#nestsimulator — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #nestsimulator, aggregated by home.social.
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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The Urbanczik-Senn plasticity model is a powerful framework for understanding synaptic #plasticity in #NeuralNetworks. It integrates dendritic prediction errors to unify supervised, unsupervised, and #ReinforcementLearning under a single rule. Its predictive coding mechanism and robust learning dynamics make it valuable for simulating neural processing and exploring plasticity. Here’s a short simulation using the #NESTsimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-22-urbanczik_senn_plasticity/
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The Urbanczik-Senn plasticity model is a powerful framework for understanding synaptic #plasticity in #NeuralNetworks. It integrates dendritic prediction errors to unify supervised, unsupervised, and #ReinforcementLearning under a single rule. Its predictive coding mechanism and robust learning dynamics make it valuable for simulating neural processing and exploring plasticity. Here’s a short simulation using the #NESTsimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-22-urbanczik_senn_plasticity/
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The Urbanczik-Senn plasticity model is a powerful framework for understanding synaptic #plasticity in #NeuralNetworks. It integrates dendritic prediction errors to unify supervised, unsupervised, and #ReinforcementLearning under a single rule. Its predictive coding mechanism and robust learning dynamics make it valuable for simulating neural processing and exploring plasticity. Here’s a short simulation using the #NESTsimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-22-urbanczik_senn_plasticity/
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The Urbanczik-Senn plasticity model is a powerful framework for understanding synaptic #plasticity in #NeuralNetworks. It integrates dendritic prediction errors to unify supervised, unsupervised, and #ReinforcementLearning under a single rule. Its predictive coding mechanism and robust learning dynamics make it valuable for simulating neural processing and exploring plasticity. Here’s a short simulation using the #NESTsimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-22-urbanczik_senn_plasticity/
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The Urbanczik-Senn plasticity model is a powerful framework for understanding synaptic #plasticity in #NeuralNetworks. It integrates dendritic prediction errors to unify supervised, unsupervised, and #ReinforcementLearning under a single rule. Its predictive coding mechanism and robust learning dynamics make it valuable for simulating neural processing and exploring plasticity. Here’s a short simulation using the #NESTsimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-22-urbanczik_senn_plasticity/
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Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-01-structural_plasticity/
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Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-01-structural_plasticity/
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Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-01-structural_plasticity/
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Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-01-structural_plasticity/
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Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-01-structural_plasticity/
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🧠 Pastorelli et al. (2025) present a "simplified two-compartment #neuron with #CalciumDynamics capturing #brain-state-specific apical-amplification, -isolation and -drive". This Ca-#AdEx model replicates distinct #dendritic mechanisms across wakefulness, #NREM & #REM sleep using a compact ThetaPlanes transfer function. Cool implementation using the #NESTsimulator 💻!
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🧠 Pastorelli et al. (2025) present a "simplified two-compartment #neuron with #CalciumDynamics capturing #brain-state-specific apical-amplification, -isolation and -drive". This Ca-#AdEx model replicates distinct #dendritic mechanisms across wakefulness, #NREM & #REM sleep using a compact ThetaPlanes transfer function. Cool implementation using the #NESTsimulator 💻!
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🧠 Pastorelli et al. (2025) present a "simplified two-compartment #neuron with #CalciumDynamics capturing #brain-state-specific apical-amplification, -isolation and -drive". This Ca-#AdEx model replicates distinct #dendritic mechanisms across wakefulness, #NREM & #REM sleep using a compact ThetaPlanes transfer function. Cool implementation using the #NESTsimulator 💻!
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🧠 Pastorelli et al. (2025) present a "simplified two-compartment #neuron with #CalciumDynamics capturing #brain-state-specific apical-amplification, -isolation and -drive". This Ca-#AdEx model replicates distinct #dendritic mechanisms across wakefulness, #NREM & #REM sleep using a compact ThetaPlanes transfer function. Cool implementation using the #NESTsimulator 💻!
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🧠 Pastorelli et al. (2025) present a "simplified two-compartment #neuron with #CalciumDynamics capturing #brain-state-specific apical-amplification, -isolation and -drive". This Ca-#AdEx model replicates distinct #dendritic mechanisms across wakefulness, #NREM & #REM sleep using a compact ThetaPlanes transfer function. Cool implementation using the #NESTsimulator 💻!
