A network of tufted layer 5 pyramidal neurons
Tufted layer 5 (TL5) pyramidal neurons are important projection neurons from the cerebral cortex to subcortical areas. Recent and ongoing experiments aimed at understanding the computational analysis performed by a network of synaptically connected TL5 neurons are reviewed here. The experiments employed dual and triple whole-cell patch clamp recordings from visually identified and preselected neurons in brain slices of somatosensory cortex of young (14- to 16-day-old) rats. These studies suggest that a local network of TL5 neurons within a cortical module of diameter 300 microns consists of a few hundred neurons that are extensively inter-connected with reciprocal feedback from at least first-, second- and third-order target neurons. A statistical analysis of synaptic innervation suggests that this recurrent network is not randomly arranged and hence each neuron could be functionally unique. Synaptic transmission between these neurons is characterized by use-dependent synaptic depression which confers novel properties to this recurrent network of neurons. First, a range of rates of depression for different synaptic connections enable each TL5 neuron to receive a unique mixture of information about the average firing rates and the temporally correlated action potential (AP) activity in the population of presynaptic TL5 neurons. Second, each AP generated by any neuron in the network induces a change (defined as an iteration step) in the functional coupling of the neurons in the network (defined as network configuration). It is proposed that the network configuration is iterated during a stimulus to achieve an optimally orchestrated network response. Hebbian, anti-Hebbian and neuromodulatory-induced modifications of neurotransmitter release probability change the rates of synaptic depression and thereby alter the iteration step size. These data may be important to understand the dynamics of electrical activity within the network.
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Record created on 2013-01-28, modified on 2016-10-06