Biophysical modeling to reverse engineer two mammalian neural circuits lower urinar Y tract and hippocampus

Computational neuroscience provides tools to abstract and generalize principles of neuronal function using mathematics and computers. This dissertation reports biophysical modeling approaches to facilitate reverse engineering of two mammalian neural circuits - the lower urinary tract for the development of stimulation techniques, and the rodent hippocampus to understand mechanisms involved in theta rhythms. The LUT in mammals consists of the urinary bladder, external urethral sphincter (EUS) and the urethra. Control of the LUT is achieved via a neural circuit which integrates distinct components. Dysfunctions of the lower urinary tract (LUT) are caused by a variety of factors including spinal cord injury and diabetes. Our model builds on previous models by using biologically realistic spiking neurons to reproduce neural control of the LUT in both normal function and dysfunction cases. The hippocampus has long been implicated in memory storage and retrieval. Also, hippocampal theta oscillations (4-12 Hz) are consistently recorded during memory tasks and spatial navigation. Previous model revealed five distinct theta generators. The present study extends the work by probing deeper into the intrinsic theta mechanisms via characterizing the mechanisms as being resonant, i.e., inherently produce theta, or synchronizing, i.e., promote coordinated activity, or possibly both. The role of the neuromodulatory state is also investigated.Includes bibliographical references (pages 157-164)

Modeling the hippocampus : finely controlled memory storage using spiking neurons

The hippocampus, an area in the temporal lobe of the mammalian brain, participates in the storage of personal memories and life events. As such traumatic memories and the consequent symptoms of post-traumatic stress are thought to be stored or at least processedin the hippocampus. While a fundamental understanding of a traumatic memory is still elusive, studying the physiology and functional properties of the hippocampus are anessential first step. Towards that goal, I developed a detailed computational model of the hippocampus. The model included the important effects of the neuromodulator Acetylcholine that switches the hippocampal network between the memory encoding state and the memory retrieval state. In the first study, I examined the mechanisms for controlling runaway excitation in the model. The results indicated different mechanisms for controlling runaway excitation in the memory encoding state as opposed to the memory retrieval state of the circuit. These findings produced the first functionally-based categorization of seizures in animals and humans, and may inspire specific treatments for these types of seizures. The second study examined the underpinnings of the rhythmic activity of the hippocampus. These oscillations in the theta range (4-12 Hz) are theorize to play a major role in the memory functions and in processing sequences of events and actions in both place and time. We found the generation of theta rhythmic activity to be best described as a product of multiple interacting generators. Importantly, we found differences in theta generation depending on the functional state of the hippocampus. Finally, the third study detailed the rules of the complex interactions between these multiple theta generators in the circuit. Our results shed more light on the role of specific components in the hippocampal circuit to maintain its function in both health and disease states

Conductance-Based Refractory Density Approach for a Population of Bursting Neurons

The conductance-based refractory density (CBRD) approach is a parsimonious mathematical-computational framework for modeling interact- ing populations of regular spiking neurons, which, however, has not been yet extended for a population of bursting neurons. The canonical CBRD method allows to describe the firing activity of a statistical ensemble of uncoupled Hodgkin-Huxley-like neurons (differentiated by noise) and has demonstrated its validity against experimental data. The present manuscript generalises the CBRD for a population of bursting neurons; however, in this pilot computational study we consider the simplest setting in which each individual neuron is governed by a piecewise linear bursting dynamics. The resulting popula- tion model makes use of slow-fast analysis, which leads to a novel method- ology that combines CBRD with the theory of multiple timescale dynamics. The main prospect is that it opens novel avenues for mathematical explo- rations, as well as, the derivation of more sophisticated population activity from Hodgkin-Huxley-like bursting neurons, which will allow to capture the activity of synchronised bursting activity in hyper-excitable brain states (e.g. onset of epilepsy).Russian Science Foundation grant (project 16-15- 10201) Spanish grant MINECO-FEDER-UE MTM-2015-71509-C2-2-R Catalan Grant number 2017SGR104

The Impact of Mild Traumatic Brain injury on Neuronal Networks and Neurobehavior

Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this dissertation, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We probed the microarchitecture of an injured cortical circuit subject to two different injury levels, mild stretch (10% peak) and mild/moderate (35%). We found that mild injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, mild/moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology, suggesting a threshold for repair and degradation. The more significant changes in network behavior at moderate stretch are influenced by NMDA receptor activation and subsequent proteolytic changes in the neuronal populations. With the ability to analyze individual neurons in a circuit before and after injury, we identified several biomarkers that confer increased risk or protection from mechanical injury. We found that pre-injury connectivity and NMDA receptor subtype composition (NR2A and NR2B content) are important predictors of node loss and remodeling. Mechanistically, stretch injury caused a reduction in voltage-dependent Mg2+ block of the NR2B-cotaning NMDA receptors, resulting in increased uncorrelated activity both at the single channel and network level. The reduced coincidence detection of the NMDA receptor and overactivation of these receptors further impaired network function and plasticity. Given the demonstrated link between NR2B-NMDARs and mitochondrial dysfunction, we discovered that neuronal de-integration from the network is mediated through mitochondrial signaling. Finally, we bridged these network level studies with an investigation of changes in neurobehavior following blast-induced traumatic brain injury (bTBI), a form of mild TBI. We first developed and validated an open-source toolbox for automating the scoring of several common behavior tasks to study the deficits that occur following bTBI. We then specifically evaluated the role of neuronal transcription factor Elk-1 in mediating deficits following blast by exposing Elk-1 knockout mouse to equivalent blast pressure loading. Our systems-level behavior analysis showed that bTBI creates a complex change in behavior, with an increase in anxiety and loss of habituation in object recognition. Moreover, we found these behavioral deficits were eliminated in Elk-1 knockout animals exposed to blast loading. Together, we merged information from different perspectives (in silico, in vitro, and in vivo) and length scales (single channels, single-cells, networks, and animals) to study the impact of mild traumatic brain injury on neuronal networks and neurobehavior

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong