Research Projects and Publications Past and Present
We are working hard both on the science and constructing this page -last updated May 15, 2008.
Input integration in cerebellar cortical Purkinje cells
Purkinje cells are the only projection neuron out of the cerebellar cortex. They are one of the largest neurons in the brain, and they certainly hold the record in the number of input connections they receive: about 200,000! They also have very interesting intrinsic dynamics, which allow bursting, bistability, and plateau potentials. The intrinsic dynamics alone lead to high firing rates of action potentials. And as another kicker, all this high-frequency output from the Purkinje cells is inhibitory onto the tartget neurons in the deep cerebellar nuclei. How does all this contribute to the input-output function used during motor behavior?
Link to a figure showing spontaneous Purkinje cell spiking in vitro and in vivo.
And real physiologists like to listen to their cells, too.
Publications
Jaeger D, Bower JM (1994) Prolonged responses in rat cerebellar Purkinje cells following activation of the granule cell layer: an intracellular in vitro and in vivo investigation. Exp Brain Res 100:200-14. Abstract
Jaeger D, De Schutter E, Bower JM (1997) The role of synaptic and voltage-gated currents in the control of Purkinje cell spiking: a modeling study. J Neurosci 17:91-106. Abstract Full PDF Text
Jaeger D, Bower JM (1999) Synaptic control of spiking in cerebellar Purkinje cells: dynamic current clamp based on model conductances. J Neurosci 19:6090-101. Abstract Full PDF Text
Santamaria F, Jaeger D, De Schutter E, Bower JM (2002) Modulatory effects of parallel fiber and molecular layer interneuron synaptic activity on purkinje cell responses to ascending segment input: a modeling study. J Comput Neurosci 13:217-35. Abstract Full PDF Text
Jaeger, D.. (2003) No parallel fiber volleys in the cerebellar cortex: Evidence from cross-correlation analysis between Purkinje cells in a computer model and in recordings from anesthetized rats. J. comp. Neurosci., 14(3):311-27 Abstract Full PDF Text
Kreiner, L., Jaeger, D. (2004) Synaptic shunting by a baseline of synaptic conductances modulates responses to inhibitory input volleys in cerebellar Purkinje cells. Cerebellum. 3: 112-125. Abstract Full PDF Text
Input integration in cerebellar cortical interneurons
Interneurons in cerebellar cortex inhibit Purkinje cells. Since inhibiting Purkinje cells means disinhibiting the deep cerebellar nuclei - thus activating motor behavior - these little neurons may hold an important key to the understanding of how the cerebellum works. Though they are little, they have input connections that are able to learn based on experience, and a single interneuron can make a noticeable dent in Purkinje cell spiking. But what conditions make an interneuron spring into action?
Publications
Suter, K.J., Jaeger, D. (2004) Reliable control of spike rate and spike timing by rapid input transients in cerebellar stellate cells. Neuroscience, 124: 305-317 Abstract Full PDF Text
Input integration in the deep cerebellar nuclei
The deep cerebellar nuclei are the final bottleneck of cerebellar processing. All output from Purkinje cells goes through here, but there are many fewer neurons present than there are Purkinje cells. How does the convergence of many Purkinje cells get integrated to generate meaningful output from the cerebellum at large?
