A modelling study of beta-amyloid induced change in hippocampal theta rhythm

Many dementia cases, such as Alzheimer’s disease (AD), are characterized by an increase in low frequency field potential oscillations. However, a definitive understanding of the effects of the beta-Amyloid peptide, which is a main marker of AD, on the low frequency theta rhythm (4-7Hz) is still unavailable. In this work, we investigate the neural mechanisms associated with beta-Amyloid toxicity using a conductance-based neuronal network model of the hippocampus CA1 region. We simulate the effects of beta-Amyloid on the A-type fast inactivating K+ channel by modulating the maximum conductance of the current in pyramidal cells, denoted by gA. Our simulation results demonstrate that as gA decreases (through A[beta]
blockage), the theta band power first increases then decreases. Thus there exists a value of gA that maximizes the theta band power. The neuronal and network mechanism underlying the change in theta rhythm is systematically analyzed. We show that the increase in theta power is due to the improved synchronization of pyramidal neurons, and the theta decrease is induced by the faster depolarisation of pyramidal neurons

Dimension Reduction and Dynamics of a Spiking Neural Network Model for Decision Making under Neuromodulation

Previous models of neuromodulation in cortical circuits have used either physiologically based networks of spiking neurons or simplified gain adjustments in low-dimensional connectionist models. Here we reduce a high-dimensional spiking neuronal network model, first to a four-population mean-field model and then to a two-population model. This provides a realistic implementation of neuromodulation in low-dimensional decision-making models, speeds up simulations by three orders of magnitude, and allows bifurcation and phase-plane analyses of the reduced models that illuminate neuromodulatory mechanisms. As modulation of excitation-inhibition varies, the network can move from unaroused states, through optimal performance to impulsive states, and eventually lose inhibition-driven winner-take-all behavior: all are clear outcomes of the bifurcation structure. We illustrate the value of reduced models by a study of the speed-accuracy tradeoff in decision making. The ability of such models to recreate neuromodulatory dynamics of the spiking network will accelerate the pace of future experiments linking behavioral data to cellular neurophysiology

Genetic algorithmic parameter optimisation of a recurrent spiking neural network model

Neural networks are complex algorithms that loosely model the behaviour of the human brain. They play a significant role in computational neuroscience and artificial intelligence. The next generation of neural network models is based on the spike timing activity of neurons: spiking neural networks (SNNs). However, model parameters in SNNs are difficult to search and optimise. Previous studies using genetic algorithm (GA) optimisation of SNNs were focused mainly on simple, feedforward, or oscillatory networks, but not much work has been done on optimising cortex-like recurrent SNNs. In this work, we investigated the use of GAs to search for optimal parameters in recurrent SNNs to reach targeted neuronal population firing rates, e.g. as in experimental observations. We considered a cortical column based SNN comprising 1000 Izhikevich spiking neurons for computational efficiency and biologically realism. The model parameters explored were the neuronal biased input currents. First, we found for this particular SNN, the optimal parameter values for targeted population averaged firing activities, and the convergence of algorithm by ~100 generations. We then showed that the GA optimal population size was within ~16-20 while the crossover rate that returned the best fitness value was ~0.95. Overall, we have successfully demonstrated the feasibility of implementing GA to optimise model parameters in a recurrent cortical based SNN.Comment: 6 pages, 6 figure

Optimality and robustness of a biophysical decision-making model under norepinephrine modulation

The locus coeruleus (LC) can exhibit tonic or phasic activity and release norepinephrine (NE) throughout the cortex, modulating cellular excitability and synaptic efficacy and thus influencing behavioral performance. We study the effects of LC-NE modulation on decision making in two-alternative forced-choice tasks by changing conductances in a biophysical neural network model, and we investigate how it affects performance measured in terms of reward rate. We find that low tonic NE levels result in unmotivated behavior and high levels in impulsive, inaccurate choices, but that near-optimal performance can occur over a broad middle range. Robustness is greatest when pyramidal cells are less strongly modulated than interneurons, and superior performance can be achieved with phasic NE release, provided only glutamatergic synapses are modulated. We also show that network functions such as sensory information accumulation and short-term memory can be modulated by tonic NE levels, and that previously-observed diverse evoked cell responses may be due to network effects

Of rodents and primates: Time-variant gain in drift-diffusion decision models

Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift-diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM’s signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys and humans, we systematically showed that time-variant gain only on the DDM’s noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM’s different components can lead to different choice behaviour, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting