A Multiscale “Working Brain” Model

Robinson, P. A.; Postnova, Svetlana; Abeysuriya, Romesh; Kim, Jong Won; Roberts, James A.; McKenzie-Sell, Lauren; Karanjai, Angela; Kerr, Cliff C.; Fung, Felix; Anderson, R. M.; Breakspear, Michael; Drysdale, P.M.; Fulcher, Ben D.; Phillips, Andrew J. K.; Rennie, Chris; Yin, Guofu

doi:10.1007/978-3-319-20037-8_5

Cited by 13 publications

(11 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 5A depicts the model output power content against the input stimulus frequency, and is an attempt to compare with a similar classic depiction of experimental data corresponding to SSVEP in human EEG by Hermann ( 2001 ). However, Hermann's depiction was modified in subsequent research with the input stimuli frequencies (the independent variables) along the abscissa (Robinson et al, 2015 ). The latter approach is adopted by Labecki et al ( 2016 ) as well as in this work as can be seen in Figures 5A , 6A .…”

Section: Resultsmentioning

confidence: 99%

A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine

et al. 2017

View full text Add to dashboard Cite

We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN) developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building massively parallel neuro-computational frameworks. The LGN model has 140 neurons representing a “basic building block” for larger modular architectures. The motivation of this work is to simulate biologically plausible LGN dynamics on SpiNNaker. Synaptic layout of the model is consistent with biology. The model response is validated with existing literature reporting entrainment in steady state visually evoked potentials (SSVEP)—brain oscillations corresponding to periodic visual stimuli recorded via electroencephalography (EEG). Periodic stimulus to the model is provided by: a synthetic spike-train with inter-spike-intervals in the range 10–50 Hz at a resolution of 1 Hz; and spike-train output from a state-of-the-art electronic retina subjected to a light emitting diode flashing at 10, 20, and 40 Hz, simulating real-world visual stimulus to the model. The resolution of simulation is 0.1 ms to ensure solution accuracy for the underlying differential equations defining Izhikevichs neuron model. Under this constraint, 1 s of model simulation time is executed in 10 s real time on SpiNNaker; this is because simulations on SpiNNaker work in real time for time-steps dt ⩾ 1 ms. The model output shows entrainment with both sets of input and contains harmonic components of the fundamental frequency. However, suppressing the feed-forward inhibition in the circuit produces subharmonics within the gamma band (>30 Hz) implying a reduced information transmission fidelity. These model predictions agree with recent lumped-parameter computational model-based predictions, using conventional computers. Scalability of the framework is demonstrated by a multi-node architecture consisting of three “nodes,” where each node is the “basic building block” LGN model. This 420 neuron model is tested with synthetic periodic stimulus at 10 Hz to all the nodes. The model output is the average of the outputs from all nodes, and conforms to the above-mentioned predictions of each node. Power consumption for model simulation on SpiNNaker is ≪1 W.

show abstract

Section: Resultsmentioning

confidence: 99%

A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine

et al. 2017

View full text Add to dashboard Cite

show abstract

“…However, in order to reproduce sleep-wake cycles these models require input from either the sleep regulatory networks or the two-process model. Recently, several attempts were made to connect mean field EEG and sleep regulatory models to allow for investigation of the links between the ascending arousal system and thalamocortical system in generation of EEG and sleep-wake behaviour [ 85 , 99 ]. Costa et al, in particular, combined the neural mass sleep regulatory model by Diniz Behn et al [ 101 ] with the cortical model by Weigenand et al [ 97 ] thus enabling simulation of 24-h sleep-wake cycles and study of the links between different sleep regulatory populations and the EEG.…”

Section: Discussionmentioning

confidence: 99%

“…This allowed for faster simulations, so that only a few seconds of computer time are needed to simulate a week of real time. This made this model very attractive for studying real world applications where a simulation of a large number of days is of interest, such as shift work [ 165 , 166 , 167 ], jetlag [ 85 ], effects of ageing [ 168 , 169 ] and prediction of alertness [ 71 , 170 ].…”

Section: Discussionmentioning

confidence: 99%

“…Majority of these models simulate the interaction among excitatory and inhibitory cortical populations and thalamic relay and reticular nuclei involved in generation of the EEG [ 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ]; some of the models account for cortical dynamics without explicitly incorporating the thalamus [ 96 , 97 ]. Collectively, these models reproduce sleep and wake EEG features and propose mechanisms for how these features are generated at the level of interacting neuronal populations.…”

Section: Brain Areas—mean Field Dynamicsmentioning

confidence: 99%

“…In 2016 Costa et al [ 99 ] proposed a first model combining cortical and sleep regulatory populations with the homeostatic and circadian processes, which allows generation of the EEG across the sleep-wake and REMS-NREMS cycles and investigation of the role of subthalamic regions in cortical dynamics. Earlier, in 2015, Robinson et al, [ 85 , 88 ] proposed a framework for connecting the corticothalamic neural field model [ 84 ] with the mutual inhibition model of the ascending arousal system [ 100 ] into a unified “working” brain model but this is yet to be done numerically.…”

Section: Brain Areas—mean Field Dynamicsmentioning

confidence: 99%

See 2 more Smart Citations

Sleep Modelling across Physiological Levels

Postnova

2019

Clocks & Sleep

View full text Add to dashboard Cite

Sleep and circadian rhythms are regulated across multiple functional, spatial and temporal levels: from genes to networks of coupled neurons and glial cells, to large scale brain dynamics and behaviour. The dynamics at each of these levels are complex and the interaction between the levels is even more so, so research have mostly focused on interactions within the levels to understand the underlying mechanisms—the so-called reductionist approach. Mathematical models were developed to test theories of sleep regulation and guide new experiments at each of these levels and have become an integral part of the field. The advantage of modelling, however, is that it allows us to simulate and test the dynamics of complex biological systems and thus provides a tool to investigate the connections between the different levels and study the system as a whole. In this paper I review key models of sleep developed at different physiological levels and discuss the potential for an integrated systems biology approach for sleep regulation across these levels. I also highlight the necessity of building mechanistic connections between models of sleep and circadian rhythms across these levels.

show abstract

A Neural Mass Computational Framework to Study Synaptic Mechanisms Underlying Alpha and Theta Rhythms

Bhattacharya

Durrant

2017

Computational Neurology and Psychiatry

View full text Add to dashboard Cite

A Multiscale “Working Brain” Model

Cited by 13 publications

References 92 publications

A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine

A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine

Sleep Modelling across Physiological Levels

A Neural Mass Computational Framework to Study Synaptic Mechanisms Underlying Alpha and Theta Rhythms

Contact Info

Product

Resources

About