What is the nature of neural representations? How does the activity of neurons in the brain reflect the external stimuli and internal states of the animal? Many studies addressing this question searched for single neurons with easily interpretable activity profiles – for example, neurons whose firing patterns tightly correspond to a particular stimulus or internal state. Striking examples of such neurons were found in several systems: orientation tuning in the early visual system, grid and place cells in the hippocampal and parahippocampal regions and even medial temporal lobe neurons responding to the face of specific actresses. Yet, it is likely that all behaviors are the result of a concerted action of large populations of neurons, and thus it would seem unlikely that such orderly single neurons can account for the entirety of network behavior. Indeed, in higher areas such as the prefrontal cortex, and even in more peripheral areas such as V1 there exist many neurons whose activity profiles defy simple explanations. The response of such neurons is typically explained by assuming that they do not directly participate in the task at hand.
I have recently begun testing an alternative hypothesis that explains both the orderly and the complex response profiles observed in experiments. Specifically, relying on results from the field of reservoir computing, I found that unstructured network models can be trained to perform tasks that were previously solved by carefully designed networks containing well-tuned single neurons.
I am now investigating the advantages and limitations of this approach, using measures such as performance, flexibility and robustness as indicators. Between the two extremes of engineered and unstructured networks lies a continuum of possible network models. I am exploring this continuum under the hypothesis that training a network for a specific task can gradually shift it from unstructured to structured. I will use experimental data from my collaborators to evaluate where along this continuum specific real networks reside. To this end, I am developing different analysis methods, evaluating their sensitivity to detect underlying network mechanisms, and later applying these methods to recorded data.