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SUMMARY:Neural circuit mechanisms of learning and attentional task-switchi
 ng during visually-guided behaviour in mice - Dr Jasper Poort ( Department
  of Psychology\, Cambridge)
DTSTART:20190215T100000Z
DTEND:20190215T110000Z
UID:TALK120106@talks.cam.ac.uk
CONTACT:Rodrigo Echeveste
DESCRIPTION:Abstract: We found that neural responses in the mouse primary 
 visual cortex (V1) become increasingly selective for relevant visual input
 \, by repeatedly imaging cells using 2‐photon calcium imaging while mice
  learned a visual discrimination task (Poort et al.\, 2015\, Neuron). Howe
 ver\, it is unclear how learning reorganises the activity of different cel
 l types\, including excitatory pyramidal neurons and different classes of 
 GABAergic interneurons. Although pyramidal cells provide the output from t
 he local circuit to other cortical areas\, different interneuron classes i
 nhibit pyramidal cells as well as each other\, and exert a powerful influe
 nce on circuit activity. We therefore simultaneously measured responses in
  V1 of pyramidal cells and different interneuron types. We find that learn
 ing leads to changes in the selectivity and co‐activation patterns acros
 s multiple cell classes\, and that increased stimulus‐specific inhibitio
 n\, especially in parvalbumin cells\, can contribute to selective processi
 ng of relevant objects (Khan et al.\, 2018\, Nature Neuroscience). To dete
 rmine whether these changes were specific to learning\, we trained the sam
 e mice to switch between a visual and an olfactory discrimination task to 
 compare neural responses when animals were attending or ignoring the same 
 visual stimuli. We found that effects of learning and task-switching on th
 e response selectivity of the same cells were largely uncorrelated. Learni
 ng and task-switching also differentially affected the interactions betwee
 n different cell classes. These results suggest there are distinct mechani
 sms underlying increased discriminability of relevant sensory stimuli acro
 ss longer and shorter time scales. In recent work\, we started to extend o
 ur experiments to freely moving mice in more complex and natural environme
 nts. One challenge for visual neuroscience is that it was so far not possi
 ble to track detailed aspects of eye and head movements. We recently publi
 shed a new and open-source method for head-mounted video tracking in mice\
 , which we combined with motion sensors to measure head movement and multi
 electrode electrophysiological recordings in visual cortex (Meyer et al.\,
  Neuron 2018). I will present preliminary results that indicate how this m
 ethod may help to understand visually-guided behaviour in freely behaving 
 mice.
LOCATION:Cambridge University Engineering Department\, CBL\, BE4-38 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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