Computational neuroscience is the science of how the brain “computes”, that is, how the brain performs cognitive functions such as recognizing a face or walking. Here I will argue that most models of cognition developed in the field, especially as regards sensory systems, are actually not biological models but hybrid models consisting of a neural model together with an abstract model.

First of all, many neural models are not meant to be models of cognition. For example, there are models that are developed to explain the irregular spiking of cortical neurons, or oscillations. I will not consider them. According to the definition above, I categorize them in theoretical neuroscience rather than computational neuroscience. Here I consider for example models of perception, memory, motor control.

An example that I know well is the problem of localizing a sound source from timing cues. There are a number of models, including a spiking neuron model that we have developed (Goodman and Brette, 2010). This model takes as input two sound waves, corresponding to the two monaural sounds produced by the sound source, and outputs the estimated direction of the source. But the neural model, of course, does not output a direction. Rather, the output of the neural model is the activity of a layer of neurons. In the model, we consider that direction is encoded by the identity of the maximally active neuron. In another popular model in the field, direction is encoded by the relative total activity of two groups of neurons (see our comparison of models in Goodman et al. 2013). In all models, there is a final step which maps the activity of neurons to estimated sound location, and this step is not a neural model but an abstract model. This causes big epistemological problems when it comes to assessing and comparing the empirical value of models because a crucial part of the models is not physiological. Some argue that neurons are tuned to sound location; others that population activity varies systematically with sound location. Both are right, and thus none of these observations is a decisive argument to discriminate between the models.

The same is seen in other sensory modalities. The output is the identity of a face; or of an odor; etc. The symmetrical situation occurs in motor control models: this time the abstract model is on the side of the input (mapping from spatial position to neural activity or neural input). Memory models face this situation twice, with abstract models both on the input (the thing to be memorized) and the output (the recall).

Fundamentally, this situation has to do with the fact that most models in computational neuroscience take a representational approach: they describe how neural networks represent in their firing some aspect of the external world. The representational approach requires defining a mapping (called the “decoder”) from neural activity to objective properties of objects, and this mapping cannot be part of the neural model. Indeed, sound location is a property of objects and thus does not belong to the domain of neural activity. So no sound localization model can ever be purely neuronal.

Thus to develop biological models, it is necessary to discard the representational approach. Instead of “encoding” things, neurons control the body; neurons are agents (rather than painters in the representational approach). For example, a model of sound localization should be a model of an orientational response, including the motor command. The model explains not how space is “represented”, but how an animal orients its head (for example) to a sound source. When we try to model an actual behavior, we find that the nature of the problem changes quite significantly. For example, because a particular behavior is an event, neural firing must also be seen as events. In this context, counting spikes and looking at the mutual information between the count and some stimulus property is not very meaningful. What matters is the events that the spikes trigger in the targets (muscles or other neurons). The goal is not to represent the sensory signals but to produce an appropriate behavior. One also realizes that the relation between sensory signals and actions is circular, and therefore cannot be adequately described as “processing”: sensory signals make you turn the head, but if you turn the head, the sensory signals change.

Currently, most models of cognition in computational neuroscience are not biological models. They include neuron models together with abstract models, a necessity stemming from the representational approach. To a make biological model requires including a model of the sensorimotor loop. I believe this is the path that the community should take.