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I recently played around with #RateModels using #NESTsimulator. Compared to #SNN, RM focus on average firing rates of #NeuronPopulations, simplifying analysis of large networks. They effectively capture collective dynamics like #oscillations and #synchronization, though they miss precise spike timing details. Thus, both approaches have their merits. Here is a brief overview:
🌍 https://www.fabriziomusacchio.com/blog/2025-08-28-rate_models/
#CompNeuro #Neuroscience #Python #PythonTutorial #SpikingNeuralNetwork
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I recently played around with #RateModels using #NESTsimulator. Compared to #SNN, RM focus on average firing rates of #NeuronPopulations, simplifying analysis of large networks. They effectively capture collective dynamics like #oscillations and #synchronization, though they miss precise spike timing details. Thus, both approaches have their merits. Here is a brief overview:
🌍 https://www.fabriziomusacchio.com/blog/2025-08-28-rate_models/
#CompNeuro #Neuroscience #Python #PythonTutorial #SpikingNeuralNetwork
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I recently played around with #RateModels using #NESTsimulator. Compared to #SNN, RM focus on average firing rates of #NeuronPopulations, simplifying analysis of large networks. They effectively capture collective dynamics like #oscillations and #synchronization, though they miss precise spike timing details. Thus, both approaches have their merits. Here is a brief overview:
🌍 https://www.fabriziomusacchio.com/blog/2025-08-28-rate_models/
#CompNeuro #Neuroscience #Python #PythonTutorial #SpikingNeuralNetwork
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I recently played around with #RateModels using #NESTsimulator. Compared to #SNN, RM focus on average firing rates of #NeuronPopulations, simplifying analysis of large networks. They effectively capture collective dynamics like #oscillations and #synchronization, though they miss precise spike timing details. Thus, both approaches have their merits. Here is a brief overview:
🌍 https://www.fabriziomusacchio.com/blog/2025-08-28-rate_models/
#CompNeuro #Neuroscience #Python #PythonTutorial #SpikingNeuralNetwork
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I recently played around with #RateModels using #NESTsimulator. Compared to #SNN, RM focus on average firing rates of #NeuronPopulations, simplifying analysis of large networks. They effectively capture collective dynamics like #oscillations and #synchronization, though they miss precise spike timing details. Thus, both approaches have their merits. Here is a brief overview:
🌍 https://www.fabriziomusacchio.com/blog/2025-08-28-rate_models/
#CompNeuro #Neuroscience #Python #PythonTutorial #SpikingNeuralNetwork
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Here is a direct follow-up on this, now showing how to implement #GapJunctions in a network of #spiking #neurons (#SNN) using the #NESTsimulator. We simulate a network of 500 inhibitory neurons with gap junctions and analyze the effects on #synchrony and #oscillations. The code is also available on GitHub. Feel free to modify and expand upon it 🤗
🌍 https://www.fabriziomusacchio.com/blog/2025-09-17-gap_junctions_network_example/
#CompNeuro #Neuroscience https://sigmoid.social/@pixeltracker/115044925455984072
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Here is a direct follow-up on this, now showing how to implement #GapJunctions in a network of #spiking #neurons (#SNN) using the #NESTsimulator. We simulate a network of 500 inhibitory neurons with gap junctions and analyze the effects on #synchrony and #oscillations. The code is also available on GitHub. Feel free to modify and expand upon it 🤗
🌍 https://www.fabriziomusacchio.com/blog/2025-09-17-gap_junctions_network_example/
#CompNeuro #Neuroscience https://sigmoid.social/@pixeltracker/115044925455984072
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Here is a direct follow-up on this, now showing how to implement #GapJunctions in a network of #spiking #neurons (#SNN) using the #NESTsimulator. We simulate a network of 500 inhibitory neurons with gap junctions and analyze the effects on #synchrony and #oscillations. The code is also available on GitHub. Feel free to modify and expand upon it 🤗
🌍 https://www.fabriziomusacchio.com/blog/2025-09-17-gap_junctions_network_example/
#CompNeuro #Neuroscience https://sigmoid.social/@pixeltracker/115044925455984072
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Here is a direct follow-up on this, now showing how to implement #GapJunctions in a network of #spiking #neurons (#SNN) using the #NESTsimulator. We simulate a network of 500 inhibitory neurons with gap junctions and analyze the effects on #synchrony and #oscillations. The code is also available on GitHub. Feel free to modify and expand upon it 🤗
🌍 https://www.fabriziomusacchio.com/blog/2025-09-17-gap_junctions_network_example/
#CompNeuro #Neuroscience https://sigmoid.social/@pixeltracker/115044925455984072
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Here is a direct follow-up on this, now showing how to implement #GapJunctions in a network of #spiking #neurons (#SNN) using the #NESTsimulator. We simulate a network of 500 inhibitory neurons with gap junctions and analyze the effects on #synchrony and #oscillations. The code is also available on GitHub. Feel free to modify and expand upon it 🤗
