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Volterra dendritic stimulus processors and biophysical spike generators with intrinsic noise sources.

Lazar AA, Zhou Y - Front Comput Neurosci (2014)

Bottom Line: For single-input multi-output neural circuit models with feedforward, feedback and cross-feedback DSPs cascaded with BSGs we theoretically analyze the effect of noise sources on stimulus decoding.Building on a key duality property, the effect of noise parameters on the precision of the functional identification of the complete neural circuit with DSP/BSG neuron models is given.We demonstrate through extensive simulations the effects of noise on encoding stimuli with circuits that include neuron models that are akin to those commonly seen in sensory systems, e.g., complex cells in V1.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Columbia University New York, NY, USA.

ABSTRACT
We consider a class of neural circuit models with internal noise sources arising in sensory systems. The basic neuron model in these circuits consists of a dendritic stimulus processor (DSP) cascaded with a biophysical spike generator (BSG). The dendritic stimulus processor is modeled as a set of nonlinear operators that are assumed to have a Volterra series representation. Biophysical point neuron models, such as the Hodgkin-Huxley neuron, are used to model the spike generator. We address the question of how intrinsic noise sources affect the precision in encoding and decoding of sensory stimuli and the functional identification of its sensory circuits. We investigate two intrinsic noise sources arising (i) in the active dendritic trees underlying the DSPs, and (ii) in the ion channels of the BSGs. Noise in dendritic stimulus processing arises from a combined effect of variability in synaptic transmission and dendritic interactions. Channel noise arises in the BSGs due to the fluctuation of the number of the active ion channels. Using a stochastic differential equations formalism we show that encoding with a neuron model consisting of a nonlinear DSP cascaded with a BSG with intrinsic noise sources can be treated as generalized sampling with noisy measurements. For single-input multi-output neural circuit models with feedforward, feedback and cross-feedback DSPs cascaded with BSGs we theoretically analyze the effect of noise sources on stimulus decoding. Building on a key duality property, the effect of noise parameters on the precision of the functional identification of the complete neural circuit with DSP/BSG neuron models is given. We demonstrate through extensive simulations the effects of noise on encoding stimuli with circuits that include neuron models that are akin to those commonly seen in sensory systems, e.g., complex cells in V1.

No MeSH data available.


Related in: MedlinePlus

Diagram of the functional identification with multiple trials. The neural circuit is presented a different stimulus um1(t) for each trial m. See also Figure 7 for details of a single trial.
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Figure 8: Diagram of the functional identification with multiple trials. The neural circuit is presented a different stimulus um1(t) for each trial m. See also Figure 7 for details of a single trial.

Mentions: The projections of the Volterra DSP kernels of Figure 7 are interpreted as inputs, while the input stimuli and the train of RKs at spike times replace the impulse response of the corresponding filters. Therefore, the functional identification problem has been transformed into a dual decoding problem, where the objects to decode are the set of projections of Volterra DSP kernels and the neural circuit is comprised of “stimulus DSP kernels” and “spike DSP kernels” with the same BSGs and noise sources. The only difference is that, instead of a Single-Input Multi-Output decoding problem, the identification was transformed into a Multi-Input Multi-Output decoding problem. In addition, multiple trials using different stimuli are needed; this procedure is illustrated in block diagram form in Figure 8. By stimulating the neural circuit with multiple stimuli in the functional identification setting, multiple neural circuits effectively encode the projections of the DSP kernels.


Volterra dendritic stimulus processors and biophysical spike generators with intrinsic noise sources.

Lazar AA, Zhou Y - Front Comput Neurosci (2014)

Diagram of the functional identification with multiple trials. The neural circuit is presented a different stimulus um1(t) for each trial m. See also Figure 7 for details of a single trial.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150400&req=5

Figure 8: Diagram of the functional identification with multiple trials. The neural circuit is presented a different stimulus um1(t) for each trial m. See also Figure 7 for details of a single trial.
Mentions: The projections of the Volterra DSP kernels of Figure 7 are interpreted as inputs, while the input stimuli and the train of RKs at spike times replace the impulse response of the corresponding filters. Therefore, the functional identification problem has been transformed into a dual decoding problem, where the objects to decode are the set of projections of Volterra DSP kernels and the neural circuit is comprised of “stimulus DSP kernels” and “spike DSP kernels” with the same BSGs and noise sources. The only difference is that, instead of a Single-Input Multi-Output decoding problem, the identification was transformed into a Multi-Input Multi-Output decoding problem. In addition, multiple trials using different stimuli are needed; this procedure is illustrated in block diagram form in Figure 8. By stimulating the neural circuit with multiple stimuli in the functional identification setting, multiple neural circuits effectively encode the projections of the DSP kernels.

Bottom Line: For single-input multi-output neural circuit models with feedforward, feedback and cross-feedback DSPs cascaded with BSGs we theoretically analyze the effect of noise sources on stimulus decoding.Building on a key duality property, the effect of noise parameters on the precision of the functional identification of the complete neural circuit with DSP/BSG neuron models is given.We demonstrate through extensive simulations the effects of noise on encoding stimuli with circuits that include neuron models that are akin to those commonly seen in sensory systems, e.g., complex cells in V1.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Columbia University New York, NY, USA.

ABSTRACT
We consider a class of neural circuit models with internal noise sources arising in sensory systems. The basic neuron model in these circuits consists of a dendritic stimulus processor (DSP) cascaded with a biophysical spike generator (BSG). The dendritic stimulus processor is modeled as a set of nonlinear operators that are assumed to have a Volterra series representation. Biophysical point neuron models, such as the Hodgkin-Huxley neuron, are used to model the spike generator. We address the question of how intrinsic noise sources affect the precision in encoding and decoding of sensory stimuli and the functional identification of its sensory circuits. We investigate two intrinsic noise sources arising (i) in the active dendritic trees underlying the DSPs, and (ii) in the ion channels of the BSGs. Noise in dendritic stimulus processing arises from a combined effect of variability in synaptic transmission and dendritic interactions. Channel noise arises in the BSGs due to the fluctuation of the number of the active ion channels. Using a stochastic differential equations formalism we show that encoding with a neuron model consisting of a nonlinear DSP cascaded with a BSG with intrinsic noise sources can be treated as generalized sampling with noisy measurements. For single-input multi-output neural circuit models with feedforward, feedback and cross-feedback DSPs cascaded with BSGs we theoretically analyze the effect of noise sources on stimulus decoding. Building on a key duality property, the effect of noise parameters on the precision of the functional identification of the complete neural circuit with DSP/BSG neuron models is given. We demonstrate through extensive simulations the effects of noise on encoding stimuli with circuits that include neuron models that are akin to those commonly seen in sensory systems, e.g., complex cells in V1.

No MeSH data available.


Related in: MedlinePlus