Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields

Citation: Ujfalussy B, Kiss T, Erdi P ( Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields Bala zs Ujfalussy 0 Tama s Kiss 0 Pe ter E rdi 0 Boris S. Gutkin, CNRS, France 0 1 Department of Biophysics, KFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of Sciences , Budapest , Hungary , 2 Center for Complex Systems Studies, Kalamazoo College , Kalamazoo, Michigan , United States of America A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron. Using a simple cascade model, we demonstrate that triggering somatic firing by a relatively small dendritic branch requires the amplification of local events by dendritic spiking and synaptic plasticity. The moderately branching dendritic tree of granule cells seems optimal for this computation since larger dendritic trees favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the soma requires a low branch number. Finally, we demonstrate that these parallel dendritic computations could contribute to the generation of multiple independent place fields of hippocampal granule cells. - Funding: This research was supported by the EU Framework 6 ICEA project (IST 027819). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Neurons possess highly branched, complex dendritic trees, but the relationship between the structure of the dendritic arbor and underlying neural function is poorly understood [1]. Recent studies suggest that dendritic branches form independent computational subunits: Individual branches function as single integrative compartments [2,3], generate isolated dendritic spikes [4,5] linking together neighbouring groups of synapses by local plasticity rules [68]. Coupling between dendritic branches and the soma is regulated in a branch-specific manner through local mechanisms [9], and the homeostatic scaling of the neurotransmitter release probability is also regulated by the local dendritic activation [10]. The computational power of active dendrites had already been demonstrated by several computational studies [1116], but how local events influence the output of the neuron remained an open question. Using the cable equation [17] or compartmental modelling tools one can calculate the current or voltage attenuation between arbitrary points in a dendritic tree [14], which is in good agreement with in vitro recordings. However, cortical networks in vivo are believed to operate in a balanced state [18,19], where the inhibitory drive is continuously adjusted such that the mean activity of the population is nearly constant [20,21]. In this case, the firing of an individual neuron is determined, beyond its own input, by the activity distribution of the population. A simple cascade model [22] incorporating numerous dendritic compartments allowed us the statistical estimation of the activity distribution of neurons within the population. We used this model to study how localized dendritic computations influence the output of the neuron. The present study focuses on hippocampal granule cells. Compared to pyramidal neurons granule cells have relatively simpler dendritic arborization: They lack the apical trunk and the basal dendrites, but are characterized by several, equivalent dendritic branches, extended into the molecular layer [23] (Figure 1A). Recordings from freely moving rats revealed that like pyramidal neurons, granule cells exhibit clear spatially selective discharge [24,25]. However, granule cells had smaller place fields than pyramidal cells, and had multiple distinct subfields [24,26]. It has also been recently shown that these subfields are independent, i.e., their distribution was irregular and the transformation of the environment resulted in incoherent rate change in the subfields [26]. The dendritic morphology of granule cells suggest that parallel dendritic computations could contribute to the generation of multiple, distinct subfields of these neurons. In the present study we analyzed how synaptic input arriving to dendritic subunits influence the neuronal output. First, we introduce the model used in this study and we define statistical criteria to measure if a dendritic branch alone is able to trigger somatic spiking. We show, that generally neurons perform input strength encoding i.e., input to the whole dendritic tree but not activation of a single branch is encoded in the somatic firing. Next we demonstrate that if the local response is enhanced by active mechanisms (dendritic spiking and synaptic plasticity) then neurons switch to feature detection mode during which the firing of the neuron is usually triggered by the activation of a single dendritic branch. Furthermore we show that moderately branched dendritic tree of granule cells is optimal for this computation as large number of branches favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the Neurons were originally divided into three morphologically distinct compartments: the dendrites receive the synaptic input, the soma integrates it and communicates the output of the cell to other neurons via the axon. Although several lines of evidence challenged this oversimplified view, neurons are still considered to be the basic information processing units of the nervous system as their output reflects the computations performed by the entire dendritic tree. In the present study, the authors build a simplified computational model and calculate that, in certain neurons, relatively small dendritic branches are able to independently trigger somatic firing. Therefore, in these cells, an action potential mirrors the activity of a small dendritic subunit rather than the input arriving to the whole dendritic tree. These neurons can be regarded as a network of a few independent integrator units connected to a common output unit. The authors demonstrate that a moderately branched dendritic tree of hippocampal granule cells may be optimized for these parallel computations. Finally the authors show that these parallel dendritic computations could explain some aspects of the location dependent activity of hippocampal granule cells. soma requires low branch number. Dendritic branches of dentate granule cells could therefore learn different inputs; and the cell, activated through different dendritic branches, cou (...truncated)