Abstract
Neurons communicate and transmit information predominantly through spikes. Given that experimentally observed neural spike trains in a variety of brain areas can be highly correlated, it is important to investigate how neurons process correlated inputs. Most previous work in this area studied the problem of correlation transfer analytically by making significant simplifications on neural dynamics. Temporal correlation between inputs that arises from synaptic filtering, for instance, is often ignored when assuming that an input spike can at most generate one output spike. Through numerical simulations of a pair of leaky integrate-and-fire (LIF) neurons receiving correlated inputs, we demonstrate that neurons in the presence of synaptic filtering by slow synapses exhibit strong output correlations. We then show that burst firing plays a central role in enhancing output correlations, which can explain the above-mentioned observation because synaptic filtering induces bursting. The observed changes of correlations are mostly on a long time scale. Our results suggest that other features affecting the prevalence of neural burst firing in biological neurons, e.g., adaptive spiking mechanisms, may play an important role in modulating the overall level of correlations in neural networks.
Highlights
Many in vivo studies have revealed that neurons in a variety of brain areas frequently exhibit correlated activity (Zohary et al, 1994; König and Engel, 1995; Bair et al, 2001; Kohn and Smith, 2005; Okun and Lampl, 2008; Gerkin et al, 2013)
We aimed to study the role of synaptic filtering in correlation transfer by numerical simulations of a pair of leaky integrate-and-fire (LIF) neurons receiving partially overlapping inputs
If this intuition is sufficient to account for the process of correlation transfer in our model, the output correlations should be similar in these two scenarios
Summary
Many in vivo studies have revealed that neurons in a variety of brain areas frequently exhibit correlated activity (Zohary et al, 1994; König and Engel, 1995; Bair et al, 2001; Kohn and Smith, 2005; Okun and Lampl, 2008; Gerkin et al, 2013). One of the key questions is how input correlations are processed and transmitted from a layer of neurons to the (Shadlen and Newsome, 1998; Diesmann et al, 1999; Salinas and Sejnowski, 2000; Reyes, 2003; de la Rocha et al, 2007; Ostojic et al, 2009; Litwin-Kumar et al, 2011; Hong et al, 2012; Schultze-Kraft et al, 2013). The conductance based LIF model (Stein, 1967) is often used in numerical and analytical studies of neural dynamics. Most previous work has studied the problem of correlation transfer by resorting to further approximations of single neuron dynamics and considering the pairwise correlation between two neurons receiving correlated inputs. A typical strategy is to use the diffusion approximation, mimicking the synaptic inputs by currents with Gaussian white noise
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