Spine head calcium as a measure of summed postsynaptic activity for driving synaptic plasticity
Abstract
We use a computational model of a hippocampal CA1 pyramidal cell to demonstrate that spine head calcium provides an instantaneous readout at each synapse of the postsynaptic weighted sum of all presynaptic activity impinging on the cell. The form of the readout is equivalent to the functions of weighted, summed inputs used in neural network learning rules. Within a dendritic layer, peak spine head calcium levels are either a linear or sigmoidal function of the number of coactive synapses, with nonlinearity depending on the ability of voltage spread in the dendrites to reach calcium spike threshold. This is strongly controlled by the potassium A-type current, with calcium spikes and the consequent sigmoidal increase in peak spine head calcium present only when the A-channel density is low. Other membrane characteristics influence the gain of the relationship between peak calcium and the number of active synapses. In particular, increasing spine neck resistance increases the gain due to increased voltage responses to synaptic input in spine heads. Colocation of stimulated synapses on a single dendritic branch also increases the gain of the response. Input pathways cooperate: CA3 inputs to the proximal apical dendrites can strongly amplify peak calcium levels due to weak EC input to the distal dendrites, but not so strongly vice versa. CA3 inputs to the basal dendrites can boost calcium levels in the proximal apical dendrites, but the relative electrical compactness of the basal dendrites results in the reverse effect being less significant. These results give pointers as to how to better describe the contributions of pre-and postsynaptic activity in the learning "rules" that apply in these cells. The calcium signal is closer in form to the activity measures used in traditional neural network learning rules than to the spike times used in spike-timing-dependent plasticity.
References
[1]
Ang, C. W., Carlson, G., & Coulter, D. A. (2005). Hippocampal CA1 circuitry dynamically gates direct cortical inputs preferentially at theta frequencies. Journal of Neuroscience, 25, 9567-9580.
[2]
Bienenstock, E. L., Cooper, L. N., & Munro, P. W. (1982). Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. Journal of Neuroscience, 2, 32-48.
[3]
Bliss, T., Collingridge, G., & Morris, R. (2007). Synaptic plasticity in the hippocampus. In P. Andersen, R. Morris, D. Amaral, T. Bliss, & J. O'Keefe (Eds.), The hippocampus book (pp. 343-474). New York: Oxford University Press.
[4]
Bloodgood, B. L., & Sabatini, B. L. (2007). Ca(2+) signaling in dendritic spines. Curr. Opin. Neurobiol., 17, 345-351.
[5]
Brun, V. H., Otnass, M. K., Molden, S., Steffenach, H. A., Witter, M. P., Moser, M. B., & Moser, E. I. (2002). Place cells and place recognition maintained by direct entorhinal-hippocampal circuitry. Science, 296, 2243-2246.
[6]
Buchanan, K. A., & Mellor, J. R. (2010). The activity requirements for spike timing-dependent plasticity in the hippocampus. Front. Synaptic Neurosci., 2:11.
[7]
Carnevale, N. T., & Hines, M. L. (2006). The NEURON book. Cambridge: Cambridge University Press.
[8]
Clopath, C., Büsing, L., Vasilaki, E., & Gerstner, W. (2010). Connectivity reflects coding: A model of voltage-based STDP with homeostasis. Nature Neuroscience, 13, 344-352.
[9]
Cutsuridis, V., Cobb, S., & Graham, B. P. (2010). Encoding and retrieval in a model of the hippocampal CA1 microcircuit. Hippocampus, 20, 423-446.
[10]
Dudman, J. T., Tsay, D., & Siegelbaum, S. A. (2007). A role for synaptic inputs at distal dendrites: Instructive signals for hippocampal long-term plasticity. Neuron, 56, 866-879.
[11]
Gómez González, J. F., Mel, B. W., & Poirazi, P. (2011). Distinguishing linear vs. non-linear Integration in CA1 radial oblique dendrites: It's about time. Frontiers in Computational Neuroscience, 5, 44.
[12]
Graupner, M., & Brunel, N. (2010). Mechanisms of induction and maintenance of spike-timing dependent plasticity in biophysical synapse models. Frontiers in Computational Neuroscience, 4, 136.
[13]
Grunditz, A., Holbro, N., Tian, L., Zuo, Y., & Oertner, T. G. (2008). Spine neck plasticity controls postsynaptic calcium signals through electrical compartmentalization. Journal of Neuroscience, 28, 13457-13466.
[14]
Hardie, J., & Spruston, N. (2009). Synaptic depolarization is more effective than backpropagating action potentials during induction of associative long-term potentiation in hippocampal pyramidal neurons. Journal of Neuroscience, 29, 3233-3241.
[15]
Harnett, M. T., Makara, J. K., Spruston, N., Kath, W. L., & Magee, J. C. (2012). Synaptic amplification by dendritic spines enhances input cooperativity. Nature, 491, 599-602.
[16]
Häusser, M. (2001). Synaptic function: Dendritic democracy. Current Biology, 11, R10-R12.
[17]
Hebb, D. O. (1949). The organization of behavior. New York: Wiley.
[18]
Holbro, N., Grunditz, A., Wiegert, J. S., & Oertner, T. G. (2010). AMPA receptors gate spine Ca2+ transients and spike-timing-dependent potentiation. PNAS, 107, 15975-15980.
[19]
Izumi, Y., & Zorumski, C. F. (2008). Direct cortical inputs erase long-term potentiation at Schaffer collateral synapses. Journal of Neuroscience, 28, 9557-9563.
[20]
Jarsky, T., Roxin, A., Kath, W. L., & Spruston, N. (2005). Conditional dendritic spike propagation following distal synaptic activation of hippocampal CA1 pyramidal neurons. Nature Neuroscience, 8, 1667-1676.
[21]
Judge, S. J., & Hasselmo, M. E. (2004). Theta rhythmic stimulation of stratum lacunosum-moleculare in rat hippocampus contributes to associative LTP at a phase offset in stratum radiatum. Journal of Neurophysiology, 92, 1615-1624.
[22]
Káli, S., & Freund, T. F. (2005). Distinct properties of two major excitatory inputs to hippocampal pyramidal cells: A computational study. European Journal of Neuroscience, 22, 2027-2048.
[23]
Körding, K. P., & König, P. (2000). Learning with two sites of synaptic integration. Network: Computation in Neural Systems, 11, 25-39.
[24]
Körding, K. P., & König, P. (2001). Supervised and unsupervised learning with two sites of synaptic integration. Journal of Computational Neuroscience, 11, 207-215.
[25]
Kumar, A., & Mehta, M. R. (2011). Frequency-dependent changes in NMDAR-dependent synaptic plasticity. Frontiers in Computational Neuroscience, 5:38.
[26]
Li, X., & Ascoli, G. A. (2006). Computational simulation of the input-output relationship in hippocampal pyramidal cells. Journal of Computational Neuroscience, 21, 191-209.
[27]
Losonczy, A., Makara, J. K., & Magee, J. C. (2008). Compartmentalized dendritic plasticity and input feature storage in neurons. Nature, 452, 436-441.
[28]
Magee, J. C., & Cook, E. P. (2000). Somatic EPSP amplitude is independent of synapse location in hippocampal pyramidal neurons. Nature Neuroscience, 3, 895-903.
[29]
Makara, J. K., Losonczy, A., Wen, Q., & Magee, J. C. (2009). Experience-dependent compartmentalized dendritic plasticity in rat hippocampal CA1 pyramidal neurons. Nature Neuroscience, 12, 1485-1487.
[30]
Migliore, M., Ferrante, M., & Ascoli, G. A. (2005). Signal propagation in oblique dendrites of CA1 pyramidal cells. Journal of Neurophysiology, 94, 4145-4155.
[31]
Morrison, A., Diesmann, M., & Gerstner, W. (2008). Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics, 98, 459-478.
[32]
O'Donnell, C., Nolan, M. F., & van Rossum, M. C. W. (2011). Dendritic spine dynamics regulate the long-term stability of synaptic plasticity. Journal of Neuroscience, 31, 16142-16156.
[33]
Otmakhova, N. A., & Lisman, J. E. (1998). Dopamine selectively inhibits the direct cortical pathway to the CA1 hippocampal region. Journal of Neuroscience, 19, 1437-1445.
