Synaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks. Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.The nervous system has evolved a communication system largely based on temporal 2 sequences of action potentials. A central feature of this communication is that action 3 potentials are communicated with variable efficacy on short (10 ms -10 s) time 4 scales [1-6]. The dynamics of synaptic efficacy at short time scales, or short-term 5 plasticity (STP), can be a powerful determinant of the flow of information, allowing the 6 same axon to communicate independent messages to different post-synaptic 7 targets [7, 8]. Properties of STP vary markedly across projections [9-11], leading to the 8idea that connections can be conceived as belonging to distinct classes [12,13] and that 9 these distinct classes shape information transmission in vivo [14][15][16]. Thus, to 10 May 29, 2020 1/25 understand the flow of information in neuronal networks, the connectome must be 11 indexed with an accurate description of STP properties. 12One approach to characterizing synaptic dynamics is to perform targeted 13 experiments and extract a summary feature, most commonly the paired-pulse 14 ratio [5,[17][18][19], whereby a synapse can be classified as short-term depressing (STD) or 15 short-term facilitating (STF). However, a single summary feature is insufficient to 16 capture the full extent of STP diversity. Longer or more complex stimulation patterns 17 are required to describe delayed facilitation onset [6], biphasic STP [20, 21] or the 18 distinction between supra-and sub-linear facilitation [22]. Such atypical STP dynamics 19 challenge the traditional dichotomy of STF and STD and suggest that more complex 20 phenotypes can exist and contribute to network function in...
Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.
Synapses show preferential responses to particular temporal patterns of activity. Across individual synapses, there is a large degree of response heterogeneity that is informally or tacitly separated into classes, and typically only two: facilitating and depressing short-term plasticity. Here we combined a kernel-based model and machine learning techniques to infer the number and the characteristics of functionally distinct subtypes of short-term synaptic dynamics in a large dataset of glutamatergic cortical connections. To this end, we took two independent approaches. First, we used unsupervised techniques to group similar synapses into clusters. Second, we used supervised prediction of cell subclasses to reveal features of synaptic dynamics that characterized cellular genetic profiles. In rodent data, we found five clusters with a remarkable degree of convergence with the transgenic-associated subtypes. Two of these clusters corresponded to different degrees of facilitation, two corresponded to depression with different degrees of variability and one corresponded to depression-then-facilitation. Strikingly, the application of the same clustering method in human data inferred highly similar clusters to those observed in rodents, supportive of a stable clustering procedure and suggesting a homology of functional subtypes across species. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.
This work presents machine learning based techniques for detecting mind-wandering and predicting hazard response time in driving using only easily measurable driving performance data (speed, horizontal and frontal acceleration, lane gap, and brake pressure). Such predictors are relevant as research tools in the driving simulation community. We present a simple method, and a feature extraction based method, of representing time-series driving performance data that both support machine learning based predictions. We use the two types of representations to compare the effectiveness of support vector machines, random forest, and multi-layer perceptrons on data from 117 drives performed by 39 participants during a previous study in the high-fidelity driving simulator at the University of Guelph.Classification of mind-wandering and prediction of hazard response time was successful when compared to baseline measures. Specifically, random forest methods were most effective in both types of prediction and feature extraction supported the strongest random forest prediction of hazard response time. A discussion of the reasoning for this is included.To our knowledge this is the first driving pattern based classification of mind-wandering in a fully immersive driving simulator.iii AcknowledgementsBefore anything else I wish to express my deepest gratitude to my amazing supervisor professor Andrew Hamilton-Wright. Andrew, your contributions to my academic journey cannot be overstated, the courses you taught me at Mount Allison are what inspired me to pursue this subject at the graduate level and the phenomenal quality of your support and mentorship here in Guelph have prepared and motivated me for my journey into the future.You are truly one of the best professors, leaders, and guides I have had the good fortune to meet.I would also like to express my deepest thanks to my co-supervisor professor Lana Trick.Lana, I want to thank you for introducing me into the world of driving simulation through the well organised, engaging, and collaborative meetings and events of the DRiVE lab, for your incredibly helpful feedback on my research project from design to completion, and for all the work you have put in over the years building and supporting the impressive lab that made this project possible.I gratefully acknowledge the assistance of my advisory committee member professor David Calvert for his time, insightful questions, and feedback. I also wish to thank Heather Walker, an incredibly conscientious PhD student in the DRiVE lab who trained me on the simulator when I arrived, provided consistent support to all of us in the lab, and, along with professor Trick, is responsible for the experimental work that this thesis relies on.Finally, I would like to thank my family, for their continued support through this degree and proofreading many drafts of many papers, especially my mother who has moved mountains for us for as long as I can remember.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations鈥揷itations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright 漏 2024 scite LLC. All rights reserved.
Made with 馃挋 for researchers
Part of the Research Solutions Family.