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Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and constant neural reorganization? Progress towards answering many of these questions can be pursued through large-scale neuronal simulations. In this thesis, a Hebbian learning rule for spiking neurons inspired by statistical inference is introduced. The spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule involves changes in both synaptic strengths and intrinsic neuronal currents. The model is motivated by molecular cascades whose functional outcomes are mapped onto biological mechanisms such as Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability. Temporally interacting memory traces enable spike-timing dependence, a stable learning regime that remains competitive, postsynaptic activity regulation, spike-based reinforcement learning and intrinsic graded persistent firing levels. The thesis seeks to demonstrate how multiple interacting plasticity mechanisms can coordinate reinforcement, auto- and hetero-associative learning within large-scale, spiking, plastic neuronal networks. Spiking neural networks can represent information in the form of probability distributions, and a biophysical realization of Bayesian computation can help reconcile disparate experimental observations. QC 20170421

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 x 104 neurons and 5.1 x 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 45x 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.

A collection of folklore and local history stories from Duleek (B.) (school) (Duleek, Co. Meath), collected as part of the Schools' Folklore Scheme, 1937-1938 under the supervision of teacher Micheál Ó Braonáin., Focla Gaeilge atá in Úsáid Fós i mBéal na mBéarlóirí i gCo. na Mí / Braonáin, Micheál Ó -- Ainmneacha Lustraí / Braonáin, Micheál Ó -- Páirceanna / Braonáin, Micheál Ó -- Duleek -- Weather / Gormáin, Séamas Ó -- Weather / Cionnaith, Tomas Mac -- Weather / Tulaigh, Pilib Ó -- Weather / Conchubhair, Seán Ó -- Rocks of Carragubbin, Duleek / Gormáin, Seamas Ó -- Rocks of Carragubbin, Duleek / Conchubhair, Seán Ó -- Ethnographic -- Duleek Ghost Stories / Tulaigh, Pilib Ó -- Duleek Ghost Stories / Cionnaith, Tomás Mac -- Duleek Ghost Stories / Cionnaith, Tomás Mac -- Duleek Ghost Stories / Gormáin, Seamas Ó -- Duleek Ghost Stories / Conchubhair, Seán Ó -- Local Heroes - Collier the Robber / Conchubhair, Seán Ó -- Collier the Robber / Gormáin, Seámas Ó -- Collier the Robber / Tulaigh, Pilib Ó -- Hidden Treasure / Gormáin, Seumas Ó -- Hidden Treasure / Conchubhair, Seán Ó -- Hidden Treasure / Baoighill, Éanna Ó -- Hidden Treasure / Cionnaith, Séumas Mac -- Hidden Treasure / Cluintiúin, Padraig -- Hidden Treasure / Stanáin, Pilib Mac Giolla -- Hidden Treasure / Gabhann, Liam Mac -- Weaving Industry of Duleek of Former Times / Braonáin, Ronan Ó -- Weaving Industry of Duleek of Former Times / Gormáin, Seumas Ó -- Weaving Industry / Conchubhair, Seán Ó -- Old Mills of Duleek / Conchubhair, Seán Ó -- Old Mills of Duleek / Gormáin, Seumas Ó -- Beaumond Mill / , Seán -- Duleek Mill / Lochráin, Brian Ó -- Remains of Old Churches About Duleek / Gormáin, Seumas Ó -- Old Churches of Duleek / Conchubhair, Seán Ó -- Old Churches of Duleek / Braonáin, Ronan Ó -- Remains of Old Churches Around Duleek / Lochráin, Brian Ó -- Remains of Old Churches Around Duleek / Tulaigh, Pilib Ó -- Remains of Old Churches - St