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Emergence of connectivity motifs via long term and sort term plasticity

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If you have a question about this talk, please contact Clare E Drysdale.

The identification of synaptic mechanisms that underlie learning and memory is a key challenge for neuroscience. These mechanisms are currently assumed to be captured by persistent modifications to the synaptic connections among neurons.

Synaptic connections in microcircuits and networks are not random; experimental observations indicate the existence of specific microscopic patterns (or connectivity motifs), with non-random features. However, it is unclear how plasticity of individual synaptic connections contributes to the formation of the observed motifs. In particular, for cortical pyramidal neurons, the degree of bidirectional connectivity varies significantly between the visual and somatosensory cortex areas (Song et al, 2005, Lefort et al, 2009). Recent evidence in prefrontal cortex (Wang et al, 2006) and in the olfactory bulb (Pignatelli, Markram, and Carleton, unpub. data) suggest that some other features of synaptic physiology, such as the short-term dynamical nature of the synapse, may be correlated to specific connectivity motifs. The causes for these structural differences are still unknown.

I will present a theory based on a phenomenological, long-term synaptic plasticity “learning rule” (Pfister et al 2006, Clopath et al, 2010), that is able to accurately reproduce a vast corpus of experimental data. The rule captures dependencies on both the timing and frequency of neuronal signals, providing a very simple mechanistic explanation for the emergence of connectivity motifs (Clopath et al, 2010, Vasilaki & Giugliano, 2011, 2012), while shedding light on the long debate about the nature of the neuronal code.

Song, S., Sjöström, P.J., Reigl, M., Nelson, S. & Chklovskii, D.B. Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 3, e350 (2005). Lefort, S., Tomm, C., Sarria, J.C.F. & Petersen, C.C.H. The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61, 301–316 (2009). Wang Y, Markram H, Goodman P, Berger T, Ma J, Goldman-Rakic, P (2006) Heterogeneity in the pyramidal network of the medial prefrontal cortex, Nature Neurosci. 9(4):534-42. Pfister J.-P. & Gerstner W. (2006) Triplets of spikes in a model of spike timing–dependent plasticity. J. Neurosci. 26, 9673–9682. Clopath C, Buesing L, Vasilaki E & Gerstner, W. (2010) Connectivity reflects coding: a model of voltage-based STDP with homeostasis, Nature Neurosci 13, 344–352. Tsodyks MV, Markram H (1997) The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc Natl Acad Sci U S A 94 : 719-723. Vasilaki E. and Giugliano M. (2011) Do synaptic dynamics and STDP govern connectivity motifs? BC11 : Computational Neuroscience and Neurotechnology Bernstein Conference 2011, Freiburg, Germany Vasilaki E. and Giugliano M. (2012) Submitted to PLoS Comp Biology.

Eleni Vasilaki is Lecturer at the department of Computer Science, University of Sheffield. She is Visiting Academic (Gastprofessor) at the University of Antwerp, and Academic editor for the scientific journal PLoS One. Prior to her Academic appointment in Sheffield, Eleni was Scientific Collaborator at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and at the University of Bern. She holds a PhD in Computer Science and Artificial Intelligence (University of Sussex), a Masters in Microelectronics (University of Athens) and a Bachelors degree (with distinction) in Informatics & Telecommunications (University of Athens). She is a Chartered Engineer, registered with of the UK Engineering Council and a member of IET .

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