Neural Comput - Influence of external inputs and asymmetry of connections on information-geometric measures involving up to ten neuronal interactions.

Tópicos

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Resumo

The investigation of neural interactions is crucial for understanding information processing in the brain. Recently an analysis method based on information geometry (IG) has gained increased attention, and the property of the pairwise IG measure has been studied extensively in relation to the two-neuron interaction. However, little is known about the property of IG measures involving more neuronal interactions. In this study, we systematically investigated the influence of external inputs and the asymmetry of connections on the IG measures in cases ranging from 1-neuron to 10-neuron interactions. First, the analytical relationship between the IG measures and external inputs was derived for a network of 10 neurons with uniform connections. Our results confirmed that the single and pairwise IG measures were good estimators of the mean background input and of the sum of the connection weights, respectively. For the IG measures involving 3 to 10 neuronal interactions, we found that the influence of external inputs was highly nonlinear. Second, by computer simulation, we extended our analytical results to asymmetric connections. For a network of 10 neurons, the simulation showed that the behavior of the IG measures in relation to external inputs was similar to the analytical solution obtained for a uniformly connected network. When the network size was increased to 1000 neurons, the influence of external inputs almost disappeared. This result suggests that all IG measures from 1-neuron to 10-neuron interactions are robust against the influence of external inputs. In addition, we investigated how the strength of asymmetry influenced the IG measures. Computer simulation of a 1000-neuron network showed that all the IG measures were robust against the modulation of the asymmetry of connections. Our results provide further support for an information-geometric approach and will provide useful insights when these IG measures are applied to real experimental spike data.

Resumo Limpo

investig neural interact crucial understand inform process brain recent analysi method base inform geometri ig gain increas attent properti pairwis ig measur studi extens relat twoneuron interact howev littl known properti ig measur involv neuron interact studi systemat investig influenc extern input asymmetri connect ig measur case rang neuron neuron interact first analyt relationship ig measur extern input deriv network neuron uniform connect result confirm singl pairwis ig measur good estim mean background input sum connect weight respect ig measur involv neuron interact found influenc extern input high nonlinear second comput simul extend analyt result asymmetr connect network neuron simul show behavior ig measur relat extern input similar analyt solut obtain uniform connect network network size increas neuron influenc extern input almost disappear result suggest ig measur neuron neuron interact robust influenc extern input addit investig strength asymmetri influenc ig measur comput simul neuron network show ig measur robust modul asymmetri connect result provid support informationgeometr approach will provid use insight ig measur appli real experiment spike data

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