This paper presents an adaptive observer for online state and parameter estimation of a broad class of biophysical models of neuronal networks. The design closely resembles classical solutions of adaptive control, and the convergence proof is based on contraction analysis. Our results include robustness guarantees with respect to unknown parameter dynamics. We discuss the potential of the approach in neurophysiological applications.
Motivated by neuronal models from neuroscience, we consider the system identification of simple feedback structures whose behaviors include nonlinear phenomena such as excitability, limit-cycles and chaos. We show that output feedback is sufficient to solve the identification problem in a two-step procedure. First, the nonlinear static characteristic of the system is extracted, and second, using a feedback linearizing law, a mildly nonlinear system with an approximately-finite memory is identified. In an ideal setting, the second step boils down to the identification of a LTI system. To illustrate the method in a realistic setting, we present numerical simulations of the identification of two classical systems that fit the assumed model structure.
Negative urgency describes the tendency for rash and impulsive behaviour during negative emotional states and has been linked to a number of psychiatric disorders. However, there has been limited research on negative urgency as an explanatory mechanism for impulsivity in experimental animals. Such research has important implications for elucidating the neurobiology of negative urgency and thereby the development of future therapeutic interventions. In this study, we investigated the effects of negative urgency using a partial reinforcement schedule to increase the frequency of non-rewarded (i.e. frustrative) trials in the five-choice serial reaction time task, a widely used task to assess visual attention and impulsivity. Using a Markov chain model to analyse trial-by-trial outcomes we found that premature (i.e. impulsive) responses in the five-choice serial reaction time task were more likely to occur after a non-rewarded trial, and mostly after a previous premature trial. However, contrary to the frustration hypothesis of negative urgency, increasing the probability of reinforcement ( p(R)) from p(R) = 0.5 to p(R) = 1 increased the number of premature responses in each session. Micro and macro levels of analyses revealed that impulsivity in the five-choice serial reaction time task is governed by at least two processes, one dependent on the overall level of reinforcement hypothesised to determine the state of behavioural activation, the second dependent on trial-by-trial outcomes consistent with negative urgency effects. These processes may depend on distinct neurobiological mechanisms and have relevance for neuropsychiatric disorders that implicate impulsive behaviours dependent on positive and negative affective states.
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