Neural Comput - Quantifying neurotransmission reliability through metrics-based information analysis.

Tópicos

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Resumo

We set forth an information-theoretical measure to quantify neurotransmission reliability while taking into full account the metrical properties of the spike train space. This parametric information analysis relies on similarity measures induced by the metrical relations between neural responses as spikes flow in. Thus, in order to assess the entropy, the conditional entropy, and the overall information transfer, this method does not require any a priori decoding algorithm to partition the space into equivalence classes. It therefore allows the optimal parameters of a class of distances to be determined with respect to information transmission. To validate the proposed information-theoretical approach, we study precise temporal decoding of human somatosensory signals recorded using microneurography experiments. For this analysis, we employ a similarity measure based on the Victor-Purpura spike train metrics. We show that with appropriate parameters of this distance, the relative spike times of the mechanoreceptors' responses convey enough information to perform optimal discrimination--defined as maximum metrical information and zero conditional entropy--of 81 distinct stimuli within 40 ms of the first afferent spike. The proposed information-theoretical measure proves to be a suitable generalization of Shannon mutual information in order to consider the metrics of temporal codes explicitly. It allows neurotransmission reliability to be assessed in the presence of large spike train spaces (e.g., neural population codes) with high temporal precision.

Resumo Limpo

set forth informationtheoret measur quantifi neurotransmiss reliabl take full account metric properti spike train space parametr inform analysi reli similar measur induc metric relat neural respons spike flow thus order assess entropi condit entropi overal inform transfer method requir priori decod algorithm partit space equival class therefor allow optim paramet class distanc determin respect inform transmiss valid propos informationtheoret approach studi precis tempor decod human somatosensori signal record use microneurographi experi analysi employ similar measur base victorpurpura spike train metric show appropri paramet distanc relat spike time mechanoreceptor respons convey enough inform perform optim discriminationdefin maximum metric inform zero condit entropyof distinct stimuli within ms first affer spike propos informationtheoret measur prove suitabl general shannon mutual inform order consid metric tempor code explicit allow neurotransmiss reliabl assess presenc larg spike train space eg neural popul code high tempor precis

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