Publications
Gauck V, Jaeger D (2000) The control of rate and timing of spikes in the deep cerebellar nuclei by inhibition. J Neurosci 20:3006-16. Abstract Full PDF Text
Gauck V, Thomann M, Jaeger D, Borst A (2001) Spatial distribution of low- and high-voltage-activated calcium currents in neurons of the deep cerebellar nuclei. J Neurosci 21:RC158. Abstract Full PDF Text
Gauck, V, Jaeger, D. (2003) The contribution of NMDA and AMPA conductances to the control of spiking in neurons of the deep cerebellar nuclei. J. Neurosci. 23: 8109-8118. Abstract Full PDF Text
Steuber V., De Schutter, E. and Jaeger, D. JM (2004) Passive models of neurons in the deep cerebellar nuclei: the effect of reconstruction errors, Neurocomputing 58-60: 563-568 Abstract Full PDF Text
Rowland N.C., and Jaeger, D. (2005) Coding of Tactile Reponse Properties in the Rat Deep Cerebellar Nuclei. J. Neurophysiol. 94: 1236–1251 Abstract Full PDF Text
Rowland NC, Jaeger D. (2008) Sensory responses in DCN neurons reflect ongoing network integration of multiple pathways in rat cerebellum. J. Neurophysiol. 99:704-717 Abstract Full PDF Text
Recent Meeting Presentations
D. Jaeger, E. De Schutter, V. Steuber. A computational study of rebound responses in a conductance-based model of a deep cerebellar nucleus cell Program No. 179.11. 2005 Abstract Viewer/Itinerary Planner. Washington, DC: Society for Neuroscience, 2005. Online. Abstract
T.D. Sangrey, D. Jaeger. Currents underlying hyperpolarization-induced rebound spiking in deep cerebellar nuclei neurons Program No. 179.10. 2005 Abstract Viewer/Itinerary Planner. Washington, DC: Society for Neuroscience, 2005. Online. Abstract
Properties of medium spiny neurons in the striatum
The medium spiny neuron is the gateway to basal ganglia processing. A very sluggish gateway, however. These neurons fire only very infrequently. Is it because they inhibit each other? Maybe not. Somehow they pull out important activity patterns from cerebral cortex and inform the basal ganglia what is going on up there. But how?
Publications
Jaeger D, Kita H, Wilson CJ (1994) Surround inhibition among projection neurons is weak or nonexistent in the rat neostriatum. J Neurophysiol 72:2555-8. Abstract
Stern EA, Jaeger D, Wilson CJ (1998) Membrane potential synchrony of simultaneously recorded striatal spiny neurons in vivo. Nature 394:475-8. Abstract Full PDF Text
Jaeger D. (1998) The control of spiking by synaptic input in striatal and pallidal neurons IBAGS Abstract Chapter (PDF)
Properties of neurons in the globus pallidus
The globus pallidus (GP) provides the mysterious centerpiece of basal ganglia processing. It is connected backwards and forwards with many other structures, and it is suggested to be thrown off-balance in Parkinson's disease. GP neurons fire fast during behavior, and they inhibit their target. Somehow reminds one of Purkinje cells, no? However, the inputs have completely different properties here, and we need clues as to what the GP is trying to extract from this situation. Like so many systems in the brain, plasticity in the connections is something important to look out for, too.
Data traces showing short term synaptic plasticity in the globus pallidus
Rotating detailed model of GP neuron firing action potentials (Beware, large file).
Publications
Hanson JE, Jaeger D (2002) Short-term plasticity shapes the response to simulated normal and parkinsonian input patterns in the globus pallidus. J Neurosci 22:5164-72. Abstract Full PDF Text
Hanson, J.E., Smith, Y., Jaeger, D. (2004) Sodium channels and dendritic spike initiation at excitatory synapses in globus pallidus neurons. J. Neurosci. 24:329-340 Abstract Full PDF Text
C. Günay, J. R. Edgerton, D. Jaeger (2008) Channel Density Distributions Explain Spiking Variability in the Globus Pallidus: A Combined Physiology and Computer Simulation Database Approach. J Neurosci. 28(30):7476-7491; doi:10.1523/JNEUROSCI.4198-07.2008 Abstract Full PDF Text Model and database files
Recent Meeting Presentations
N. W. Schultheiss, J. R. Edgerton, D. Jaeger. Phase response curve analysis of a globus pallidus neuron model in the presence of randomly timed excitatory and inhibitory synaptic backgrounds. Program No. 587.18. 2007 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2007. Online. Abstract
S. Li, D. Jaeger. Conductances contributing to pacemaking activity in entopeduncular nucleus neurons. Program No. 515.15. 2007 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2007. Online. Abstract
C. Günay, J. R. Edgerton, D. Jaeger. Interplay between maximal conductance and voltage half-activation parameters in a multicompartmental globus pallidus model neuron: comparison between two model databases. Program No. 477.16. 2007 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2007. Online. Abstract
Properties of of neurons in the Substantia Nigra, pars
reticulata
And one should neglect the tail end of basal ganglia processing, which the SNpr share with the entopeduncular nucleus (called GPi in primates). The neurons here look and fire much like GP neurons. But there are some interesting differences, and is unlikely they perform the same computation. These guys are busy, too, since there are very few of them, and there are so many inputs from striatum to process. To keep life in the active zone, they also take more exciting messages from subthalamic nucleus. How do excitation and inhibition jive
Meeting Presentations
A. Delgado-Reyes*; D. Jaeger (2001) EXCITATORY SYNAPTIC RESPONSES AND SHORT-TERM PLASTICITY IN SUBSTANTIA NIGRA GABAERGIC NEURONS Abstract
Basal Ganglia activity in the anesthetized rat
Intracellular and extracellular in vivo recordings can be combined in the anesthetized rat. Global oscillations reverberate between cortex and basal ganglia structures in this state, and the relative strength of different pathways can be elucidated. Importantly, this condition also bears some similarity to Parkinson's disease, in which an increase in bursting and oscillations is also found.
