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Monday, July 20 • 7:00pm - 8:00pm
P163: Biophysics and dynamics shape the cross-correlation properties of monosynaptic connections

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Rodrigo Pena will be leading the discussion of this poster. 

Join us at: 

https://zoom.us/j/4935100819?pwd=Y0lIZ3FZVzk3SGwyeVlmZ1NJTUZ3UT09

Meeting ID: 493 510 0819

Password: 771843


Abstract:
Finely-timed spike relationships provide knowledge of putative monosynaptic connections in populations of neurons. Recent experiments involving hippocampal in vivo recordings were able to demonstrate such a relationship by means of the cross-correlation function (CCF) [1,2].  A sharp peak within a few milliseconds in the CCF indicates the presence of a connection. Yet, neurons that are not monosynaptically connected can emit spikes within some short temporal distance as a result of  network co-modulation [3], usually in the form of background noise. In general, there is an agreement that  CCFs are shaped by either the connectivity, synaptic properties, or background activity [4]. However, it remains unclear whether and how the postsynaptic intrinsic neuronal properties such as the ionic currents’ nonlinearities and time constants shape the CCFs between pre- and postsynaptic neurons. The presence of presynaptic-dependent postsynaptic signatures may serve to differentiate between correlation and causation.

We address these issues by combining biophysical modeling, numerical simulations and dynamical systems tools. We extend the framework developed in [5] to describe an ultra-precise monosynaptic connection by including ionic currents with representative dynamics. The model consists of two neurons receiving uncorrelated noise where the presynaptic neuron sends a fixed number of synaptic events to the postsynaptic neuron. CCF is computed as an average over a number of trials. We consider a number of scenarios corresponding to different levels of the ionic currents, their nonlinearities and effective time constants.

Our results show the emergence of an additional slower and wider temporal relationship, after the sharp peak in the CCF. This relationship depends on the dynamic properties present in the postsynaptic neuron model (ionic curretns) in the subthreshold regime. Upon a synaptic event, if the neuron is not on the verge of a spike, it will increase its voltage following some dynamics, which depends particularly on the effective time constant, and which will be reflected in the CCF. This temporal relationship may not be clearly observed in experiments due to a high signal-to-noise ratio and is not capturing external modulation effects. We explain this effect using a phase-plane description where we capture the spike-initiation nonlinearity in terms of nullclines and connect it to the CCF.

We expect that these results will help the identification of monosynaptic connections between different neuron types, in particular, those connections among neurons from different classes.

Funding Acknowledgment

This work was supported by the National Science Foundation grant DMS-1608077 (HGR). 

References

[1] English, D. F., McKenzie, S., Evans, T., Kim, K., Yoon, E., and Buzsáki, G. (2017). Pyramidal cell-interneuron circuit architecture and dynamics in hippocampal networks. Neuron96, 505-520.

[2] Constantinidis, C., and Goldman-Rakic, P.S. (2002). Correlated discharges among putative pyramidal neurons and interneurons in the primate prefrontal cortex. J. Neurophysiol. 88, 3487–3497.

[3] Yu, J., and Ferster, D. (2013). Functional coupling from simple to complex cells in the visually driven cortical circuit. J. Neurosci., 33, 18855-18866.

[4] Ostojic, S., Brunel, N., & Hakim, V. (2009). How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains. J. Neurosci, 29, 10234-10253.

[5] Platkiewicz, J., Saccomano, Z., McKenzie, S., English, D., and Amarasingham, A. (2019). Monosynaptic inference via finely-timed spikes. arXiv preprint arXiv:1909.08553.



Speakers
HR

Horacio Rotstein

Federated Department of Biological Sciences, NJIT / Rutgers University, New Jersey Institute of Technology



Monday July 20, 2020 7:00pm - 8:00pm CEST
Slot 21