Signal-to-noise ratio for this decoding was between 1.0 and 2.5, which is rather impressive given the typical variability of cortical neurons and the small number of cells used for decoding (between 5 and 20). The decoding performance is illustrated in Fig. This recursive approach is necessary in this real-time application. The decoding algorithm is a recursive algorithm for calculating the Bayesian estimate of the stimulus at time t t in term the past activity. Instead of the method described in Section 11.3, Truccolo et al. A and C are redrawn from ( 400), B follows a similar figure in ( 399). Increasing the number of cells improves the decoding performance of both the coupled model (thick line) and the optimal linear decoder (dashed lines). The signal-to-noise ratio (SNR) as function of frequency for decoding using the fully coupled model (thick line), the uncoupled model (thin line) or using an optimal linear decoder (dashed lines).
Binary light stimulus (thick black) is compared with the decoded stimulus using Bayesian MAP (dashed line, Section 11.3).ī. Aĭecoding of light stimulus from recordings of neurons in the retina.Ī. The decoding performance is expected to grow with the number of recorded neurons, as can be seen in Fig. For the fully coupled model discussed in 11.2.2, the signal-to-noise ratio can be higher than 3.5 for some frequencies. 11.11).įor most of the frequencies, the signal-to-noise ratio of the decoded signal was above one, meaning that the decoded signal was greater than the error.
The decoding performance can be quantified by evaluating the signal-to-noise ratio for different frequencies (Fig. The stimulus was a spatio-temporal binary white noise. 11.3.3 Decoding in vision and neuroprosthetics See ( 437 400) for discussion of a related bound on the posterior entropy, which can be used to bound the mutual information between the stimulus and response. ( 11.17) is often quiteĪccurate in the context of decoding. P ( 𝒙 | D ) ∝ p ( D | 𝒙 ) p ( 𝒙 ), p(\mbox.īy the Gaussian of Eq.
We focus specifically on decoding methods that rely on BayesianĪpply Bayes’ rule to obtain the posterior probability of the stimulus, A variety of statistical techniques have beenĪpplied to this problem ( 437 74 400 10) in this section, Theoretical and practical implications for the study of neural coding Information contained in a set of neural spike trains ‘Decoding’ refers to the problem of how to ‘read out’ the
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