Publications
Goldberg, J.A., Kats, S.S., and Jaeger, D. (2003) Globus Pallidus Discharge Is Coincident with Striatal Activity during Global Slow Wave Activity in the Rat. J. Neurosci. 23: 10058-10063. Abstract Full PDF Text
Antidromic Effects of Deep Brain Stimulation (DBS) in the Subthalamic Nucleus
We combined intra- and extracellular recordings in the anesthetized rat to establish the degree to which the mechanism of DBS may work through antidromic cortical activation. The mechanisms by which DBS works are still under debate, but most previous work has focused on a change in outflow of basal ganglia activity. We find that antidromic cortical activiation could also play a significant role in suppressing pathological oscillatory activtity.
Publications
Li S, Arbuthnott GW, Jutras M, Goldberg JA, Jaeger D. (2007) Resonant antidromic cortical circuit activation as a consequence of high-frequency subthalamic deep-brain stimulation. J. Neurophysiol. 98: 3525-3537 Abstract Full PDF Text
Basal Ganglia activity in the awake behaving monkey
When the whole basal ganglia network acts together in the behaving animal, an impressive array of responses to sensory stimuli and movement can be found. We are still far from understanding the underlying computations.
Publications
Jaeger D, Gilman S, Aldridge JW (1993) Primate basal ganglia activity in a precued reaching task: preparation for movement. Exp Brain Res 95:51-64. Abstract
Jaeger D, Gilman S, Aldridge JW (1995) Neuronal activity in the striatum and pallidum of primates related to the execution of externally cued reaching movements. Brain Res 694:111-27. Abstract Full PDF Text
Mophologically realistic computer simulations of biological neurons
Publications
D. Jaeger Accurate reconstruction of neuronal morphology. chapter 6 in: Computational Neuroscience: Realistic Modeling for Experimentalists. De Schutter, E. editor, CRC press, 2000 Full PDF Text
Jaeger, D. Realistic Single Cell Modeling - from Experiment to Simulation. SPECIAL ISSUE ON REALISTIC NEURAL MODELING, Brains, Minds & Media - open access eJournal http://www.brains-minds-media.org, 2005
Herz, A.V.M, Gollisch, T., Machens, C.K., and Jaeger, D. (2006) Review: Modeling single-neuron dynamics and computations: A balance of detail and abstraction. Science. 314: 80-85 Abstract Full PDF Text
C. Günay, J. R. Edgerton, D. Jaeger (2008). Channel Density Distributions Explain Spiking Variability in the Globus Pallidus: A Combined Physiology and Computer Simulation Database Approach. J Neurosci. 28(30):7476-7491; doi:10.1523/JNEUROSCI.4198-07.2008 Abstract Full PDF Text Model and database files
Recent Meeting Presentations
E. B. Hendrickson, J. R. Edgerton, D. Jaeger. A generalized method for the 100-fold reduction of full morphological neuron models using evolutionary algorithms. Program No. 102.5. 2007 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2007. Online. Abstract