Simulation of alcohol action upon a detailed Purkinje neuron model and a simpler surrogate model that runs >400 times faster

Background: An approach to investigate brain function/dysfunction is to simulate neuron circuits on a computer. A problem, however, is that detailed neuron descriptions are computationally expensive and this handicaps the pursuit of realistic network investigations, where many neurons need to be simulated. Results: We confront this issue; we employ a novel reduction algorithm to produce a 2 compartment model of the cerebellar Purkinje neuron from a previously published, 1089 compartment model. It runs more than 400 times faster and retains the electrical behavior of the full model. So, it is more suitable for inclusion in large network models, where computational power is a limiting issue. We show the utility of this reduced model by demonstrating that it can replicate the full model’s response to alcohol, which can in turn reproduce experimental recordings from Purkinje neurons following alcohol application. Conclusions: We show that alcohol may modulate Purkinje neuron firing by an inhibition of their sodium-potassium pumps. We suggest that this action, upon cerebellar Purkinje neurons, is how alcohol ingestion can corrupt motor co-ordination. In this way, we relate events on the molecular scale to the level of behavior

MICRO-STIMULATION OF NEURONS

This project encompasses the basic understanding of neuropharmacology. Neuropharmacology is the study of how drugs affect cellular function in the nervous system, and the neural mechanisms through which they influence behavior. There are two main branches of neuropharmacology: behavioral and molecular. Behavioral neuropharmacology focuses on the study of how drugs affect human behavior (neuropsychopharmacology), including the study of how drug dependence and addiction affect the human brain.Molecular neuropharmacology involves the study of neurons and their neurochemical interactions, with the overall goal of developing drugs that have beneficial effects on neurological function. Both of these fields are closely connected, since both are concerned with the interactions of neurotransmitters, neuropeptides, neurohormones, neuromodulators, enzymes, second messengers, co-transporters, ion channels, and receptor proteins in the central and peripheral nervous systems. Studying these interactions, researchers are developing drugs to treat many different neurological disorders, including pain, neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, psychological disorders, addiction, and many others.Before understanding the effect of drugs we studied firing rates of neurons and neural networks. Initially we started off understanding firing rates and at what frequencies spikes will be recorded on the SpikerBox. We found that at low frequencies we can observe spikes. Later we started stimulating muscles in the cockroach’s leg using music. Upon varying bass, treble, etc we noticed the difference in spikes and recorded the same. After understanding the spikes we injected Nicotine and Mono Sodium Glutamate with control (water) at intervals of 2-4 minutes to observe which drug would have an effect on the neurons. MSG is present in 80% networks however for insects we found Nicotine stimulates and MSG does not

Identification and characterization of interneurons in drosophila gustatory circuitry

Precise control of feeding behaviour is essential for the survival of animals and is largely determined by internal nutritional state and food palatability. While progress has been made towards the identification of gustatory circuitry, it remains largely elusive how information regarding internal nutritional state is integrated into the feeding circuitry to modulate feeding behavior. Here we identify a new class of interneurons (VM neurons) within the gustatory circuitry of drosophila that modulate sweet sensitivity of the animal in a starvation-dependent manner. VM neurons likely receive direct input from Gr5a sweet receptor neurons and VM neurites are present in closed proximity to the branches of insulin producing cells. Conditional silencing of VM neurons impairs sweet sensitivity of starved animals while artificial activation is sufficient to elicit proboscis extension. Interestingly silencing of insulin producing cells (IPCs) increases sweet sensitivity, suggesting that IPCs exert a modulatory effect on the sensitivity of the gustatory circuitry. in a manner opposite to VM neurons. Consistent with our hypothesis that VM neurons representing a direct target of IPCs in the gustatory circuit, blocking insulin signaling in VM neurons increases sweet sensitivity in fed animals. Taken together, we identified a neuromodulatory pathway that insulin signaling translates information of internal nutritional state into changes in gustatory circuit sensitivity. This mechanism enables animals rapidly adjust their feeding behaviours to maintain a homeostatic nutritional state

