My research focuses on identifying the rules by which changes in synaptic strength - believed to be the basis of learning, memory and development in the cortex - take place. These synapses are the means by which one neuron communicates with another, and changes in these weights are called synaptic plasticity. I concentrate on theoretical/ computational approaches to the study of synaptic plasticity and its implications on learning, memory and development. I study synaptic plasticity at many levels, from its molecular basis to its functional implications and I believe that theoretical studies are essential for forming the link between these different levels of description.
|Video: Monocular Deprivation. View/Download MPG File (997 kb)|
Topics I currently study include:
The Molecular Basis of Synaptic Plasticity
Much is known about the molecular and physiological basis of synaptic plasticity. I carry out complex simulations of signal transduction pathways involved in synaptic plasticity, as well as analysis of the molecular dynamics of molecules such as calcium that are essential for synaptic plasticity.
Simplified Cellular Models of Synaptic Plasticity
Derivation of simplified models, either by approximating the more complex molecular models, or from first principles can help bridge the gap between the molecular level and electrophysiological experiments. Recently I derived a simple unified calcium dependent plasticity model that can account for the various induction paradigms, including spike time dependent plasticity (STDP). Both the assumptions and predicted consequences of the model can be tested experimentally.
The Contribution of Synaptic Plasticity to Receptive Field Development
Many properties of receptive fields in visual cortex, as well as other cortical areas are experience dependent. We have previously accounted for such properties using more traditional, rate-based models of synaptic plasticity, in visual environments composed of natural images. Currently we are examining if the unified calcium dependent model can account for the development of receptive fields as well.
The aim of this research is fundamental: understand how humans learn and how our nervous system develops. When we understand that, we will be able to understand why something goes wrong with those processes.
Rittenhouse, CD, Shouval HZ, Paradiso MA, Bear MF. (1999) Monocular deprivation induces homosynaptic long-term depression in visual cortex. Nature, 397(6717): p. 347-50.
Blais, B, Cooper, LN, Shouval, HZ. (2000) Formation of direction selectivity in natural scene environments. Neural Comput, 12(5): p. 1057-66.
Shouval, HZ, et al. (2000) Structured long-range connections can provide a scaffold for orientation maps. J Neurosci, 20(3): p. 1119-28.
Castellani, GC, Quinlan, EM, Cooper, LN, Shouval, HZ. (2001) A biophysical model of bidirectional synaptic plasticity: dependence on AMPA and NMDA receptors. Proc Natl Acad Sci USA. 98(22): p. 12772-7.
Shouval, HZ, et al. (2002) Converging evidence for a simplified biophysical model of synaptic plasticity. Biol Cybern. 87(5-6): p. 383-91.
Shouval, HZ, Bear, MF, Cooper, LN. (2002) A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proc Natl Acad Sci USA, 99(16): p. 10831-6.
Shouval, HZ. (2005) Clusters of interacting receptors can stabilize synaptic efficacies. Proc Natl Acad Sci USA. 2005 Oct 4;102(4).
Cai, Y, Gavornik, JP, Cooper, LN, Yeung, LC, Shouval, HZ. (2007) Effect of stochastic synaptic and dendritic dynamics on synaptic plasticity in visual cortex and hippocampus. J Neurophysiol. 2007 Jan; 97(1):375-86.
Aslam, N., Kubota, Y., Wells, D., and Shouval H.Z. (2009) Translational Switch for Long Term Maintenance Of Synaptic Plasticity, Molecular Systems Biology 5:284.
Gavornik J.P., Hussain Shuler, M.G., Loewenstein, Y. Bear, M.F., and Shouval, H.Z. (2009) Learning Reward Timing in Cortex through Reward Dependent Expression of Synaptic Plasticity. Proc. Natl. Acad. 106:6826-31.
Shouval H.Z, Wang S.S, Wittenberg G.M. (2010) Spike timing dependent plasticity: a consequence of more fundamental learning, Front Comput Neurosci. 4:19.
Gavornik J.P. and Shouval H.Z. (2011) A network of spiking neurons that can represent interval timing: mean field analysis. J. Comput. Neurosci. 30:501-13.
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