Index of papers in PLOS Comp. Biol. that mention
  • ICA
Naoki Hiratani, Tomoki Fukai
Discussion
Furthermore, we derived an STDP-like online learning rule by considering an approximation of Bayesian ICA with sequence sampling.
Introduction
We further found a possible link between stochastic membrane dynamics and sampling process, which is necessary for neural approximation of learning algorithm of Bayesian independent component analysis ( ICA ).
Neural Bayesian ICA and blind source separation
Neural Bayesian ICA and blind source separation
Neural Bayesian ICA and blind source separation
To this end, we compared the performance of the model with that of the Bayesian ICA algorithm, in which independence of external sources is treated as a prior [46,47].
Neural mechanism of blind source separation
An effective solution for this problem is ICA [76—78], and the neural implementation of the algorithm has been studied by several authors [14,18,79,80].
STDP and Bayesian ICA
STDP and Bayesian ICA
STDP and Bayesian ICA
Our results indicated that STDP in a lateral inhibition circuit mimicked Bayesian ICA [46,47].
STDP and Bayesian ICA
By contrast, in the Bayesian ICA framework, the mixing matrix (corresponding to synaptic weight matrix) is treated as a probabilistic variable.
Suboptimality of STDP
In the Bayesian ICA framework, blind source separation can be formulized as an optimization problem, but, in this case, the problem itself is ill-defined because optimality does not guarantee the true solution.
pf = 1 — <1 — rsAofi [1 — am: ask/szy] ,qsk = 2; 3 M + 1/2>At12exp[—<k + mam/at].
We approximated this Bayesian ICA algorithm by a sequential sampling source activity instead of calculating the integral over all possible combinations in the estimation of the log-pos-terior of the response probability matriX Q.
pf = 1 — <1 — rsAofi [1 — am: ask/szy] ,qsk = 2; 3 M + 1/2>At12exp[—<k + mam/at].
We further studied the response of the models for the same inputs and 1 found that the logarithm of the average membrane potential ufi = W Z well approxi-,Ll jEQP‘ mates the log-posterior estimated in Bayesian ICA , even in the absence of a stimulus (Fig 7E).
ICA is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Ethan S. Sokol, Sandhya Sanduja, Dexter X. Jin, Daniel H. Miller, Robert A. Mathis, Piyush B. Gupta
Results
A third algorithm, ICA , does not require that the constituent components be orthogonal to one another—and instead identifies components by maximizing their independence in a statistical sense.
Results
ICA has proven useful for deconstructing mixed signals (e.g., audio) into their constituent parts.
Results
Although our goal in developing PEACS was to apply it in settings where neither the state expression vectors nor cell-state proportions are known, to assess the effectiveness of the algorithms described above (SVD, NMF, ICA ) we needed an idealized context in which cell-state proportions could be experimentally defined.
Supporting Information
SVD, NMF, and ICA results.
Supporting Information
The results of first and second dimensions of (D) SVD, (E) NMF, and (F) ICA deconvolution were plotted 82 Fig.
ICA is mentioned in 10 sentences in this paper.
Topics mentioned in this paper:
Willemijn Groenendaal, Francis A. Ortega, Armen R. Kherlopian, Andrew C. Zygmunt, Trine Krogh-Madsen, David J. Christini
Extending the protocol: Adding multi-step voltage clamp data
Our 6s long voltage clamp protocol effectively isolates 1K1, ICaL , and 1KS as shown by the disproportionally large contributions of these currents in step -120 mV, +20 mV, and +40 and -30 mV, respectively (Fig 3 B—3F).
GA optimization using a single action potential
Nine model parameters, describing maximal conductances of ionic currents [the sodium current (INa), the L-type calcium current (ICaL), the T-type calcium current ( ICaT ), the inwardly rectifying potassium (1K1), the rapid and slow delayed rectifier potassium currents (1Kr and IKS), the plateau potassium current (IKp), and the sarcolemmal calcium pump current (IpCa)] and the maximal flux of the sarcoplasmic reticulum Ca2+-ATPase (ISERCA) were estimated using a GA technique.
Limitations and potential improvements
Our multi-step voltage-clamp protocol effectively isolates IKS, ICaL , and 1K1.
Limitations and potential improvements
As expected, local sensitivity analysis on simulated calcium traces demonstrates that they are most sensitive to changes in ICaL , IpCa, and ISERCA (SS Fig), which leads to the speculation that the estimation of these parameters could improve.
Parameter estimation shows changes compared to FR model and variability among individual cells
In contrast, for all four cells, maximal conductance of IKr and 1K1 are increased around 2-fold compared to the FR model, while ICaL is slightly increased.
Stochastic stimulation protocol improves model fit and predictability over single action potential protocol
Compared to the single action potential, the stochastic stimulation leads to a modest overall improvement of the parameter estimation, but it did notably better in determining the maximal conductances of IKr, ICaL , and 1K3 (Fig 2C).
Stochastic stimulation protocol improves model fit and predictability over single action potential protocol
However, as shown in Fig 2C, a few parameters remain incorrectly estimated (maximal conductances of ICaL and 1K5), and some are estimated with a large spread (maximal conductance values of IKP, IpCa, ICaT , and IKr).
Supporting Information
Blue symbols indicate parameters with a statistically significant effect on the model output While the red symbols indicate the parameters with a nonsignificant effect on the model output ( ICaT , 11(1), and IpCa).
The combined stochastic current and multi-step voltage clamp protocol improves parameter estimation
The estimation of the ICaL conductance is very close to 1, but is slightly overestimated in all runs.
The combined stochastic current and multi-step voltage clamp protocol improves parameter estimation
However, a few conductances were estimated poorly (in particular ISERCA and ICaT ) and models optimized based on voltage clamp data alone were, not surprisingly, inferior at predicting complex action potential dynamics during stochastic pacing (Fig 4C).
ICA is mentioned in 10 sentences in this paper.
Topics mentioned in this paper:
Jyotika Bahuguna, Ad Aertsen, Arvind Kumar
AMSN
At low cortical rates, | Dlefl| is larger than | DZeficl due to the scaled cortical excitation for D1 ( ICl > ICZ) and hence, the D1 type neurons dominate.
AMSN
In all subsequent analyses, we used the scenario II, keeping ICl > [CZ and maintaining identical input rate XCTX to both D1 and D2 MSNs.
Effect of Dopamine on the DTT
Therefore, in our model we simulated the effect of dopamine depletion by decreasing ICI and increasing ICZ, whereas higher than normal dopamine was simulated by increasing I01 and decreasing I02.
Effect of cortical spiking activity correlations on the DTT
For weak input correlations, as D1 MSNs form stronger synapses with cortical afferents, ( ICl > ICZ; scenario II), D1 MSNs spike at higher rate than D2 MSNs (Fig 5B).
ICA is mentioned in 4 sentences in this paper.
Topics mentioned in this paper: