Search

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]

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.

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]