“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 14558 |
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One of the fundamental questions in neuroscience is the way information is processed through neurons in cortex. Neural correlation is one of the key features of neural activity that gives a clue to this question; because it reflects underlying network architecture and their computation. However the relation between correlation and information, that the neurons convey, has not been described with full mathematical clarity even for simple neuron models and network architectures. To tackle this problem, here we consider one-layer feedforward leaky integrate-and-fire (LIF) neurons where each receives noise and common inputs. For this network we are able to analytically study pairwise and higher-order correlations caused by the common inputs. Based on our previous study, we found analytically the exact spike-timing distribution of the LIF neurons when they receive signaling input on top of noisy balanced inputs. This spike-timing distribution is used not only to compute the neural correlation but also to analytically calculate Fisher information with respect to the common input amplitude. We elucidate how pairwise, higher-order correlation and Fisher information are changed as a function of the noisy input's diffusion coefficient and common input amplitude. Based on these calculations, we discuss how the Fisher information and correlation caused by common input's amplitude are related in the space of scaled diffusion coefficient and input amplitude's parameters. We observe an optimum value for the common input amplitude, in which, the Fisher information is maximized. Finally, we investigate if this optimum value coincides with the so called equilibrium value which is obtained, using spike timing dependent learning procedures.
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