Improving Associative Memory in a Network of Spiking Neurons

In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition.We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory

Sleep studies in mice - open and closed loop devices for untethered recording and stimulation

Sleep is an important biological processes that has been studied extensively to date. Research in sleep typically involves mice experiments that use heavy benchtop equipment or basic neural loggers to record ECoG/EMG signals which are then processed offline in workstations. These systems limit the complexity of experiments that can be carried out to only simple open loop recordings, due to either the tethered setup used, which restricts animal movements, or the lack of devices that can offer more advanced features without compromising its portability. With rising popularity in exploring more physiological features that can affect sleep, such as temperature, whose importance has been highlighted in several papers [1][2][3] and advances in optogenetic stimulation, allowing high temporal and spatial neural control, there is now an unprecedented demand for experimental setups using new closed loop paradigms. To address this, this thesis presents compact and lightweight neural logging devices that are not only capable of measuring ECoG and EMG signals for core sleep analysis but also capable of taking high resolution temperature recordings and delivering optogenetic stimulus with fully adjustable parameters. Together with its embedded on-board automatic sleep stage scoring algorithm, the device will allow researchers for the first time to be able to quickly uncover the role a neural circuit plays in sleep regulation through selective neural stimulation when the animal is under the target sleep vigilance state. Original contributions include: the development of two novel multichannel neural logging devices, one for core sleep analysis and another for closed loop experimentation; the development and implementation of a lightweight, fast and highly accurate automatic on-line sleep stage scoring algorithm; and the development of a custom optogenetic coupler that is compatible with most current optogenetic setups for LED-Optical fibre coupling.Open Acces

Enhancing memory-related sleep spindles through learning and electrical brain stimulation

Sleep has been strongly implicated in mediating memory consolidation through hippocampal-neocortical communication. Evidence suggests offline processing of encoded information in the brain during slow wave sleep (SWS), specifically during slow oscillations and spindles. In this work, we used active exploration and learning tasks to study post-experience sleep spindle density changes in rats. Experiences lead to subsequent changes in sleep spindles, but the strength and timing of the effect was task-dependent. Brain stimulation in humans and rats have been shown to enhance memory consolidation. However, the exact stimulation parameters which lead to the strongest memory enhancement have not been fully explored. We tested the efficacy of both cortical sinusoidal direct current stimulation and intracortical pulse stimulation to enhance slow oscillations and spindle density. Pulse stimulation reliably evoked state-dependent slow oscillations and spindles during SWS with increased hippocampal ripple-spindle coupling, demonstrating potential in memory enhancement

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Neuropsychology of reinforcement processes in the rat

