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Showing 1 to 14 of 14 for “"spike-timing- dependent plasticity (STDP)"”.

  1. Network Behavior Analysis of Spike Timing Dependent Plasticity (STDP) in Simulated Neural Networks

    … research. This project applies a multiplicative Spike Timing Dependent Plasticity (STDP) model to the weighted graph output from neural growth simulations and analyzes the resulting spike and weight changes over time. This preliminary investigation establishes a baseline process for understanding …

    washington Repository record for Network Behavior Analysis of Spike Timing Dependent Plasticity (STDP) in Simulated Neural Networks (opens in a new tab)

  2. Towards a neocortically-inspired ab initio cellular model of associative memory

    … network topologies that evolve according to spike-timing dependent plasticity (STDP). We desire to understand how external signals (like speech, vision, etc.) are encoded in the dynamics of such SNNs. In particular, we desire to identify and confirm the extent to which various network-level …

    uiuc Repository record for Towards a neocortically-inspired ab initio cellular model of associative memory (opens in a new tab)

  3. Optimizing Reservoir Computing Architecture for Dynamic Spectrum Sensing Applications

    … hardware. Through the adoption of triplet-based Spike-Timing-Dependent Plasticity (STDP) and various spike encoding schemes on the spectrum dataset within the LSM, we investigate the advantages offered by these proposed techniques compared to traditional LSM models on the FPGA. FPGA boards, known …

    vt Repository record for Optimizing Reservoir Computing Architecture for Dynamic Spectrum Sensing Applications (opens in a new tab)

  4. Growing synfire chains with triphasic spike-time-dependent plasticity

    … is guided by an experimentally reported spike-timing-dependent plasticity (STDP) rule, triphasic STDP, plus activity-dependent excitability. This STDP rule, which has not been previously used in computational studies, is shown to successfully develop a synfire chain in a network of binary …

    whiterose Repository record for Growing synfire chains with triphasic spike-time-dependent plasticity (opens in a new tab)

  5. Spike Processing Circuit Design for Neuromorphic Computing

    … and energy-efficient computing platforms. Spike based-neuromorphic computing systems require high performance and low power neural encoder and decoder to emulate the spiking behavior of neurons. These two spike-analog signals converting interface determine the whole spiking neuromorphic …

    vt Repository record for Spike Processing Circuit Design for Neuromorphic Computing (opens in a new tab)

  6. Investigating neuronal network dynamics : scale-invariance, preferred firing rates, and plasticity via phase-shift encoding

    … critical for advancing our knowledge of neural plasticity and computation. Scale-invariant properties, which suggest self-organised criticality (SOC), have been observed in these networks, but the mechanisms underlying these dynamics have remained unclear. Despite theoretical models, there is …

    oxford-brookes Repository record for Investigating neuronal network dynamics : scale-invariance, preferred firing rates, and plasticity via phase-shift encoding (opens in a new tab)

  7. Induction and Maintenance of Synaptic Plasticity

    … biochemical network involving calcium/calmodulin-dependent protein kinase II (CaMKII) and its regulating protein signaling cascade has been hypothesized to durably maintain the synaptic state in form of a bistable switch. Furthermore, it has been shown experimentally that CaMKII and associated …

    qucosa-diss

  8. Analog VLSI circuit design of spike-timing-dependent synaptic plasticity

    Synaptic plasticity is the ability of a synaptic connection to change in strength and is believed to be the basis for learning and memory. Currently, two types of synaptic plasticity exist. First is the spike-timing-dependent-plasticity (STDP), a timing-based protocol that suggests that the …

    mit Repository record for Analog VLSI circuit design of spike-timing-dependent synaptic plasticity (opens in a new tab)

  9. Developing digital and analog spiking neural Networks for interacting with biological Tissues in the Case Study of temporal lobe Epilepsy

    … capability of the SNN to learn and adapt using Spike-Timing-Dependent Plasticity (STDP) algorithms, laying the foundation for further hybrid experiments conducted with the IIT team. Subsequent stages of the project involved the design and fabrication of ASIC chips and PCBs to create a fully …

    sevilla Repository record for Developing digital and analog spiking neural Networks for interacting with biological Tissues in the Case Study of temporal lobe Epilepsy (opens in a new tab)

  10. Network Structures Arising from Spike-Timing Dependent Plasticity

    Spike-timing dependent plasticity (STDP), a widespread synaptic modification mechanism, is sensitive to correlations between presynaptic spike trains, and organizes neural circuits in functionally useful ways. n this dissertation, I study the structures arising from STDP in a population of synapses …

    columbia-diss Repository record for Network Structures Arising from Spike-Timing Dependent Plasticity (opens in a new tab)

  11. Constructive spiking neural networks for simulations of neuroplasticity

    … spiking neural networks and simulations of neuroplasticity. This theory is applied in the development of a constructive algorithm based on spike-timing- dependent plasticity (STDP) that achieves continual one-shot learning of hidden spike patterns through neuron construction. The theoretical …

    adelaide Repository record for Constructive spiking neural networks for simulations of neuroplasticity (opens in a new tab)

  12. Emergence and stability of complex structures from stochastic neuronal networks

    A single neuron’s connectivity is the key to understanding the network of neurons in the brain. However, it is already a complicated system and many different approaches to understanding it have been taken over the years. One way is from anatomical study, which is to observe the morphology of each …

    uiuc Repository record for Emergence and stability of complex structures from stochastic neuronal networks (opens in a new tab)

  13. Inference and Learning in Spiking Neural Networks for Neuromorphic Systems

    … network. The state and output of SNN do not only dependent on the current input, but also dependent on the history information. Another distinct property of SNN is that the information is represented, transmitted, and processed as discrete spike events, also referred to as action potentials. All …

    syracuse-diss Repository record for Inference and Learning in Spiking Neural Networks for Neuromorphic Systems (opens in a new tab)

  14. Inference And Learning In Spiking Neural Networks For Neuromorphic Systems

    … network. The state and output of SNN do not only dependent on the current input, but also dependent on the history information. Another distinct property of SNN is that the information is represented, transmitted, and processed as discrete spike events, also referred to as action potentials. All …

    syracuse-diss Repository record for Inference And Learning In Spiking Neural Networks For Neuromorphic Systems (opens in a new tab)