Biology subjects

McLaughlin, D. W.

Publications and source records attributed to McLaughlin, D. W..

3 recordsLinked to original sources

bioRxiv2023

A Biochemical Description of Postsynaptic Plasticity - with Timescales Ranging from Milliseconds to Seconds

Synaptic plasticity (long term potentiation/depression (LTP/D)), is a cellular mechanism underlying learning. Two distinct types of early LTP/D (E-LTP/D), acting on very different time scales, have been observed experimentally - spike timing dependent plasticity (STDP), on time scales of tens of ms; and behavioral time scale plasticity(BTSP), on time scales of seconds. BTSP is a candidate for the mechanism for rapid learning of spatial location by hippocampal place cells. Here a computational model of the induction of E-LTP/D at a spine head of a synapse of a hippocampal pyramidal neuron is developed. The single compartment model represents two interacting biochemical pathways for the activation (phosphorylation) of the kinase (CaMKII) with a phosphatase, with Ion inflow described by NMDAR, CaV1, and Na channels. The biochemical reactions are represented by a deterministic system of differential equations. This single model captures realistic responses (temporal profiles with the differing timescales) of STDP and BTSP and their asymmetries for each (STDP or BTSP) signaling protocol. The simulations detail several mechanisms underlying both STDP and BTSP, including i) the flow of Ca2+ through NMDAR vs CaV1 channels, and ii) the origin of several time scales in the activation of CaMKII. The model also realizes a priming mechanism for E-LTP that is induced by Ca2+ flow through CaV1.3 channels. Once in the spine head, this small additional Ca2+ opens the compact state of CaMKII, placing CaMKII "in the ready" for subsequent induction of LTP.

neuroscience↗

bioRxiv2023

Quantitative relations among causality measures with applications to pulse-output nonlinear network reconstruction

The causal connectivity of a network is often inferred to understand the network function. It is arguably acknowledged that the inferred causal connectivity relies on the causality measure one applies, and it may differ from the networks underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, e.g., neural networks with spike output, and address the above issues based on four intensively utilized causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking the simulated Hodgkin-Huxley neural network and the real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore establishing a direct link between the causal and structural connectivity. We stress that the structural connectivity of networks can be reconstructed pairwise without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction. Significance StatementInferring network connectivity is a key challenge in many diverse scientific fields. We investigate networks with pulse signal as measured output and solve the above reverse-engineering issue by establishing a direct link between the networks causal connectivity and structural connectivity. Here, the causal connectivity can be inferred by any one of the four causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We analytically reveal the relationship among these four measures and show that they are equally effective to fully reconstruct the network connectivity pairwise. Our work provides a practical framework to reconstruct the structural connectivity in general pulse-output nonlinear networks or subnetworks.

neuroscience↗

bioRxiv2020

Idealized model of the developing visual cortex

Recent experiments in the developing mammalian visual cortex have revealed that gap junctions couple excitatory cells and potentially influence the formation of chemical synapses. Though gap junctions between inhibitory cells are ubiquitous in the adult cortex, and their presence has been shown to promote synchronous network firing, their function among excitatory, pyramidal cells remains poorly understood. During development, pyramidal cells that were derived from the same progenitor cell, called sister cells, are preferentially connected by a gap junction during the first postnatal week, while chemical synapses are still being formed. Additionally, these sister cells tend to share an orientation preference and a chemical synapse in the adult cortex, a property that is diminished when gap junctions are blocked. In this work, we construct an idealized model of the mouse visual cortex during the first two postnatal weeks of development to analyze the response properties of gap-junction-coupled cells and their effect on synaptic plasticity. Further, as an application of this model, we investigate the interplay of gap-junction coupling and synaptic plasticity on the order, or organization, of the resulting cortical map of orientation preference. Author summaryGap junctions, or sites of direct electrical connections between neurons, have a significant presence in the cortex, both during development and in adulthood. Their primary function during either of these periods, however, is still poorly understood. In the adult cortex, gap junctions between local, inhibitory neurons have been shown to promote synchronous firing, a network characteristic thought to be important for learning, attention, and memory. During development, gap junctions between excitatory, pyramidal cells, have been conjectured to play a role in synaptic plasticity and the formation of cortical circuits. In the visual cortex, where neurons exhibit tuned responses to properties of visual input such as orientation and direction, recent experiments show that excitatory cells are coupled by gap junctions during the first postnatal week and are replaced by chemical synapses during the second week. In this work, we explore the possible contribution of gap-junction coupling during development to the formation of chemical synapses both into the visual cortex from the thalamus and within the visual cortex between cortical cells. Specifically, within a mathematical model of the visual cortex during development, we identify the response properties of gap-junction-coupled cells and their influence on the formation of the cortical map of orientation preference.

neuroscience↗