Timing and the cerebellum (and the VOR) Neurophysiology of systems 2010
Asymmetry in learning in the reverse direction Full recovery from UP using DOWN: initial return to naïve values within 10 minutes, and then similar learning curve to naïve. Slow, partial recovery from DOWN using UP. Results cannot be explained on the basis of weaker learning signals (retinal slip measured).
Phase data Masking predicts no changes in phase in Fig. D. Reversal predicts change of Phase & amplitude along the same trajectory but in opposite directions Different learning mechanisms) Gain-up then gain-down > complete reversal Gain-down then gain-up > partial reversal and partial masking
A possible explanation for learning asymmetry Model A enables only masking!! Model B suggests different plasticity mechanisms for an increase and a decrease in VOR gain.
Cerebellar involvement in timing Spike timing, Motor coordination, Working memorysleep / wake cycle coincidence detection, sensory expectation, 1 ms 10 ms 50 ms 100 ms 500 ms 1 s 5 s 10 s Day
General framework of neural mechanisms for timing a) A particular neural region is uniquely capable of representing temporal information b) Representation of temporal information results from the interactions within a set of neural structures. c) Temporal information is computed within the neural structures required for a particular task. Ivry and Spencer Curr. Opin. 04
Multi-joint coordination and timing D. Timmann et al, J. Neurophysiol Vol. 82 No. 1 July 1999, pp. 103-114 114
Cerebellar activation during eyelid conditioning Purkinje cells DCN cells Eyelid response Medina, et al. Current Opinion in Neurobiology 2000
LTD in the cerebellar cortex is essential for learning-dependent timing Koekkoek, et al. Science 2003
Valentino Breitenberg There is information in the structure; neuroanatomy may profitably be consulted when other kinds of evidence fail to provide decisive clues.
The basic anatomical unit according to Braitenberg The most numerous kind of interneurons, the granule cells, simply shift signals through their axonal branches (the PFs) from their origin in the input in opposite directions along the laterolateral coordinate of the cortex at a low and fairly constant speed. we may assign a merely ancillary role to the fibers oriented in the other, the anteroposterior direction. They simply suppress activity on either side of a particularly active beam of parallel fibers. This view implies that the one-dimensional beam is the computational unit in the cerebellar cortex The anteroposterior extension must be related to the number of such beams.
Parallel fiber beams Dow et al. J Neurophys 1949. Action potentials of cerebellar cortex in response to local electrical stimulation. Observed by many labs using many different techniques over the past decades.
Parallel fiber activity propagates at constant velocity in vivo M A P L
Parallel fiber diameter is relatively narrowly distributed Conduction velocity is relatively constant! Sultan F, Neuroscience Letters 280 (2000)
Another fact from comparative anatomy and a speculation Relative dendritic density in cerebral cortex is about 1:1000. In cerebellum (for Purkinje cells) it is 1:1: Purkinje cells keep out of each other s way. The arrangement seems to imply that for each dendritic tree there is a predetermined place along the length of the PFs at which it is to receive its synaptic input. The idea that what really matters is the time at which the synapses become active is old but still enticing.
The original Braitenberg timing V1: 1958/1961/1965 hypothesis Parallel fibers are generators of time delays between the activation of different muscles involved in one movement. Fall of the theory: movements take 200-300 ms, a PF beam will last for only 10 ms (a few mm at about 0.2-0.5 m/s)
The revised Braitenberg tidal wave hypothesis V2: 1983/1987 Cerebellar cortex is tuned to granule cell inputs which propagate at a specific velocity Lateral inhibition of neighboring beams : winner takes all?
Somatotopy in cerebellum: fractured and multiple representations Adrian 1943 Bower et al.
Tuning to velocity: a simple model Vo - is the velocity of conduction in the parallel fibers d - is the spread of the signal along the parallel fibers Δx is the length of the stretch over which the stimulus was presented
What is the length of a beam? Tidal wave reaches a plateau after traveling the average length of a PF. It can, though, continue traveling at the plateau for the entire length of the folium! The longest, unbroken folia can thus accommodate about 200 ms.
So what is the cerebellum doing according to Braitenberg? Motor cortex initiates movement, updating cerebellum ahead of time (-100 ms) Cerebellum receives a mixture of motor commands and somatosensory feedback Input reaches many beams, but reaches only one in the appropriate GC velocity; the PCs in this beam can thus contribute to the motor output
How to generate precise timing? Transmission lines Oscillators Spectral models 10 ms 50 ms 100 ms
Neurons of the inferior olive Ramon Y Cajal
Olivary neurons exhibit subthreshold oscillations 20 mv 2 sec 0.5 sec
In vivo intracellular recordings from olivary neurons
Drawbacks Very low oscillation frequency 1-2 Hz Not clear if exists in behaving animals
Patterns of precise timing Using the oscillatory nature of olivary neurons to generate specific patterns of activity in Purkinje cells
Few facts we need to know about olivary neurons They are electrically coupled via gap junctions
Electrical coupling between olivary neurons Cell 1 20 mv Cell 2 Cell 2 5 mv Cell 1 200 ms Cell 1 Cell 2 Cell 1 Cell 2 20 mv 2 mv 100 ms
Oscillations are synchronous in pairs of neurons 1 1 sec 10 mv 5 7 9 0-1 -0.2 0 0.2 1 0 2 4 6-1 -0.4 0 0.4
Zero lag network oscillations 1 0 0.2 sec -1-0.2 0 0.2 Time (sec)
Phase shift in the oscillations generates a propagating wave 1 0 0.5 sec -1-0.4 0 0.4 Time (sec)
Coupling vs. oscillations Placantonakis, et al JNS 06
Is there a mechanism for controlling the oscillations?
GABA modulates the subthreshold oscillations V/I GABA 1 sec 20mV
GABA blockers induce oscillations
Synaptic organization of olivary glumeruli (De Zeeuw et al. 1998)
Is temporal accuracy limited by IO cycle? ΔT = 1/f
The olivo-cerebellar timing circuit A working hypothesis
A putative mechanism for temporal pattern selectivity Purkinje cells fire DCN neurons are silenced Coupling in the inferior olive increases selectively