Biophysics of Purkinje computation

Although others have reported and characterised different patterns of Purkinje firing (Womack and Khodakhah, 2002, 2003, 2004; McKay and Turner, 2005) this thesis is the first study that moves beyond their description and investigates the actual basis of their generation. Purkinje cells can intrinsically fire action potentials in a repeating trimodal or bimodal pattern. The trimodal pattern consists of tonic spiking, bursting and quiescence. The bimodal pattern consists of tonic spiking and quiescence. How these firing patterns are generated, and what ascertains which firing pattern is selected, has not been determined to date. We have constructed a detailed biophysical Purkinje cell model that can replicate these patterns and which shows that Na+/K+ pump activity sets the model’s operating mode. We propose that Na+/K+ pump modulation switches the Purkinje cell between different firing modes in a physiological setting and so innovatively hypothesise the Na+/K+ pump to be a computational element in Purkinje information coding. We present supporting in vitro Purkinje cell recordings in the presence of ouabain, which irreversibly blocks the Na+/K+ pump. Climbing fiber (CF) input has been shown experimentally to toggle a Purkinje cell between an up (firing) and down (quiescent) state and set the gain of its response to parallel fiber (PF) input (Mckay et al., 2007). Our Purkinje cell model captures these toggle and gain computations with a novel intracellular calcium computation that we hypothesise to be applicable in real Purkinje cells. So notably, our Purkinje cell model can compute, and importantly, relates biophysics to biological information processing. Our Purkinje cell model is biophysically detailed and as a result is very computationally intensive. This means that, whilst it is appropriate for studying properties of the 8 individual Purkinje cell (e.g. relating channel densities to firing properties), it is unsuitable for incorporation into network simulations. We have overcome this by deploying mathematical transforms to produce a simpler, surrogate version of our model that has the same electrical properties, but a lower computational overhead. Our hope is that this model, of intermediate biological fidelity and medium computational complexity, will be used in the future to bridge cellular and network studies and identify how distinctive Purkinje behaviours are important to network and system function

Encoding and control of motor prediction and feedback in the cerebellar cortex

University of Minnesota Ph.D. dissertation. August 2017. Major: Neuroscience. Advisor: Timothy Ebner. 1 computer file (PDF); xi, 162 pages.Extensive research implicates the cerebellum as a forward internal model that predicts the sensory consequences of motor commands and compares them to their actual feedback, generating prediction errors that guide motor learning. However, lacking is a characterization of how information relevant to motor control and sensory prediction error is processed by cerebellar neurons. Of major interest is the contribution of Purkinje cells, the primary output neurons of the cerebellar cortex, and their two activity modalities: simple and complex spike discharges. The dominant hypothesis is that complex spikes serve as the sole error signal in the cerebellar cortex. However, no current hypotheses fully explain or are completely consistent with the spectrum of previous experimental observations. To address these major issues, Purkinje cell activity was recorded during a pseudo-random manual tracking task requiring the continuous monitoring and correction for errors. The first hypothesis tested by this thesis was whether climbing fiber discharge controls the information present in the simple spike firing. During tracking, complex spikes trigger robust and rapid changes in the simple spike modulation with limb kinematics and performance errors. Moreover, control of performance error information by climbing fiber discharge is followed by improved tracking performance, suggesting that it is highly important for optimizing behavior. A second hypothesis tested was whether climbing fiber discharge is evoked by errors in movement. Instead, complex spikes are modulated predictively with behavior. Additionally, complex spikes are not evoked as a result of a specific ‘event’ as has been previously suggested. Together, this suggests a novel function of complex spikes, in which climbing fibers continuously optimize the information in the simple spike firing in advance of changes in behavior. A third hypothesis tested is whether the simple spike discharge is responsible for encoding the sensory prediction errors crucial for online motor control. To address this, two novel manipulations of visual feedback during pseudo-random tracking were implemented to assess whether disrupting sensory information pertinent to motor error prediction and feedback modulates simple spike activity. During these manipulations, the simple spike modulation with behavior is consistent with the predictive and feedback components of sensory prediction error. Together, this thesis addresses a major outstanding question in the field of cerebellar physiology and develops a novel hypothesis about the interaction between the two activity modalities of Purkinje cells