[34]
Palmer, L., & Stuart, G. (2009). Membrane potential changes in dendritic spines during action potentials and synaptic input. Journal of Neuroscience, 29, 6897-6903.
[35]
Pissadaki, E. K., & Poirazi, P. (2007). Modulation of excitability in CA1 pyramidal neurons via the interplay of entorhinal cortex and CA3 inputs. Neurocomputing, 70, 10-12, 1735-1740.
[36]
Pissadaki, E. K., Sidiropoulou, K., Reczko, M., & Poirazi, P. (2010). Encoding of spatio-temporal input characteristics by a CA1 pyramidal neuron model. PLOS Computational Biology, 6, 12, e1001038.
[37]
Poirazi, P., Brannon, T., & Mel, B. W. (2003a). Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell. Neuron, 37, 977-987.
[38]
Poirazi, P., Brannon, T., & Mel, B. W. (2003b) Pyramidal neuron as 2-layer neural network. Neuron, 37, 989-999.
[39]
Rackham, O. J. L., Tsaneva-Atanasova, K., Ganesh, A., & Mellor, J. R. (2010). A Ca2+-based computational model for NMDA receptor dependent synaptic plasticity at individual post-synaptic spines in the hippocampus. Frontiers in Synaptic Neuroscience, 2:31.
[40]
Remondes, M., & Schuman, E. M. (2002). Direct cortical input modulates plasticity and spiking in CA1 pyramidal neurons. Nature, 416, 736-740.
[41]
Sabatini, B. L., Oertner, T. G., & Svoboda, K. (2002). The life cycle of Ca2+ ions in dendritic spines. Neuron, 33, 439-452.
[42]
Saudargiene, A., Porr, B., & Wörgötter, F. (2004). How the shape of pre - and postsynaptic signals can influence STDP: A biophysical model. Neural Computation, 16, 595-625.
[43]
Saudargiene, A., Porr, B., & Wörgötter, F. (2005). How the shape of pre - and postsynaptic signals can influence STDP: A biophysical model. Biological Cybernetics, 92, 128-138.
[44]
Shouval, H. Z., Wang, S. S.-H., & Wittenberg, G. M. (2010). Spike timing dependent plasticity: A consequence of more fundamental learning rules. Frontiers in Computational Neuroscience, 4, 19.
[45]
Sterratt, D. C., Groen, M. R., Meredith, R. M., & van Ooyen, A. (2012). Spine calcium transients induced by synaptically-evoked action potentials can predict synapse location and establish synaptic democracy. PLoS Comput. Biol., 8, e1002545.
[46]
Takahashi, H., & Magee, J. C. (2009). Pathway interactions and synaptic plasticity in the dendritic tuft regions of CA1 pyramidal neurons. Neuron, 62, 102-111.
[47]
Tsay, D., Dudman, J. T., & Siegelbaum, S. A. (2007). HCN1 channels constrain synaptically evoked Ca2+ spikes in distal dendrites of CA1 pyramidal neurons. Neuron, 56, 1076-1089.
- Spine head calcium as a measure of summed postsynaptic activity for driving synaptic plasticity
Recommendations
Ryanodine-receptor-driven intracellular calcium dynamics underlying spatial association of synaptic plasticity
Synaptic modifications induced at one synapse are accompanied by hetero-synaptic changes at neighboring sites. In addition, it is suggested that the mechanism of spatial association of synaptic plasticity is based on intracellular calcium signaling that ...
Simplified calcium signaling cascade for synaptic plasticity
AbstractWe propose a model for synaptic plasticity based on a calcium signaling cascade. The model simplifies the full signaling pathways from a calcium influx to the phosphorylation (potentiation) and dephosphorylation (depression) of ...
Modeling Synaptic Plasticity in Conjunction with the Timing of Pre- and Postsynaptic Action Potentials
We present a spiking neuron model that allows for an analytic calculation of the correlations between pre- and postsynaptic spikes. The neuron model is a generalization of the integrate-and-fire model and equipped with a probabilistic spike-triggering ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
MIT Press
Cambridge, MA, United States
Publication History
Published: 01 October 2014
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Dec 2024