STANDARD
DTSTART:16010101T020000
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=-1SU;BYMONTH=10
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:16010101T010000
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=-1SU;BYMONTH=3
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
CLASS:PUBLIC
CREATED:20091109T101015Z
DESCRIPTION:Speaker: Rüdiger Thul University of Nottingham\n\nTopic: Noisy thresholds - casting neural dynamics in a new light\n<p> <span style="color: rgb(32, 31, 30); font-family: "Segoe UI", "Segoe UI Web (West European)", -apple-system, "system-ui", Roboto, "Helvetica Neue", sans-serif; font-size: 15px;">Neural dynamics epitomises excitability and hence is dramatically shaped by the interplay between molecular fluctuations and the firing threshold. The most common approach to modelling stochastic neural dynamics is via stochastic differential equations (SDEs) as exemplified by the stochastic version of the seminal integrate-and-fire (IF) model. Here, a stochastic subthreshold process is integrated until it reaches a pre-defined threshold, which signals the onset of a neuronal spike. Typically, the firing threshold is a deterministic function. However, experimental evidicene suggests that the firing threshold itself is random. We incorporate this finding into a novel class of stochastic IF models in which the subthreshold process is deterministic, but the dynamics of the firing threshold is governed by an SDE. We demonstrate that this model exhibits a nonlinear dependence of the firing rate as a function of the noise strength and explain this behaviour known as inverse stochastic resonance semi-analytically by computing the full first passage time (FPT) probability distribution. This setup also allows us to generalise the influential Rice series for FPT calculations to non-differentiable Gaussian processes. In the last part of my talk, I will apply the concept of noisy thresholds to neural fields and demonstrate its versatility by constructing travelling fronts, stationary bumps and multi-bumps and highlighting non-trivial linear stability properties of bumps.</span></p>
DTSTART;TZID=GMT Standard Time:20220315T13:30:00
DTEND;TZID=GMT Standard Time:20220315T14:30:00
TZID=GMT Standard Time
DTSTAMP:20100109T093305Z
LAST-MODIFIED:20091109T101015Z
LOCATION:https://Universityofexeter.zoom.us/j/98128535238?pwd=b1NNR1MwV295VHRWWFh4b1NDTjJoZz09 Meeting ID: 981 2853 5238 Password: 561487
PRIORITY:5
SEQUENCE:0
SUMMARY;LANGUAGE=en-gb:Noisy thresholds - casting neural dynamics in a new light
TRANSP:OPAQUE
UID:040000008200E00074C5B7101A82E008000000008062306C6261CA01000000000000000
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
X-MICROSOFT-CDO-IMPORTANCE:1
X-MICROSOFT-DISALLOW-COUNTER:FALSE
X-MS-OLK-ALLOWEXTERNCHECK:TRUE
X-MS-OLK-AUTOFILLLOCATION:FALSE
X-MS-OLK-CONFTYPE:0
BEGIN:VALARM
TRIGGER:-PT1440M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
END:VCALENDAR