We demonstrate a spiking neural circuit with timing-dependent adaptive synapses to track contours in a two-dimensional plane. Our model is inspired by the architecture of the 7-neuron network believed to control the thermotaxis behavior in the nematode Caenorhabditis Elegans. However, unlike the C. Elegans network, our sensory neuron only uses the local variable (and not its derivative) to implement contour tracking, thereby minimizing the complexity of implementation. We employ spike timing based adaptation and plasticity rules to design micro-circuits for gradient detection and tracking. Simulations show that our bio-mimetic neural circuit can identify isotherms with a 60% higher probability than the theoretically optimal memoryless Levy foraging model. Further, once the set-point is identified, our model's tracking accuracy is in the range of ±0.05 °C, similar to that observed in nature. The neurons in our circuit spike at sparse biological rates ( 100 Hz), enabling energy-efficient implementations.