🌍 https://www.fabriziomusacchio.com/blog/2025-09-17-gap_junctions_network_example/
#CompNeuro #Neuroscience https://sigmoid.social/@pixeltracker/115044925455984072
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📝 New blog post: #GapJunctions (#ElectricalSynapses) enable direct electrical and chemical communication between #neurons, synchronizing activity and supporting rapid signal propagation. Their #modeling is crucial for understanding #NeuralNetworkDynamics, #oscillations, and #brain 🧠 function. Here is a brief summary including a small #PythonTutorial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2025-08-15-gap_junctions/
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📝 New blog post: #GapJunctions (#ElectricalSynapses) enable direct electrical and chemical communication between #neurons, synchronizing activity and supporting rapid signal propagation. Their #modeling is crucial for understanding #NeuralNetworkDynamics, #oscillations, and #brain 🧠 function. Here is a brief summary including a small #PythonTutorial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2025-08-15-gap_junctions/
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📝 New blog post: #GapJunctions (#ElectricalSynapses) enable direct electrical and chemical communication between #neurons, synchronizing activity and supporting rapid signal propagation. Their #modeling is crucial for understanding #NeuralNetworkDynamics, #oscillations, and #brain 🧠 function. Here is a brief summary including a small #PythonTutorial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2025-08-15-gap_junctions/
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📝 New blog post: #GapJunctions (#ElectricalSynapses) enable direct electrical and chemical communication between #neurons, synchronizing activity and supporting rapid signal propagation. Their #modeling is crucial for understanding #NeuralNetworkDynamics, #oscillations, and #brain 🧠 function. Here is a brief summary including a small #PythonTutorial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2025-08-15-gap_junctions/
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📝 New blog post: #GapJunctions (#ElectricalSynapses) enable direct electrical and chemical communication between #neurons, synchronizing activity and supporting rapid signal propagation. Their #modeling is crucial for understanding #NeuralNetworkDynamics, #oscillations, and #brain 🧠 function. Here is a brief summary including a small #PythonTutorial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2025-08-15-gap_junctions/
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In 2000, Nicolas Brunel presented a framework for studying sparsely connected #SpikingNeuralNetworks (#SNN) with random connectivity & varied excitation-inhibition balance. The model, characterized by high sparseness & low firing rates, captures diverse neural dynamics such as synchronized regular and asynchronous irregular activity and global oscillations. Here is a brief summary of these concepts & a #PythonTuroial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2024-07-21-brunel_network/
#CompNeuro #Neuroscience -
In 2000, Nicolas Brunel presented a framework for studying sparsely connected #SpikingNeuralNetworks (#SNN) with random connectivity & varied excitation-inhibition balance. The model, characterized by high sparseness & low firing rates, captures diverse neural dynamics such as synchronized regular and asynchronous irregular activity and global oscillations. Here is a brief summary of these concepts & a #PythonTuroial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2024-07-21-brunel_network/
#CompNeuro #Neuroscience -
In 2000, Nicolas Brunel presented a framework for studying sparsely connected #SpikingNeuralNetworks (#SNN) with random connectivity & varied excitation-inhibition balance. The model, characterized by high sparseness & low firing rates, captures diverse neural dynamics such as synchronized regular and asynchronous irregular activity and global oscillations. Here is a brief summary of these concepts & a #PythonTuroial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2024-07-21-brunel_network/
#CompNeuro #Neuroscience -
In 2000, Nicolas Brunel presented a framework for studying sparsely connected #SpikingNeuralNetworks (#SNN) with random connectivity & varied excitation-inhibition balance. The model, characterized by high sparseness & low firing rates, captures diverse neural dynamics such as synchronized regular and asynchronous irregular activity and global oscillations. Here is a brief summary of these concepts & a #PythonTuroial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2024-07-21-brunel_network/
#CompNeuro #Neuroscience -
In 2000, Nicolas Brunel presented a framework for studying sparsely connected #SpikingNeuralNetworks (#SNN) with random connectivity & varied excitation-inhibition balance. The model, characterized by high sparseness & low firing rates, captures diverse neural dynamics such as synchronized regular and asynchronous irregular activity and global oscillations. Here is a brief summary of these concepts & a #PythonTuroial using the #NESTsimulator.