Cianán / Pudarlaigh, Peadar -- Remains of Old Churches - St Cianán / Colmáin, Padraig Ó -- St Cianán Patron of Duleek / Gabhann, Liam Mac -- Another Legend of St Cianán / Baoighill, Éanna Ó -- Wells of Duleek - St Cianán / Gormáin, Seumas Ó -- Wells of Duleek- St Cianán's Well / Conchubhair, Seán Ó -- The Wells of Duleek / Braonáin, Ronan Ó -- St Cianán's Well / Lochráin, Brian Ó -- St Cianán's Well / Seanan, Pilib Mac Giolla -- Standing on a Stray Sod / Gormáin, Seumas Ó -- Raspberry Production in Duleek / Moss, Pete -- Raspberry Production in Duleek - Preparation of the Soil / Moss, Pete -- Raspberry Production in Duleek - Planting / Moss, Pete -- Raspberry Production in Duleek - Cultivation / Moss, Pete -- Raspberry Production in Duleek - Manuring / Moss, Pete -- Raspberry Production in Duleek - Harvesting the Crop / Moss, Pete -- Raspberry Production in Duleek - Marketing the Fruit / Moss, Pete -- Cromwell and Duleek / Baoighill, Éanna Ó -- Cromwell and Duleek / Gormáin, Seumas Ó -- Cromwell and Duleek / Conchubhair, Seán Ó -- Cromwell and Duleek / Brennan, Ronan -- Duleek - Games of Former Times / Gormáin, Séumas Ó -- Duleek Games / Tulaigh, Pilib Mac an -- Duleek Games - Jackstones / Kenna, Jane Mc -- May Day Customs in Old Duleek / Gormáin, Seumas Ó -- May Day Customs in Old Duleek / Conchubhair, Seán Ó -- May Day Customs in Old Duleek / Brennan, Ronan -- May Day Customs in Old Duleek / Cionnaith, Tomas Mac -- Remarkable Finds at Keenoge, Duleek / Braonáin, Micheál Ó -- Rushlights / Gormáin, Seumas Ó -- Rushlights / Tully, Philip -- Rushlights / Clarke, Brendan -- Rushlights / Cionnaith, Tomas Mac -- Old Graveyard of Duleek (now disused) / Gormáin, Seumas Ó -- Old Duleek Graveyard - Now Disused / Pudarlaigh, Peadar -- The Caterpillar Known in Duleek as God's Horse / Conchubhair, Seán Ó -- Caterpillar Known in Duleek as God's Horse / Cluintiuin, Padraig -- Duleek and the Battle of the Boyne / Baoighill, Éanna Ó -- Duleek and the Battle of the Boyne / Brennan, Ronan -- Duleek and the Battle of the Boyne / Connor, John -- Wakes and Funerals / Conchubhair, Seán Ó -- Wakes and Funerals / Gormáin, Séumas Ó -- Dally-Iron / Braonáin, Micheál Ó -- Irish Caoin / Braonáin, Micheál Ó -- A list of successive managers of the present national school as far back as memory goes. -- Things Lucky and Unlucky / Gormáin, Séumas Ó -- Things Lucky and Unlucky / Connor, John -- Local Heroes / Gormáin, Séumas Ó -- Hallow Eve Customs in Duleek / Gormáin, Séumas Ó -- Hallow Eve Customs / Brennan, Ronan -- Races on the Commons, Duleek / Gormáin, Séumas Ó -- Potato Crop / Gormáin, Seumas Ó -- Potato Crop / Tulaigh, Pilib Ó -- Big Wind / Gormáin, Seúmas Ó -- Churning / Gorman, Seumas -- Churning / Cionnaith, Seumas Mac -- Christmas / Boyle, Enda O' -- Evictions in Duleek / Gormáin, Seumas Ó -- Evictions in Duleek / Conchubhair, Seán Ó -- Cures / Gormáin, Seumas Ó -- Cures / Brennan, Ronan -- Cures / Conchubhair, Seán Ó -- Cures / Tully, Philip -- Cures / Boyle, Enda O' -- Cures / Cionnaith, Seumas Mac -- Boys' School, Duleek -- Schools, Duleek / Gormáin, Seumas Ó -- Girls' School -- Schools, Duleek / Conchubhair, Seán Ó -- Bronze Age Battle-Axe -- Ruins of Duleek / Braonáin, Micheál Ó -- Weasel / Gormáin, Seumas Ó -- Sally Picker or Pecker - A Bird / Sampson, Seán -- Sally Picker / Cionnaith, Tomás Mac, Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.

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.