Decorrelation of low-frequency neural activity by inhibitory feedback

Frequency separation by an excitatory-inhibitory network

We consider a situation in which individual features of the input are represented in the neural system by different frequencies of periodic firings. Thus, if two of the features are presented concurrently, the input to the system will consist of a superposition of two periodic trains. In this paper we present an algorithm that is capable of extracting the individual features from the composite signal by separating the signal into periodic spike trains with different frequencies. We show that the algorithm can be implemented in a biophysically based excitatory-inhibitory network model. The frequency separation process works over a range of frequencies determined by time constants of the model's intrinsic variables. It does not rely on a "resonance" phenomenon and is not tuned to a discrete set of frequencies. The frequency separation is still reliable when the timing of incoming spikes is noisy.

Decorrelation of cortical inputs and motoneuron output

Oscillations in the primary motor cortex are transmitted through the corticospinal tract to the motoneuron pool. This pathway is believed to produce an effective and direct command from the motor cortex to the spinal motoneurons for the modulation of the force output. In this study, we used a computational model of a population of motoneurons to investigate the factors that can influence the transmission of the cortical input to the output of motoneurons, since it can be quantified by coherence analysis. The simulations demonstrated that, despite the nonlinearity of the motoneurons, oscillations present in the cortical input are transmitted to the output of the motoneuron pool at the same frequency. However, the interference introduced by the nonlinearity of the system increases the variability of the oscillations in output, introducing spectral lines whose frequency depends on the input frequencies and the motoneuron discharge rates. Moreover, an additional source of synaptic input common to all motoneurons but independent from the corticospinal component decorrelates the cortical input and motoneuron output and, thus, decreases the magnitude of the estimated coherence, even if the effective cortical drive does not change. These results indicate that the corticospinal input can effectively be sampled by a small population of motoneurons. However, the transmission of a corticospinal drive to the motoneuron pool is influenced by the nonlinearity of the spiking processes of the active motoneurons and by synaptic inputs common to the motoneuron population but independent from the cortical input.

How structure determines correlations in neuronal networks

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.