<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body style="overflow-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div style="font-family: HelveticaNeue;"><font face="HelveticaNeue">Dear Soft Matter & Complex Systems Colleagues and Friends,</font></div><div style="font-family: HelveticaNeue;"><font face="HelveticaNeue"><br></font></div><div style="font-family: HelveticaNeue;"><div><font face="HelveticaNeue">On Friday 24 November 2023 at 9:30 AM at the UW Faculty of Physics (Pasteura 5, Warsaw; room 1.40) we are hosting a seminar during which</font></div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;"><b><br></b></div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;"><font face="HelveticaNeue"><b>Daniel Wójcik </b>(Nencki </font>Institute for Experimental Biology, PAS)</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;"><font face="HelveticaNeue"><br></font></div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><font face="HelveticaNeue">will give a talk</font></div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;"><span style="caret-color: rgb(68, 68, 68);"><br></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;"><b>Statistical framework for identification of individual and social aspects of animal learning in intelligent cages</b></div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;"><br></div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><div style="margin: 0px; font-stretch: normal; line-height: normal;"><div style="margin: 0px; font-stretch: normal; line-height: normal;"><h2 style="margin: 6px 0px 12px; padding: 0px; caret-color: rgb(68, 68, 68);"><b style="font-size: 12px;">Abstract</b></h2></div></div></div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><div style="margin: 0px; font-stretch: normal; line-height: normal;"><div style="margin: 0px; font-stretch: normal; line-height: normal;"><div style="margin: 0px; font-stretch: normal; line-height: normal;"><div style="margin: 0px; font-stretch: normal; line-height: normal;"><div style="margin: 0px; font-stretch: normal; line-height: normal;">Several recent cage designs support studies of multiple animals housed for weeks with minimal human intervention in a single or multiple compartments where they can interact with cage elements and with each other, and their behavior can be tracked in various ways. Here we focus on Intellicage system where up to 14 female mice housed together can be identified with an RFID transponder interacting with intelligent corners providing reward, and the behavior is described in terms of discrete events. We present a general conceptual, analytical and computational framework for stochastic description, analysis and modeling of data from such cages. This framework combines the theory of point processes (as used in spike train analysis) with reinforcement learning models. We demonstrate how individual and social aspects of learning can be identified within the data, and show different specific approaches which facilitate study of effects of the whole group on an animal or formation of a hierarchy of social effects in group learning. The results of the analysis are validated with equivalent simulated data.</div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><br></div><div style="margin: 0px; font-stretch: normal; line-height: normal;">To illustrate this conceptual framework and our analytical approach we designed an experimental paradigm where rewards are offered depending on an arbitrary assignment of an animal to one of two groups, “majority” or “minority”. The two groups were assigned different locations with reward availability, changing in consecutive phases of the experiment. We show that the data support importance of the social effects in animal learning of the reward and may also be used to identify a social structure within the group. Corresponding generative models can be used for validation of various analytical methods and for prediction of mice behavior.</div></div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><br></div></div></div></div></div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><font face="HelveticaNeue">We warmly welcome everyone to attend the talk and the Soft Matter Coffee Break after the seminar, held in room 2.63 (2nd floor).</font></div></div><div style="font-family: HelveticaNeue;"><font face="HelveticaNeue"><br></font></div><div style="font-family: HelveticaNeue;"><div dir="auto" style="overflow-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div style="margin: 0px; font-stretch: normal; line-height: normal;"><font face="HelveticaNeue">Maria Ekiel-Jeżewska</font></div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><font face="HelveticaNeue">Maciej Lisicki</font></div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><font face="HelveticaNeue">Piotr Szymczak</font></div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><font face="HelveticaNeue">Panagiotis Theodorakis</font></div><div style="margin: 0px; font-stretch: normal; line-height: normal;"><font face="HelveticaNeue">Marek Trippenbach</font></div></div></div></body></html>