How a stimulus, such as an odour, is represented in the brain is one of the main questions in neuroscience. It is becoming clearer that information is encoded by a population of neurons, but, how the spiking activity of a population of neurons conveys this information is unknown. Several population coding hypotheses have formulated over the years, and therefore, to obtain a more definitive answer as to how a population of neurons represents stimulus information we need to test, i.e. support or falsify, each of the hypotheses. One way of addressing these hypotheses is to record and analyse the activity of multiple individual neurons from the brain of a test subject when a stimulus is, and is not, presented. With the advent of multi electrode arrays (MEA) we can now record such activity. However, before we can investigate/test the population coding hypotheses using such recordings, we need to determine the number of neurons recorded by the MEA and their spiking activity, after spike detection, using an automatic spike sorting algorithm (we refer to the spiking activity of the neurons extracted from the MEA recordings as MEA sorted data). While there are many automatic spike sorting methods available, they have limitations. In addition, we are lacking methods to test/investigate the population coding hypotheses in detail using the MEA sorted data. That is, methods that show whether neurons respond in a hypothesised way and, if they do, shows how the stimulus is represented within the recorded area. Thus, in this thesis, we were motivated to, firstly, develop a new automatic spike sorting method, which avoids the limitations of other methods. We validated our method using simulated and biological data. In addition, we found our method can perform better than other standard methods. We next focused on the population rate coding hypothesis (i.e. the hypothesis that information is conveyed in the number of spikes fired by a pop- ulation of neurons within a relevant time period). More specifically, we developed a method for testing/investigating the population rate coding hypothesis using the MEA sorted data. That is, a method that uses the multi variate analysis of variance (MANOVA) test, where we modified its output, to show the most responsive subar- eas within the recorded area. We validated this using simulated and biological data. Finally, we investigated whether noise correlation between neurons (i.e. correlations in the trial to trial variability of the response of neurons to the same stimulus) in a rat's olfactory bulb can affect the amount of information a population rate code conveys about a set of stimuli. We found that noise correlation between neurons was predominately positive, which, ultimately, reduced the amount of information a population containing >45 neurons could convey about the stimuli by ~30%.