Browsing by Subject "Information theory"

Now showing 1 - 4 of 4
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    Computation is concentrated in rich clubs of local cortical networks
    (The MIT Press, 2019-02-01) Faber, Samantha P.; Timme, Nicholas M.; Beggs, John M.; Newman, Ehren L.; Physics, School of Science
    To understand how neural circuits process information, it is essential to identify the relationship between computation and circuit organization. Rich clubs, highly interconnected sets of neurons, are known to propagate a disproportionate amount of information within cortical circuits. Here, we test the hypothesis that rich clubs also perform a disproportionate amount of computation. To do so, we recorded the spiking activity of on average ∼300 well-isolated individual neurons from organotypic cortical cultures. We then constructed weighted, directed networks reflecting the effective connectivity between the neurons. For each neuron, we quantified the amount of computation it performed based on its inputs. We found that rich-club neurons compute ∼160% more information than neurons outside of the rich club. The amount of computation performed in the rich club was proportional to the amount of information propagation by the same neurons. This suggests that in these circuits, information propagation drives computation. In total, our findings indicate that rich-club organization in effective cortical circuits supports not only information propagation but also neural computation.
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    Encoding of the Intent to Drink Alcohol by the Prefrontal Cortex Is Blunted in Rats with a Family History of Excessive Drinking
    (Society for Neuroscience, 2019) Lisenbardt, David N.; Timme, Nicholas M.; Lapish, Christopher C.; Psychology, School of Science
    The prefrontal cortex (PFC) plays a central role in guiding decision making, and its function is altered by alcohol use and an individual's innate risk for excessive alcohol drinking. The primary goal of this work was to determine how neural activity in the PFC guides the decision to drink. Towards this goal, the within-session changes in neural activity were measured from medial PFC (mPFC) of rats performing a drinking procedure that allowed them to consume or abstain from alcohol in a self-paced manner. Recordings were obtained from rats that either lacked or expressed an innate risk for excessive alcohol intake, Wistar or alcohol-preferring (P) rats, respectively. Wistar rats exhibited patterns of neural activity consistent with the intention to drink or abstain from drinking, whereas these patterns were blunted or absent in P rats. Collectively, these data indicate that neural activity patterns in mPFC associated with the intention to drink alcohol are influenced by innate risk for excessive alcohol drinking. This observation may indicate a lack of control over the decision to drink by this otherwise well-validated supervisory brain region.
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    Resources at Risk: The Coordinated Management of Meaning and Study Abroad
    (2012-03-16) Noblet, Nicholas Patrick; Parrish-Sprowl, John; Sandwina, Ronald M.; Goering, Elizabeth M.
    This study seeks to elucidate the concept of resources at risk as detailed in the Coordinated Management of Meaning (CMM) theoretical framework. Risk is the possibility that a communicator’s resources are in jeopardy of change, and this study seeks to explicate how a communicator places his or her resources at risk. An undergraduate spring break study abroad program was selected as the context for this examination, with six students participating in before and after interviews. Results showed that three types of resources at risk were identified, with a fourth type unable to be identified through transcript analysis. This study demonstrates theoretical and practical implications that further the understanding of CMM and its execution. In addition, limitations and areas for future research are discussed.
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    A Tutorial for Information Theory in Neuroscience
    (Society for Neuroscience, 2018-09-11) Timme, Nicholas M.; Lapish, Christopher; Psychology, School of Science
    Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study.