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Introduction: Although increased cholesterol level has been acknowledged as a risk factor for dementia, evidence synthesis based on published data has yielded mixed results. This is especially relevant in older adults where individual studies report non-linear relationships between cholesterol and cognition and, in some cases, find higher cholesterol associated with a lower risk of subsequent cognitive decline or dementia. Prior evidence synthesis based on published results has not allowed us to focus on older adults or to standardize analyses across studies. Given our ageing population, an increased risk of dementia in older adults, and the need for proportionate treatment in this age group, an individual participant data (IPD) meta-analysis is timely. Method: We combined data from 8 studies and over 21,000 participants aged 60 years and over in a 2-stage IPD to examine the relationship between total, high-density, and low-density lipoprotein (HDL and LDL) cholesterol and subsequent incident dementia or cognitive decline, with the latter categorized using a reliable change index method. Results: Meta-analyses found no relationship between total, HDL, or LDL cholesterol (per millimoles per litre increase) and risk of cognitive decline in this older adult group averaging 76 years of age. For total cholesterol and cognitive decline: odds ratio (OR) 0.93 (95% confidence interval [CI] 0.86: 1.01) and for incident dementia: OR 1.01 [95% CI 0.89: 1.13]. This was not altered by rerunning the analyses separately for statin users and non-users or by the presence of an APOE e4 allele. Conclusion: There were no clear consistent relationships between cholesterol and cognitive decline or dementia in this older adult group, nor was there evidence of effect modification by statin use. Further work is needed in younger populations to understand the role of cholesterol across the life-course and to identify any relevant intervention points. This is especially important if modification of cholesterol is to be further evaluated for its potential influence on risk of cognitive decline or dementia. [ABSTRACT FROM AUTHOR]

The brain enables animals to behaviorally adapt in order to survive in a complex and dynamic environment, but how reward-oriented behaviors are achieved and computed by its underlying neural circuitry is an open question. To address this concern, we have developed a spiking model of the basal ganglia (BG) that learns to dis-inhibit the action leading to a reward despite ongoing changes in the reward schedule. The architecture of the network features the two pathways commonly described in BG, the direct (denoted D1) and the indirect (denoted D2) pathway, as well as a loop involving striatum and the dopaminergic system. The activity of these dopaminergic neurons conveys the reward prediction error (RPE), which determines the magnitude of synaptic plasticity within the different pathways. All plastic connections implement a versatile four-factor learning rule derived from Bayesian inference that depends upon pre- and post-synaptic activity, receptor type, and dopamine level. Synaptic weight updates occur in the D1 or D2 pathways depending on the sign of the RPE, and an efference copy informs upstream nuclei about the action selected. We demonstrate successful performance of the system in a multiple-choice learning task with a transiently changing reward schedule. We simulate lesioning of the various pathways and show that a condition without the D2 pathway fares worse than one without D1. Additionally, we simulate the degeneration observed in Parkinson's disease (PD) by decreasing the number of dopaminergic neurons during learning. The results suggest that the D1 pathway impairment in PD might have been overlooked. Furthermore, an analysis of the alterations in the synaptic weights shows that using the absolute reward value instead of the RPE leads to a larger change in D1. [ABSTRACT FROM AUTHOR]

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison. [ABSTRACT FROM AUTHOR]

SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models. [ABSTRACT FROM AUTHOR]

An abstract of the article "Probabilistic computation underlying sequence learning in a spiking attractor memory network" by Philip Tully, Henrik Lindén, Matthias H. Hennig and Anders Lansner is presented.