Browsing by Author "Ackermann, Etienne"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Analysis of an open source, closed-loop, realtime system for hippocampal sharp-wave ripple disruption
    (IOP Publishing, 2018) Dutta, Shayok; Ackermann, Etienne; Kemere, Caleb
    Objective. The ability to modulate neural activity in a closed-loop fashion enables causal tests of hypotheses which link dynamically-changing neural circuits to specific behavioral functions. One such dynamically-changing neural circuit is the hippocampus, in which momentary sharp-wave ripple (SWR) events—≈  100 ms periods of large 150–250 Hz oscillations—have been linked to specific mnemonic functions via selective closed-loop perturbation. The limited duration of SWR means that the latency in systems used for closed-loop interaction is of significant consequence compared to other longer-lasting circuit states. While closed-loop SWR perturbation is becoming more wide-spread, the performance trade-offs involved in building a SWR disruption system have not been explored, limiting the design and interpretation of paradigms involving ripple disruption. Approach. We developed and evaluated a low-latency closed-loop SWR detection system implemented as a module to an open-source neural data acquisition software suite capable of interfacing with two separate data acquisition hardware platforms. We first use synthetic data to explore the parameter space of our detection algorithm, then proceed to quantify the realtime in vivo performance and limitations of our system. Main results. We evaluate the realtime system performance of two data acquisition platforms, one using USB and one using ethernet for communication. We report that signal detection latency decomposes into a data acquisition component of 7.5–13.8 ms and 1.35–2.6 ms for USB and ethernet hardware respectively, and an algorithmic component which varies depending on the threshold parameter. Using ethernet acquisition hardware, we report that an algorithmic latency in the range of  ≈20–66 ms can be achieved while maintaining  <10 false detections per minute, and that these values are highly dependent upon algorithmic parameter space trade-offs. Significance. By characterizing this system in detail, we establish a framework for analyzing other closed-loop neural interfacing systems. Thus, we anticipate this modular, open-source, realtime system will facilitate a wide range of carefully-designed causal closed-loop experiments.
  • Thumbnail Image
    Item
    Functional inference of conductances in the LGMD neuron
    (2013-08-27) Ackermann, Etienne; Cox, Steven J.; Embree, Mark; Sorensen, Danny C.; Dabaghian, Yuri
    This thesis develops an approach to determine spatially-varying ionic channel conductances throughout the dendrites of the LGMD neuron from distal transmembrane potential recordings in response to distributed subthreshold current injections. In particular this approach is demonstrated on a straight cable approximation to the LGMD neuron with leak and hyperpolarization-activated h-currents. Knowledge of the underlying channel conductances can help neuroscientists to characterize, better understand, and predict neuronal behavior---and topographic integration in the LGMD neuron in particular---but it is extremely difficult to measure these conductances directly. As a consequence, these conductances are commonly estimated by searching for several parameters that lead to simulated responses that are consistent with recorded behavior. In contrast, the approach presented here uses the method of moments to directly recover the underlying conductances, eliminating the need to simulate responses, making this approach both faster and more robust than typical optimization approaches since the solution cannot get trapped in local minima.
  • Thumbnail Image
    Item
    Uncovering temporal structure in hippocampal output patterns
    (eLife Sciences Publications, Ltd, 2018) Maboudi, Kourosh; Ackermann, Etienne; de Jong, Laurel Watkins; Pfeiffer, Brad E.; Foster, David; Diba, Kamran; Kemere, Caleb
    Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals' positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.