Investigating the function of alpha frequency oscillatory activity

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonA fundamental challenge in modern neuroscience is to understand the role of synchronous oscillatory activity of groups of neurons in information processing. This thesis addressed the problem of how alpha frequency oscillatory activity might help control the flow of information from both the external world and from higher cognitive areas (responsible for inhibitory control, top-down and bottom-up information flow). A series of experiments investigated how alpha neuronal dynamics might aid/control cognition. In order to study the functional significance of alpha frequency oscillatory activity, the effects on performance in cognitive tasks of alpha activity directly elicited using photic stimulation were examined. Initially, we were interested in the role of alpha oscillations in information transfer across cortical areas, which was probed using a numerical Stroop task with every trial preceded by a flicker prime. The incongruent trials of the Stroop task introduce a conflict between competing responses which results in people being slower in responses to the task compared with congruent trials. That slower response has been related to increased communication between conflict processing fronto-parietal and early somatosensory regions. If alpha oscillations improve communication efficiency across the cortex it was predicted that inducing stronger alpha oscillations would affect the performance, (i.e. the Stroop cost would diminish). That hypothesis was tested in a series of three experiments. None of the manipulations (different frequencies, amplitudes induced and alpha phases where the Stroop task was initiated) showed that alpha oscillatory activity reduces the Stroop effect. However, the last task showed that people were faster when the task was preceded by an alpha frequency flicker prime, especially around 10Hz. The fourth experiment built on the well-established phenomena that when alpha activity is elicited in a particular hemisphere it attenuates processing of sensory information in that hemisphere, while the opposite hemisphere, is characterised by increased efficiency of information processing/flow. The study tested whether that could occur within a hemisphere by localised entrainment of part of the visual field. This hypothesis was tested by examining whether it could resolve differences in results previously published by Mathewson et al., (2012) and Spaak et al., (2014). In this study, a target circle was presented at time points after the offset of an alpha flicker prime, such that it was either in or out of phase with the prime. The target was displayed briefly, and then a masking ring appeared around the target location. There were two experimental conditions. First priming occurred at the central target location, and this was expected to inhibit perception at that location, (i.e. the target would be best detected at out of alpha phase time points). In contrast, in the second condition, the target surround area (e.g. the mask location) was stimulated, and this was expected to inhibit perception at that location, (i.e. the mask would be most effective in phase time points and so the target more easily detected). However, in both instances, target detection was best at in-phase time points and attenuated at out of phase time points, in line with Mathewson et al., (2012) results. This gives us some insight into the role of the alpha phase in allowing the external stimuli to be perceived/detected. The fifth experiment tested whether the level of spatial uncertainty of briefly presented target determines the alpha phase position for its best detection. This task used a similar masked circle paradigm as the fourth experiment, but the target could appear at one of two locations either side of fixation, which were both preceded by a flicker prime (either alpha frequency or randomly jittered) and followed by masking rings. The hypothesis was that the optimal alpha phase for target detection depends on whether people are pre-guided (by an arrow cue) to the target location or uncued (a higher level of spatial uncertainty). This hypothesis was again tested by examining whether it could resolve differences in results previously published by Mathewson et al., (2012) and Spaak et al., (2014). This experiment showed that the level of spatial uncertainty of briefly presented target determines the optimal alpha phase for its detection. Targets whose location was not pre-guided were the most likely to be detected when presented at time points out of phase with the entrained alpha prime; targets whose location was pre-guided by a brief arrow were the most likely to be detected when presented at time points in phase with the entrained alpha prime. The sixth experiment used EEG to investigate the neural dynamics underlying the behaviourally tested phenomenon in the previous experiment. Results showed that for targets with a high level of spatial uncertainty, the average alpha power peak was detected earlier in anterior electrodes compared with posterior electrodes, which is consistent with a greater reliance on alpha top-down dynamics. In contrast, for targets at a spatially cued location, the average alpha power peak was detected earlier at posterior electrodes, which suggests a greater reliance on bottom-up alpha neuronal dynamics. In summary, this thesis confirmed that mid-alpha phase determines the probability of detection of a briefly presented target. Also, it showed that optimal alpha phase for detecting briefly presented target would differ depending on the level of spatial uncertainty of that target. Targets at non-predictable locations are more likely to be detected at a trough in the phase of alpha activity whilst those at cued locations are most likely to be detected in-phase. Hence, perception depends not only on the internal neuronal alpha dynamics but also on the type of the visual percept. This difference may highlight the role of two different neuronal alpha sources which dominate in the different scenarios. When the target location is uncertain, top-down alpha dynamics dominate. However, when the target location is pre-guided, bottom-up alpha dynamics dominate