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Morris Water Maze Analyzer

Upload XY tracking data or per-trial summaries. Get escape latency curves, quadrant analysis, probe trial heatmaps, swim path visualization, and search strategy classification.

Swim Path AnalysisProbe TrialCSV Export

Try it out

Load example MWM data to see the full workflow

Maze Configuration

Upload XY Tracking CSV

Columns: x, y, time, animal_id (+ optional: group, day, trial, probe). Auto-detects EthoVision, ANY-maze, and generic formats.

  • Analyze MWM training trials: escape latency curves, path length, swim speed across days and groups
  • Probe trial analysis: quadrant dwell time, platform crossings, Gallagher proximity measure
  • Generate heatmaps from XY tracking data showing probe trial search distribution
  • Classify swim path search strategies (direct, focal, scanning, random) from XY coordinates
  • Compare spatial learning across treatment groups with learning curves (mean +/- SEM)
  • Export individual and cohort summary data to CSV for downstream statistics

Don't use for

  • Visible platform (cued) trials where the platform is above water — this tool assumes hidden platform protocol
  • Barnes maze, radial arm maze, or T-maze — different paradigms with different metrics
  • Real-time video tracking — use ConductVision or dedicated tracking software (EthoVision, ANY-maze) for that

Resources

  • Water temperature at 22±1°C
  • Platform submerged 1-2cm below surface
  • Extra-maze cues visible and consistent
  • Water opacified (non-toxic paint or milk)
  • Camera positioned directly overhead
  • Recording software armed and tested
  • Release positions randomized for today's sessions
  • Towels and warming cage ready

What Is the Morris Water Maze?

The Morris Water Maze was introduced by Richard G.M. Morris in 1981 as a test of spatial learning and memory. The apparatus consists of a large circular pool (typically 120-180 cm diameter) filled with opaque water (made milky with non-toxic paint or powdered milk) maintained at 22-26 degrees C. A hidden escape platform is submerged 1-2 cm below the water surface in one quadrant of the pool. The animal is placed in the water at varying start locations around the perimeter and must use distal visual cues (posters, shapes, or landmarks on the walls of the testing room) to navigate to the hidden platform. Over multiple training trials across several days, healthy rodents learn the spatial location of the platform, showing progressively shorter escape latencies and more direct swim paths. The MWM has become the gold standard for assessing hippocampal function, spatial reference memory, and the effects of pharmacological, genetic, and lesion manipulations on spatial cognition. Its power lies in the fact that it requires allocentric spatial navigation — the animal must build a cognitive map from distal cues rather than relying on proximal or intramaze cues.

Spatial Learning Metrics in the MWM

Multiple complementary metrics capture different aspects of MWM performance. Escape latency (seconds to reach platform) is the most common but conflates spatial knowledge with swim speed — a sedated animal may know where the platform is but swim slowly. Path length (total distance swum) partially controls for speed differences. Swim speed (path length / latency) detects motor impairments or motivational differences. Quadrant dwell time (% time in each quadrant) measures spatial bias during probe trials, with chance at 25%. Platform crossings count how many times the animal passes over the former platform location. The Gallagher proximity measure (mean distance to platform across all time samples) is the most sensitive metric for detecting subtle impairments, as shown in age-related cognitive decline studies. For training data, learning curves (escape latency or path length plotted across days or sessions, mean +/- SEM per group) visualize acquisition rate. For probe trials, quadrant bar charts and heatmaps provide complementary views of search distribution.

Search Strategy Classification

Categorizing swim paths into search strategies provides qualitative insight into how animals navigate. Naive or impaired animals typically show random strategies: thigmotaxis (persistent wall-hugging), random swimming across all quadrants, or circling. As learning progresses, strategies shift to scanning (searching broadly in the correct region), then focal search (concentrated swimming near the platform), and finally direct swims (efficient, nearly straight trajectories from the start position to the platform). Classification algorithms typically use heading error (deviation from the optimal direction to the platform), dwell time in specific zones, and path efficiency. In this tool, a direct swim is classified when the heading error is below 15 degrees for more than 60% of the path with short latency; focal search when the animal spends more than 80% of time within twice the platform radius in the target quadrant; scanning when more than 60% of time is in the target half of the pool; and random for all remaining paths. Tracking the proportion of each strategy across training days reveals the learning progression more richly than latency alone.

Frequently Asked Questions

Next Steps

Morris Water Maze

Circular pool with automated platform for spatial learning and memory assessment

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Track with ConductVision

Real-time swim path tracking from video — set up zones with the ROI Editor, no manual XY extraction

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Automate with ConductMaze

Atlantis platform control, start position randomization, and TTL sync for electrophysiology

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