🌍 https://www.fabriziomusacchio.com/blog/2024-07-21-brunel_network/
#CompNeuro #Neuroscience -
This #tutorial explores the oscillatory #PopulationDynamics of generalized #IntegrateAndFire (GIF) neurons simulated with #NESTSimulator. The GIF #NeuronModel is a biophysically detailed model that captures the essential features of spiking neurons, including #SpikeFrequencyAdaptation and #DynamicThreshold behavior:
🌍 https://www.fabriziomusacchio.com/blog/2024-07-14-oscillating_gif_neuron_population/
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This #tutorial explores the oscillatory #PopulationDynamics of generalized #IntegrateAndFire (GIF) neurons simulated with #NESTSimulator. The GIF #NeuronModel is a biophysically detailed model that captures the essential features of spiking neurons, including #SpikeFrequencyAdaptation and #DynamicThreshold behavior:
🌍 https://www.fabriziomusacchio.com/blog/2024-07-14-oscillating_gif_neuron_population/
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This #tutorial explores the oscillatory #PopulationDynamics of generalized #IntegrateAndFire (GIF) neurons simulated with #NESTSimulator. The GIF #NeuronModel is a biophysically detailed model that captures the essential features of spiking neurons, including #SpikeFrequencyAdaptation and #DynamicThreshold behavior:
🌍 https://www.fabriziomusacchio.com/blog/2024-07-14-oscillating_gif_neuron_population/
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This #tutorial explores the oscillatory #PopulationDynamics of generalized #IntegrateAndFire (GIF) neurons simulated with #NESTSimulator. The GIF #NeuronModel is a biophysically detailed model that captures the essential features of spiking neurons, including #SpikeFrequencyAdaptation and #DynamicThreshold behavior:
🌍 https://www.fabriziomusacchio.com/blog/2024-07-14-oscillating_gif_neuron_population/
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This #tutorial explores the oscillatory #PopulationDynamics of generalized #IntegrateAndFire (GIF) neurons simulated with #NESTSimulator. The GIF #NeuronModel is a biophysically detailed model that captures the essential features of spiking neurons, including #SpikeFrequencyAdaptation and #DynamicThreshold behavior:
🌍 https://www.fabriziomusacchio.com/blog/2024-07-14-oscillating_gif_neuron_population/
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It’s actually very easy and straightforward setting up a large-scale, multi-population #SpikingNeuralNetwork (#SNN) with the #NESTsimulator. Here is an example with two distinct populations of #Izhikevich neurons:
🌍 https://www.fabriziomusacchio.com/blog/2024-06-30-nest_izhikevich_snn/
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It’s actually very easy and straightforward setting up a large-scale, multi-population #SpikingNeuralNetwork (#SNN) with the #NESTsimulator. Here is an example with two distinct populations of #Izhikevich neurons:
🌍 https://www.fabriziomusacchio.com/blog/2024-06-30-nest_izhikevich_snn/
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It’s actually very easy and straightforward setting up a large-scale, multi-population #SpikingNeuralNetwork (#SNN) with the #NESTsimulator. Here is an example with two distinct populations of #Izhikevich neurons:
🌍 https://www.fabriziomusacchio.com/blog/2024-06-30-nest_izhikevich_snn/
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It’s actually very easy and straightforward setting up a large-scale, multi-population #SpikingNeuralNetwork (#SNN) with the #NESTsimulator. Here is an example with two distinct populations of #Izhikevich neurons:
🌍 https://www.fabriziomusacchio.com/blog/2024-06-30-nest_izhikevich_snn/
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It’s actually very easy and straightforward setting up a large-scale, multi-population #SpikingNeuralNetwork (#SNN) with the #NESTsimulator. Here is an example with two distinct populations of #Izhikevich neurons:
🌍 https://www.fabriziomusacchio.com/blog/2024-06-30-nest_izhikevich_snn/