Ca(2+) currents resistant to organic Ca(2+) channel antagonists are present in different types of central neurons. Here, we describe the properties of such currents in CA1 neurons acutely dissociated from rat hippocampus. Blocker-resistant Ca(2+) currents were isolated by combined application of N-, P/Q- and L-type Ca(2+) current antagonists (omega-conotoxin GVIA 2 microM; omega-conotoxin MVIIC 3 microM; omega-agatoxin IVA 200 nM; nifedipine 10 microM) and constituted approximately 21% of the total Ba(2+) current. The blocker-resistant current showed properties similar to R-type currents in other cell types, i.e. voltages of half-maximal inactivation and activation of -76 and -17 mV, respectively, and strong inactivation during the test pulse. In addition, blocker-resistant Ca(2+) currents in CA1 neurons displayed a characteristically rapid deactivation. Application of mock action potentials revealed that charge transfer through blocker-resistant Ca(2+) channels is highly sensitive to action potential shape and changes in resting membrane voltage. Pharmacological experiments showed that these currents were highly sensitive to the divalent cation Ni(2+) (half-maximal block at 28 microM), but were relatively resistant to the spider toxin SNX-482 (8% and 52% block at 0.1 and 1 microM, respectively). In addition to the functional analysis, we examined the expression of pore-forming and accessory Ca(2+) channel subunits on the messenger RNA level in isolated CA1 neurons using quantitative real-time polymerase chain reaction. Of the pore-forming alpha subunits encoding high-threshold Ca(2+) channels, Ca(v)2.1, Ca(v)2.2 and Ca(v)2.3 messenger RNA levels were most prominent, corresponding to the high proportion of N-, P/Q- and R-type currents in these neurons. In summary, CA1 neurons display blocker-resistant Ca(2+) currents with distinctive biophysical and pharmacological properties similar to R-type currents in other neuron types, and express Ca(2+) channel messenger RNAs that give rise to R-type Ca(2+) currents in expression systems.