A number of computational models of hippocampal place cells incorporate attractor neural network architecture to simulate key findings in the place cell literature, including the properties of pattern completion, firing in the absence of visual input, and nonlinear responses to environmental manipulations. To test for evidence of attractor dynamics, ensembles of place cells were recorded using multiple-tetrode techniques. After many days of experience in an environment with salient local surface cues on a circular track and salient distal landmarks on the wall, the local surface cues were rotated as a set in opposition to the distal landmarks. The amount of mismatch between the local and distal sets of cues varied from 45 to 180 degrees. If place cells were parts of strong attractors, then their place fields should follow either the local cues or the distal cues as an integrated ensemble. Instead, in single recording sessions, some place cells were controlled by the distal landmarks, other cells were controlled by the local cues, and other cells became silent or gained new fields. In some cases, individual place fields split in half, following both the local and distal cues. If place cells are indeed parts of attractor networks in the hippocampus, then the attractors may be weak relative to the inputs from external sources, such as representations of the sensory environment and representations of heading direction, in a familiar, well explored environment.