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 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 [1, 2]. Recent evidence in prefrontal cortex [3] and in the olfactory bulb [4] 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” [5, 6], 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 [7, 8, 9], while shedding light on the long debate about the nature of the neuronal code [6].
References
[1] Song, S., Sjostrom, P.J., Reigl, M., Nelson, S., Chklovskii, D.B. Highly non-
random features of synaptic connectivity in local cortical circuits. PLoS Biol.3, e350 (2005).
[2] 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, (2009).
[3] Wang Y, Markram H, Goodman P, Berger T, Ma J, et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat Neurosci 9, (2006).
[4] Pignatelli M. Structure and Function of the Olfactory Bulb Microcircuit. Ph.D. thesis, Ecole Polytechnique Federale de Lausanne, library.epfl.ch/en/theses/?nr=4275, (2009).
[5] Pfister J.P., Gerstner W. Triplets of spikes in a model of spike timing–dependent plasticity. J Neurosci 26, (2006).
[6] Clopath C., Buesing L., Vasilaki E., Gerstner W. (2010) Connectivity reflects coding: A model of voltage-based STDP with homeostasis. Nat Neurosci 13 (2010).
[7] Vasilaki E., Giugliano M. Emergence of connectivity patterns from long-term and short-term plasticities. In: ICANN 2012, Lausanne, Switzerland, (2012).
[8] Vasilaki E., Giugliano M. Emergence of Connectivity Motifs in Networks of Model Neurons with Short- and Long-Term Plastic Synapses. PLOS ONE, 10.1371/journal.pone.0084626 (2014).
[9] Esposito, U., Giugliano, M. and Vasilaki, E. (2015), Adaptation of short-term plasticity parameters via error-driven learning may explain the correlation between activity-dependent synaptic properties, connectivity motifs and target specificity. Frontiers in Computational Neuroscience, 8(175), doi: 10.3389/fn-com.2014.00175.
