| | Models | Description |
| 1. |
FNS spiking neural simulator; LIFL neuron model, event-driven simulation (Susi et al 2021)
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FNS is an event-driven Spiking Neural Network simulator, oriented to data-driven simulations.
FNS combines spiking/synaptic level description with the event-driven approach, allowing the user to define heterogeneous modules and multi-scale connectivity with delayed connections and plastic synapses, providing fast simulations at the same time. A novel parallelization strategy is also implemented in order to further speed up simulations.
FNS is based on the Leaky-Integrate and Fire with Latency (LIFL) spiking neuron model, that combines some realistic neurocomputational features to low computational complexity.
FNS is written in Java, distributed as open source and protected by the GPL license. |
| 2. |
Human L5 Cortical Circuit (Guet-McCreight)
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We used L5 Pyr neuron models fit to electrophysiology data from younger and older individuals to simulate detailed human layer 5 microcircuits. These circuits also included detailed parvalbumin+ (PV), somatostatin+ (SST), and vasoactivate intestinal polypeptide+ (VIP) inhibitory interneuron models. |
| 3. |
Human layer 2/3 cortical microcircuits in health and depression (Yao et al, 2022)
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| 4. |
Human tactile FA1 neurons (Hay and Pruszynski 2020)
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"... we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3°) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. ... our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs supports discrimination of coarser orientations and maintains robustness over longer timescales" |
