Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization

Pitti, Alexandre; Quoy, Mathias; Boucenna, Sofiane; Lavandier, Catherine

doi:10.1371/journal.pcbi.1008566

Cited by 3 publications

(4 citation statements)

References 78 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar with what we showed in (36,44) this variational process is an online stochastic hill-climbing algorithm performed iteratively; a pseudocode is provided thereinafter. We added in (47) a more sophisticated hill-climbing algorithm corresponding to simulated annealing to efficiently drive the exploration process.…”

Section: D)mentioning

confidence: 93%

“…Hence, this type of coding differs from the temporal coding of the bioinspired spike timing-dependent plasticity reinforcement rule ( 40 , 41 ). Relative ordinal codes also depart from the rank-order coding (ROC) algorithm of Thorpe and colleagues ( 42 , 43 ), although this work and previous works are inspired by it ( 36 , 44 ). The ROC algorithm has been proposed as a model to explain the rapid processing performed by the visual system in a few cortical layers.…”

Section: Modelmentioning

confidence: 99%

“…To reconstruct each sequence learned by each neuron and to have an estimate of the items in each alphabet, we can use any metaheuristic methods, such as the hill-climbing algorithm, simulated annealing, or genetic algorithms, to evaluate it by trial and error. Here, we implement the simulated annealing algorithm solutions through an iterative stochastic optimization as the one used in related works on predictive coding ( 36 , 44 ) ( Fig. 1 ).…”

Section: Modelmentioning

confidence: 99%

“…Similar with what we showed in refs. 36 and 44 , this variational process is an online stochastic hill-climbing algorithm performed iteratively; a pseudocode is provided thereinafter. We added in ref.…”

Section: Modelmentioning

confidence: 99%

See 3 more Smart Citations

Digital computing through randomness and order in neural networks

Pitti

Weidmann

Quoy

2022

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

We propose that coding and decoding in the brain are achieved through digital computation using three principles: relative ordinal coding of inputs, random connections between neurons, and belief voting. Due to randomization and despite the coarseness of the relative codes, we show that these principles are sufficient for coding and decoding sequences with error-free reconstruction. In particular, the number of neurons needed grows linearly with the size of the input repertoire growing exponentially. We illustrate our model by reconstructing sequences with repertoires on the order of a billion items. From this, we derive the Shannon equations for the capacity limit to learn and transfer information in the neural population, which is then generalized to any type of neural network. Following the maximum entropy principle of efficient coding, we show that random connections serve to decorrelate redundant information in incoming signals, creating more compact codes for neurons and therefore, conveying a larger amount of information. Henceforth, despite the unreliability of the relative codes, few neurons become necessary to discriminate the original signal without error. Finally, we discuss the significance of this digital computation model regarding neurobiological findings in the brain and more generally with artificial intelligence algorithms, with a view toward a neural information theory and the design of digital neural networks.

show abstract

Section: D)mentioning

confidence: 93%