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The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping medial frontal cortex activity that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ∼2 seconds before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses. HIGHLIGHTS: 1. We reveal a mechanism for slow-ramping signals before spontaneous voluntary movements. 2. Slow synapses stabilize spontaneous fluctuations in spiking neural network. 3. We validate model predictions in human frontal cortical single neuron recordings. 4. The model recreates the readiness potential in an EEG proxy signal. 5. Neurons that ramp together had correlated activity before ramping onset.

This project is conducted by the COVID-DYNAMIC Team (http://conte.caltech.edu/content/covid-dynamic-investigator-team) The COVID-19 pandemic is confronting people with significant changes in affectively-laden media exposure as well as in social behavior. To quantify longitudinal effects that could inform public health policy, we will collect anonymized data over the internet on subject's implicit and explicit emotional biases, and characterize how these might relate to social, economic, moral and political judgments and decision-making. We will sample approximately 1000 English-speaking adult participants in the US over the internet anonymously, recruited through Prolific.co. We will also sample an extant registry of approximately 100 community-dwelling adults in Los Angeles that are part of the Caltech Conte Center on the Neurobiology of Social Decision-Making (http://conte.caltech.edu/content/covid-dynamic). We plan to test participants longitudinally for approximately one hour, up to one year. We expect that the frequency of testing will vary depending on the development of the pandemic, from weekly to monthly. Subject recruitment and testing began on April 5, 2020, with additional waves re-tested in the subsequent two weeks, and data collection ongoing. At this point, we have only analyzed the data from these three waves for quality control and instrument design purposes, but have not analyzed any data for any hypothesis testing. This preregistration includes methods of participant recruitment, sample size, inclusion and exclusion criteria, and general study procedures. Subsequent preregistrations will provide updates and cover specific studies that we will conduct using the data collected with the procedures described here.

Data and supplemental information for the Covid-Dynamic longitudinal study (https://coviddynamic.caltech.edu/ ) Code available on https://github.com/adolphslab/CovidDynamic_dataRelease.git

A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.

The covid-19 pandemic has widened political and social divides in the US – a trend most notable by the emergence of anti-science and ideological movements bent on hindering governments’ actions by disseminating disinformation. Thus, certain views, often found at the fringe of the marketplace of ideas, have pervaded into mainstream media to the point of influencing the 2020 presidential elections, for example. Overall, the spread of conspiracy theories has led to increased distrust in institutions, reduced social engagement and greater hesitancy towards evidence-based health practices towards Covid-19. As a prominent example, while evidence indicates that vaccination is currently the most reliable strategy for protecting vulnerable populations and preventing overwhelming hospitalizations, disinformation has increased overall hesitancy in the general population. Previous work indicates that motivations to support conspiratorial thinking is heterogenous—while some individuals may be prone to support such views for epistemic reasons (e.g., wanting to better understand), others may do so due to existential (e.g., wanting to be safe) or social (e.g., wanting to protect or belong to one’s group) motives. Our research aims to explore heterogeneity in conspiracy theory related to Covid-19. As part of this effort, we collected longitudinal data from a large sample size (N>600) of US residents across multiple waves of the same participants during the pandemic, starting April 2020. Participants completed a series of self-reported questionnaire and experimental tasks, while we also recorded demographics information. Our measures included various psychological assessments, race attitudes, and covid-related questions. Participants also completed a questionnaire concerning their views on various aspects pertaining to Covid-19 vaccination, news media, and government to examine whether they support conspiracy theories. Moreover, they performed a target discrimination task, in which they were asked to indicate whether a visual stimulus, embedded within noise, was a face or a house. Critically, the task involved a detection component where the target stimulus was absent for 30% of trials. In this way, we estimated participants’ tendency to detect patterns when none were present, based on a signal-detection theoretic framework. To explore heterogeneity for conspiracy theory of Covid-19, our analyses will comprise structural equation modelling and latent profile analysis based on personality traits, psychological wellbeing, and socio-political views. With this approach, we aim to understand the extent to which these factors promote conspiracy theories of Covid-19 in different ways. Last, we will investigate how this effects vaccine hesitancy.