Computing the inverse of the neurophysiological spike-response transform

Consider the transform from a discrete neuronal spike train to a continuous neurophysiological response such as postsynaptic membrane voltage or muscle contraction. Often, we can record only the response. Here we therefore ask about the inverse of this transform: given the response, how can we estimate from it the spike train that produced it? In previous work, we have developed a kernel-based "decoding" method for system identification of the forward transform. Using several variant approaches based on this method, here we demonstrate how to identify the spike train, in the minimal case from just the response, with synthetic as well as real synaptic and neuromuscular data

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

15 th Annual Computational Neuroscience Meeting CNS*2006 Decoding modulation of the neuromuscular transform

Question. Neuromodulators regulate many neurophysiological processes. These processes are often modeled as input-output relationships. Here we consider, for example, the neuromuscular transform [1]: in a neuromuscular system, the transformation of the pattern of motor neuron spikes to the waveform of muscle contractions that the spikes elicit (Fig. 1). Consider a modulator that, to meet some physiological demand, must alter the output contractions in a particular way. How can the modulator accomplish this? Experimentally, it is observed that it generally alters the input spike pattern. Yet both theory and experiment strongly suggest that this alone will often be insufficient. This is because the neuromuscular transform acts as a constraining channel whose properties are tuned, at any particular time, only to a narrow range of input patterns. When the input pattern is modulated outside this range, the output contractions will not be able to follow [2]. To allow them to follow, the properties of the neuromuscular transform must be retuned correspondingly [3]. We predict, therefore, that physiological modulators of neuromuscular function, and o