This thesis investigated the role played by regions of the prefrontal cortex and ventral striatum in the control of rats’ behaviour by Pavlovian conditioned stimuli, and in their capacity to choose delayed reinforcement. First, the function of the anterior cingulate cortex (ACC) in simple Pavlovian conditioning tasks was addressed. The ACC is a subdivision of prefrontal cortex that has previously been suggested to be critical for the formation of stimulus–reward associations. It was found that lesions of the ACC did not prevent rats from learning a simple conditioned approach response to a conditioned stimulus (CS) predictive of food reward, or from utilizing that CS as a conditioned reinforcer subsequently. Additionally, these subjects successfully acquired a conditioned freezing response to a CS predicting footshock. However, the same animals were impaired at the acquisition of autoshaped behaviour, an impairment that has been demonstrated previously. An autoshaping deficit was also observed when lesions were made following training. The phenomenon of Pavlovian–instrumental transfer was intact in these subjects. The hypothesis was developed that the ACC is not critical for the formation of stimulus–reward associations per se, but is critical when multiple stimuli must be discriminated on the basis of their differential association with reward. In support of this hypothesis, animals with lesions of the ACC were impaired on a version of the conditioned approach task in which a second, neutral stimulus, perceptually similar to the CS, was added; the lesioned subjects exhibited reduced discrimination. Second, the role of the nucleus accumbens (Acb) in Pavlovian–instrumental transfer was investigated. The nucleus accumbens core, together with a larger amygdalar–striatal network of which it is a component, has previously been shown to be necessary for the expression of ‘simple’ Pavlovian–instrumental transfer. Rats with lesions of the nucleus accumbens core (AcbC) and shell (AcbSh) were tested on a ‘response-specific’ Pavlovian–instrumental transfer task, in which a Pavlovian CS selectively enhances instrumental responding for the outcome with which the CS was originally paired. AcbC lesions impaired the response specificity of this effect, while AcbSh lesions abolished Pavlovian–instrumental transfer entirely. These results are consistent with some — but not all — previous results in suggesting that the shell provides ‘vigour’ and the core provides ‘direction’ for the potentiation of behaviour by Pavlovian CSs. Third, an attempt was made to train rats on a task for assessing preference for delayed reinforcement, using the ‘adjusting-delay’ paradigm. It was not immediately apparent that the rats reacted to the contingencies operative in this task, and mathematical analysis of their behaviour was conducted to establish whether their behaviour was sensitive to the delay, and what ‘molar’ features of performance on this task could be explained by delay-independent processes. Fourth, a different delayed reinforcement choice task was developed, modifying a previously published task in which the subject is repeatedly offered a choice, in discrete trials, of a small reward delivered immediately, and a large reward delivered after a delay, with the delays systematically varied by the experimenter. Rats were trained on versions of this task in which the large, delayed reinforcer was or was not explicitly signalled by a cue present during the delay. The behavioural basis of performance on this task was examined, and d-amphetamine, chlordiazepoxide, and alpha-flupenthixol were administered systemically. It was found that the effects of d-amphetamine depended on whether the delayed reinforcer was signalled or unsignalled, increasing preference for signalled delayed reinforcement at some doses, but decreasing preference for unsignalled delayed reinforcement. These results may resolve contradictions in the literature, and are suggested to reflect the known effect of amphetamine to potentiate responding for conditioned reinforcers. Fifth, rats that had been trained on this task (with no explicit signals present during the delay) were given lesions of the ACC, AcbC, or medial prefrontal cortex (mPFC). ACC-lesioned rats were no different from sham-operated controls in their ability to choose a large, delayed reinforcer. Lesions of mPFC reduced the tendency of subjects to shift from one lever to the other during the course of a session, but mPFC-lesioned subjects responded normally to removal of the delays, suggesting a loss of stimulus control. However, rats with lesions of the AcbC were severely impaired on this task, preferring the small, immediate reward, even though they discriminated the reinforcers. Additionally, the effects of intra-Acb amphetamine were assessed using a different version of the delayed reinforcement choice task, and found to have slight but inconsistent effects to reduce preference for the delayed reinforcer, though this effect did not depend on whether the delayed reward was signalled or unsignalled. These results suggest that the AcbC contributes significantly to the rat’s ability to choose a delayed reward, a finding that has important implications for the understanding of Acb function. It is suggested that dysfunction of the AcbC may be a key element in the pathology of impulsivity.Supported by a UK Medical Research Council (MRC) research studentship, 1997–2000, and a James Baird award, University of Cambridge School of Clinical Medicine, 1997–2000

Sensor Fusion in the Perception of Self-Motion

This dissertation has been written at the Max Planck Institute for Biological Cybernetics (Max-Planck-Institut für Biologische Kybernetik) in Tübingen in the department of Prof. Dr. Heinrich H. Bülthoff. The work has universitary support by Prof. Dr. Günther Palm (University of Ulm, Abteilung Neuroinformatik). Main evaluators are Prof. Dr. Günther Palm, Prof. Dr. Wolfgang Becker (University of Ulm, Sektion Neurophysiologie) and Prof. Dr. Heinrich Bülthoff.amp;lt;bramp;gt;amp;lt;bramp;gt; The goal of this thesis was to investigate the integration of different sensory modalities in the perception of self-motion, by using psychophysical methods. Experiments with healthy human participants were to be designed for and performed in the Motion Lab, which is equipped with a simulator platform and projection screen. Results from psychophysical experiments should be used to refine models of the multisensory integration process, with an mphasis on Bayesian (maximum likelihood) integration mechanisms.amp;lt;bramp;gt;amp;lt;bramp;gt; To put the psychophysical experiments into the larger framework of research on multisensory integration in the brain, results of neuroanatomical and neurophysiological experiments on multisensory integration are also reviewed

Growing Brains in Silico: Integrating Biochemistry, Genetics and Neural Activity in Neurodevelopmental Simulations

Biologists\u27 understanding of the roles of genetics, biochemistry and activity in neural function is rapidly improving. All three interact in complex ways during development, recovery from injury and in learning and memory. The software system NeuroGene was written to simulate neurodevelopmental processes. Simulated neurons develop within a 3D environment. Protein diffusion, decay and receptor-ligand binding are simulated. Simulations are controlled by genetic information encoded using a novel programming language mimicking the control mechanisms of biological genes. Simulated genes may be regulated by protein concentrations, neural activity and cellular morphology. Genes control protein production, changes in cell morphology and neural properties, including learning. We successfully simulate the formation of topographic projection from the retina to the tectum. We propose a novel model of topography based on simulated growth cones. We also simulate activitydependent refinement, through which diffuse connections are modified until each retinal cell connects to only a few target cells