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Causal Inference and Explaining Away in a Spiking Network.

Moreno-Bote R, Drugowitsch J - Sci Rep (2015)

Bottom Line: Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons.The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities.This type of network might underlie tasks such as odor identification and classification.

View Article: PubMed Central - PubMed

Affiliation: Department of Technologies of Information and Communication, University Pompeu Fabra, 08018 Barcelona, Spain.

ABSTRACT
While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.

No MeSH data available.


Related in: MedlinePlus

Slow firing rate covariations underlie reliable encoding.(a,b) Population activity patterns over time for a noiseless (black dots), weak-noise (red) and strong-noise (green) network. The noiseless network is identical in the two panels but represented at two different time resolutions. Networks only differ in the injected noise variance, while other parameters including initial conditions are identical (Methods). (c) Angular error as a function of time for the three networks (100 ms time window). (d) Angular error as a function of time for the noiseless network (black line) and for trial- (light blue) and bin- (dark blue) shuffled networks. When the slow covariations of firing rate are destroyed by the shuffling, performance largely deteriorates compared to the one of the noiseless network.
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f5: Slow firing rate covariations underlie reliable encoding.(a,b) Population activity patterns over time for a noiseless (black dots), weak-noise (red) and strong-noise (green) network. The noiseless network is identical in the two panels but represented at two different time resolutions. Networks only differ in the injected noise variance, while other parameters including initial conditions are identical (Methods). (c) Angular error as a function of time for the three networks (100 ms time window). (d) Angular error as a function of time for the noiseless network (black line) and for trial- (light blue) and bin- (dark blue) shuffled networks. When the slow covariations of firing rate are destroyed by the shuffling, performance largely deteriorates compared to the one of the noiseless network.

Mentions: So far we have confirmed that our spiking network performs causal inference with high accuracy even when there are internally generated sources of variability. Now we turn to the second question: Is our spiking algorithm robust against external sources of noise? To answer this question, we compared a network with no input noise (reproduced in Fig. 5a,b, at two different time resolutions; black dots) to networks in which weak (red) or strong (green) noise was injected.


Causal Inference and Explaining Away in a Spiking Network.

Moreno-Bote R, Drugowitsch J - Sci Rep (2015)

Slow firing rate covariations underlie reliable encoding.(a,b) Population activity patterns over time for a noiseless (black dots), weak-noise (red) and strong-noise (green) network. The noiseless network is identical in the two panels but represented at two different time resolutions. Networks only differ in the injected noise variance, while other parameters including initial conditions are identical (Methods). (c) Angular error as a function of time for the three networks (100 ms time window). (d) Angular error as a function of time for the noiseless network (black line) and for trial- (light blue) and bin- (dark blue) shuffled networks. When the slow covariations of firing rate are destroyed by the shuffling, performance largely deteriorates compared to the one of the noiseless network.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664919&req=5

f5: Slow firing rate covariations underlie reliable encoding.(a,b) Population activity patterns over time for a noiseless (black dots), weak-noise (red) and strong-noise (green) network. The noiseless network is identical in the two panels but represented at two different time resolutions. Networks only differ in the injected noise variance, while other parameters including initial conditions are identical (Methods). (c) Angular error as a function of time for the three networks (100 ms time window). (d) Angular error as a function of time for the noiseless network (black line) and for trial- (light blue) and bin- (dark blue) shuffled networks. When the slow covariations of firing rate are destroyed by the shuffling, performance largely deteriorates compared to the one of the noiseless network.
Mentions: So far we have confirmed that our spiking network performs causal inference with high accuracy even when there are internally generated sources of variability. Now we turn to the second question: Is our spiking algorithm robust against external sources of noise? To answer this question, we compared a network with no input noise (reproduced in Fig. 5a,b, at two different time resolutions; black dots) to networks in which weak (red) or strong (green) noise was injected.

Bottom Line: Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons.The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities.This type of network might underlie tasks such as odor identification and classification.

View Article: PubMed Central - PubMed

Affiliation: Department of Technologies of Information and Communication, University Pompeu Fabra, 08018 Barcelona, Spain.

ABSTRACT
While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.

No MeSH data available.


Related in: MedlinePlus