Realistic modeling applied to cerebellar function

R e a l i s t i c n e t w o r k a n d s i n g l e c e l l m o d e l i n g c a n b e p o w e r f u l t o o l s t o s t u d y t h e o p e r a t i o n o f r e a l bra in s t ruc tures . We have used rea l i s t i c s imula t ion approaches based on fu l ly ac t ive compartmental models to study the cerebel lum. Here I summarize some of our recent results that address cerebe l lar l earning . Assuming that longterm depress ion (LTD) i s the bas i s o f l earn ing in cerebellar Purkinje c e l l s , two quest ions arise: w h a t i s a p a r a l l e l f i b e r p a t t e r n a n d h o w c a n t h e occurrence of learned patterns be decoded from the Purkinje cel l spike train? We have used deta i l ed network s imulat ions o f the granular layer using conductance based models of granule and Golgi ce l l s to s tudy the patterns of paral le l f iber a c t i v i t y in response to natural m o s s y f i b e r s t i m u l a t i o n . B a s e d o n these s imul a t i o n s w e p r e d i c t a sparse temporal coding b y p a r a l l e l f i b e r s o f c o m p l e x s p a t i a l m a p s o f m o s s y f iber input . We have studied for the f irst t ime pattern recogn i t ion by Purkinje c e l l s in a rea l i s t i c c o n t e x t , i . e . how to recognize the ef fect o f d e p r e s s e d s y n a p s e s o n a s p i k i n g n e u r o n w h i c h i s spontaneous ly ac t ive most o f the t ime . Us ing our standard Purkinje c e l l model we predict that para l l e l f iber pa t t erns tha t have undergone LTD cause an increase in the Purkinje c e l l output instead o f the decrease assumed by m o s t cerebel lar learning theories .