Multiscale analysis of neural microcircuit function - using perineuronal nets to understand plasticity and network processing

Hjernen viser en utrolige evne til å tilpasse seg behovet for stadig ny læring og samtidig bevare minner og innlærte ferdigheter. De nevrale og molekylære mekanismene som ligger til grunn for balansen mellom plastisititet og stabilitet er ikke kjent. Senere tids forskning har vist at en gruppe hjerneceller som har en dempende aktivitet på andre hjerneceller også er viktige for hjerneplastistitet, læring og hukommelse. Disse hjernecellene, som kalles for PV-celler, er omsluttet av en spesiell form for ekstracellulær matriks molekyler kalt perinevrale nett. Vi og andre har vist at de perinevrale nettene begrenser plastisteten i den voksne hjerne og er viktig for å stabilisere langtidsminner. I EXTRANEURONETprosjektet skal vi undersøke samspillet mellom perinevrale nett of PV-celler under lærings- og hukommelsesprosesser. Ved hjelp av genediteringsteknikker som CRISPR/Cas9 systemet skal vi intervenere med PNN strukturen. I ulike læringsoppgaver vil vi måle endringer i adferd og hjernecelleaktivitet for å undersøke effekten av manipuleringene. Dataene vil være drivende for å utvikle teoretiske modeller for hvordan biologisk inspirerte kunstige nervecellenettverk opererer under læring. Prosjektet vil gi dyp kunnskap om mekanismer for læring og hukommelse.

What are the mechanisms underlying the brain’s immense ability to learn while being stable enough to store memories for decades? Cognition and behavior rely on a fine-tuned balance between excitation and inhibition in the neural circuits in our brain. The activity of inhibitory neurons, and in particular the fast spiking parvalbumin (PV) expressing neurons are essential for proper network function and plasticity. However, while neuronal activity directs neural microcircuit computation, elements working on longer time scales are necessary to allow modifications and stable preservations of neuron connections. I propose that a specific extracellular matrix structure called perineuronal nets (PNNs) may link these scales. Assembling around PV neurons at the end of juvenile plasticity, PNNs are a hallmark of the matured inhibitory network. We showed that removing PNNs from the adult brain resets plasticity to the juvenile state but impairs recall of old memories by destabilizing circuits. Thus, I hypothesize that fine-scale regulation of the nets contribute to the brain’s seemingly paradoxical ability to encode novel experiences while also preserving stored knowledge or skills. However, progress has been hampered by lack of tools for targeted investigations. Recent advances in gene editing, imaging and large scale recordings of neuron activity now change this and time is ripe for dissection of function. With these techniques in place in my laboratory and a team of outstanding team members, we will conduct targeted perturbations of PNNs and PV cell activity and directly assess the effects on different stages of memory processing. The goal of EXTRANEURONET is to understand how perineuronal nets modulate the PV-neural network function to support memory processing. This bold plan will renew our understanding on how neural networks operate throughout vast temporal and spatial scales explaining cognition and establish new research directions to probe neural circuits in vivo.