HEBBIANNEWSPINES

Visualizing the structural synaptic memory trace: presynaptic partners of newly formed spines

 Coordinatore MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN E.V. 

 Organization address address: Hofgartenstrasse 8
city: MUENCHEN
postcode: 80539

contact info
Titolo: Ms.
Nome: Annette
Cognome: Starke
Email: send email
Telefono: +49 89 8578 3776

 Nazionalità Coordinatore Germany [DE]
 Totale costo 168˙863 €
 EC contributo 168˙863 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-2010-IEF
 Funding Scheme MC-IEF
 Anno di inizio 2011
 Periodo (anno-mese-giorno) 2011-04-01   -   2013-03-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN E.V.

 Organization address address: Hofgartenstrasse 8
city: MUENCHEN
postcode: 80539

contact info
Titolo: Ms.
Nome: Annette
Cognome: Starke
Email: send email
Telefono: +49 89 8578 3776

DE (MUENCHEN) coordinator 168˙863.20

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

synapses    active    functionally    ca    spines    newly    techniques    spine    forming    vivo    maintenance    vitro    presynaptic    memories    formed    functional    indeed    memory    stable    imaging    neuron   

 Obiettivo del progetto (Objective)

'The formation and elimination of spine synapses can be visualized both in vitro as well as in vivo using advanced imaging techniques. It is known that the number of dendritic spines changes both during development as well as during the acquisition of new memories. However, up to now these observations are entirely correlative. It is unclear if the presynaptic neuron forming a functional contact on a newly formed spine indeed was the one that showed correlated activity with the postsynaptic cell as would be required for a learning mechanism following classical Hebbian plasticity. In fact, since it has never been shown what information is transmitted via a newly formed spine synapse in vivo, it has yet to be demonstrated that new long-term stable spines indeed could carry a memory trace related to a specific previous experience. Here, I propose to functionally identify the presynaptic partners of newly formed spine synapses. I will assess if the information carried by a new spine can be predicted from readily observable global changes in neuronal activation strength in a well-defined sensory deprivation paradigm in vivo. Furthermore, I will study whether indeed neurons that ‘fire together’ also ‘wire together’ by forming new stable spine synapses in vitro. I will develop chronic single spine Ca2 imaging techniques and novel optophysiological tools like a dual-color genetically encoded Ca2 indicator (GCaMP3-based) and an activity-dependent structural tag (TetTag-based) of active axons. I aim to functionally define the active presynaptic population in during visual and electrical stimulation in vivo and in vitro. Furthermore, I will assess if deletion of proteins involved in the functional maintenance of synaptic strengthening (LTP-maintenance) prevents the formation and/or stabilization of new synapses between defined coactive partners. This work will take our understanding of memory formation forward while providing a range of broadly applicable new techniques.'

Introduzione (Teaser)

Formation of long-term memories is a complex process that is still not understood at the individual neuron level. An EU-funded research project worked to change the status quo.

Altri progetti dello stesso programma (FP7-PEOPLE)

MICRORNA FBDD (2011)

Towards microRNA modulators by fragment-based drug discovery (FBDD) approaches

Read More  

NUMIRDT (2011)

NUCLEOLAR-DEPENDENT SECRETION OF MICRORNA IN A MODEL OF DOXORUBICIN AND TRASTUZUMAB CARDIOMYOPATHY

Read More  

DEVELOP‐LEARNING (2014)

Developmental trajectories for model-free and model-based reinforcement learning: computational and neural bases

Read More