Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. Hover, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here,  study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise.  derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus  thus obtain an estimate of the uncertainty as ll. Furthermore,  derive a ‘spike-by-spike’ online decoding scheme that recursively updates the posterior with the arrival of each new spike.  use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as ll as neural populations.
['Sebastian Gerwinn', 'Jakob H. Macke', 'Matthias Bethge']
I am writing a research paper on a idea Bayesian population decoding of spiking neurons
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian population decoding of spiking neurons"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. Hover, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here,  study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise.  derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus  thus obtain an estimate of the uncertainty as ll. Furthermore,  derive a \u2018spike-by-spike\u2019 online decoding scheme that recursively updates the posterior with the arrival of each new spike.  use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as ll as neural populations."}]