[go: up one dir, main page]

Medini et al., 2014 - Google Patents

Computationally efficientbio-realistic reconstructions of cerebellar neuron spiking patterns

Medini et al., 2014

View PDF
Document ID
9035718078571617532
Author
Medini C
Vijayan A
D'Angelo E
Nair B
Diwakar S
Publication year
Publication venue
Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing

External Links

Snippet

Simple spiking models have been known to replicate detailed mathematical models firing properties with reliable accuracy in spike timing. We modified the adaptive exponential integrate and fire mathematical model to reconstruct different cerebellar neuronal firing …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G06N3/049—Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning

Similar Documents

Publication Publication Date Title
Mazzoni et al. Computing the local field potential (LFP) from integrate-and-fire network models
Brette et al. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity
Deco et al. The dynamic brain: from spiking neurons to neural masses and cortical fields
Rall Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input.
Battaglia et al. Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex
Kameneva et al. Modelling intrinsic electrophysiological properties of ON and OFF retinal ganglion cells
Neishabouri et al. Axonal noise as a source of synaptic variability
Geminiani et al. Complex dynamics in simplified neuronal models: reproducing Golgi cell electroresponsiveness
Steuber et al. Modeling the generation of output by the cerebellar nuclei
El Boustani et al. Stable learning in stochastic network states
Murakami et al. Contribution of ionic currents to magnetoencephalography (MEG) and electroencephalography (EEG) signals generated by guinea‐pig CA3 slices
Medini et al. Computationally efficientbio-realistic reconstructions of cerebellar neuron spiking patterns
Antunes et al. Mirror neurons modeled through spike-timing-dependent plasticity are affected by channelopathies associated with autism spectrum disorder
Zhang et al. Ionic mechanisms underlying history-dependence of conduction delay in an unmyelinated axon
Naud et al. Counting on dis-inhibition: a circuit motif for interval counting and selectivity in the anuran auditory system
Creamer et al. Bridging the gap between the connectome and whole-brain activity in C. elegans
Herice et al. Decision making under uncertainty in a spiking neural network model of the basal ganglia
Gerstner Hebbian learning and plasticity
Wang et al. First-spike latency in Hodgkin's three classes of neurons
Hutt The population firing rate in the presence of GABAergic tonic inhibition in single neurons and application to general anaesthesia
Häusler et al. Natural image sequences constrain dynamic receptive fields and imply a sparse code
Ujfalussy et al. Parallel computational subunits in dentate granule cells generate multiple place fields
Marreiros et al. DCM, conductance based models and clinical applications
Nair Auditory fear circuits in the amygdala–insights from computational models
Valova et al. Modeling of inhibition/excitation firing in olfactory bulb through spiking neurons