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  Recurrent connectivity regulates the ability of inhibitory STDP to produce E/I co-tuning in a spiking network

Giannakakis, E., Vinogradov, O., & Levina, A. (2021). Recurrent connectivity regulates the ability of inhibitory STDP to produce E/I co-tuning in a spiking network. Poster presented at Bernstein Conference 2021.

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Giannakakis, E1, 2, Author           
Vinogradov, O1, 2, Author           
Levina, A1, 2, Author           
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1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: The detailed balance of excitation and inhibition has been shown to control network dynamics and boost signal propagation in cortical networks [1]. Such balance can be created via the co-tuning of excitatory and inhibitory synaptic weights (Fig 1.B) that develop following various plasticity rules. While the ability of STDP to form excitatory receptive fields has been long demonstrated, only recently have computational studies demonstrated the ability of inhibitory STDP (iSTDP) to produce inhibitory tuning that mirrors excitatory connectivity [2] in a feedforward network propagating distinct signals. However, given the ubiquitous presence of noise and highly recurrent connectivity in biological neural networks, the question arises whether a tight E-I co-tuning can be produced by inhibitory STDP in networks where noise and recurrent connectivity affect the correlation structure of the inputs that a neuron receives. Our study examines the ability of iSTDP, to produce detailed E/I balance of synaptic currents converging to a single postsynaptic neuron under realistic levels of noise and recurrent connectivity in the presynaptic populations. Our findings suggest that presynaptic noise (Fig 1.C) and/or unstructured recurrent connectivity (Fig 1.D) can significantly affect the ability of iSTDP to produce E/I co-tuning due to their effect on the covariance structure of the resulting input currents the postsynaptic neuron receives (Fig 1.E). We investigate this effect in a reduced rate model and compare our findings with a large complex network of LIF neurons (Fig 1.A). We subsequently investigate whether specific structures in the presynaptic connectivity can produce the necessary input statistics for E/I tuning to emerge. To this end, we utilize a Bayesian approach [3] to estimate the connectivity parameters that result in the appropriate input statistics. We find that specific clustering of the pre-synaptic connections can create the conditions for E/I tuning to emerge (Fig 1.F), by reinforcing the signal that noise and unstructured recurrence weaken. Our findings suggest that the effects of noise and recurrent connectivity on the ability of iSTDP to produce E/I co-tuning can be effectively mitigated by the highly clustered topology of the presynaptic network. We thus suggest that a combined effect of connectivity and plasticity allows E/I co-tuning to emerge in networks with biologically plausible connectivity structures.

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 Dates: 2021-09
 Publication Status: Published online
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Title: Bernstein Conference 2021
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Start-/End Date: 2021-09-21 - 2021-09-24

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Title: Bernstein Conference 2021
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: P 33 Start / End Page: - Identifier: -