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Parvalbumin-positive basket cells differentiate among hippocampal pyramidal cells.

Neuron | Jun 4, 2014

CA1 pyramidal cells (PCs) are not homogeneous but rather can be grouped by molecular, morphological, and functional properties. However, less is known about synaptic sources differentiating PCs. Using paired recordings in vitro, two-photon Ca(2+) imaging in vivo, and computational modeling, we found that parvalbumin-expressing basket cells (PVBCs) evoked greater inhibition in CA1 PCs located in the deep compared to superficial layer of stratum pyramidale. In turn, analysis of reciprocal connectivity revealed more frequent excitatory inputs to PVBCs by superficial PCs, demonstrating bias in target selection by both the excitatory and inhibitory local connections in CA1. Additionally, PVBCs further segregated among deep PCs, preferentially innervating the amygdala-projecting PCs but receiving preferential excitation from the prefrontal cortex-projecting PCs, thus revealing distinct perisomatic inhibitory interactions between separate output channels. These results demonstrate the presence of heterogeneous PVBC-PC microcircuits, potentially contributing to the sparse and distributed structure of hippocampal network activity.

Pubmed ID: 24836505 RIS Download

Mesh terms: Amygdala | Animals | CA1 Region, Hippocampal | Calcium | Entorhinal Cortex | Female | Male | Mice | Mice, Inbred C57BL | Nerve Net | Parvalbumins | Physical Conditioning, Animal | Prefrontal Cortex | Pyramidal Cells

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Associated grants

  • Agency: NINDS NIH HHS, Id: R01 NS074432
  • Agency: NINDS NIH HHS, Id: NS74432

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neuroConstruct

Software for simulating complex networks of biologically realistic neurons, i.e. models incorporating dendritic morphologies and realistic cell membrane conductance, implemented in Java and generates script files for the NEURON and GENESIS simulators, with support for other simulation platforms (including PSICS and PyNN) in development. neuroConstruct is being developed in the Silver Lab in the Department of Neuroscience, Physiology and Pharmacology at UCL and uses the latest NeuroML specifications, including MorphML, ChannelML and NetworkML. Some of the key features of neuroConstruct are: Creation of networks of biologically realistic neurons, positioned in 3D space. Complex connectivity patterns between cell groups can be specified for the networks. Can import morphology files in GENESIS, NEURON, Neurolucida, SWC and MorphML format for inclusion in network models. Simulations can be run on the NEURON or GENESIS platforms. Cellular processes (synapses/channel mechanisms) can be imported from native script files or created in ChannelML. Recording of simulation data generated by the simulation and visualization/analysis of data. Stored simulation runs can be viewed and managed through the Simulation Browser interface.

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