Recognition

In this first example, we have learned one period-3 elementary sequence ( $\mathbf{s}=(1,2,3)$). We then run the spontaneous dynamics (1), and present different input signals, some of them corresponding to the learned sequence, and others being unknown (Fig.4). The point is to check that the feedback signal (i) is stimulus-dependent and (ii) can adapt dynamically to its inputs.

Figure: Activation dynamics, after learning the elementary sequence $\mathbf{s}=(1,2,3)$, while presenting different stimuli. Input changes take place at $t=31$ (Input shift) and $t=61$ (reverse temporal order). $N^{(1)}=200$, $N^{(2)}=200$, other parameters are in Tab.1. - a - Neuronal activity on secondary layer. 20 individual signals are represented, with their mean activity. - b - Input signal (I) and Feedback reinforcement (F). Only the values corresponding to the three first primary neurons are represented between $t=1$ and $t=74$ (some time steps have been discarded for readability).

During the 30 first steps, the input stimulus corresponds to the learned one. After 10-12 transient steps, the system reaches its attractor, and the feedback signal is activated. At time $t=32$, a phase shift occurs on the input signal. The misfit between input and feedback signals leads to a transitory decrease of the feedback signal, which allows the inner dynamics to adapt to the input. After this new transient time, the feedback signal is again in synchrony with the input signal. Last, at time $t=62$, we reverse the time order of the input signal. In that case, the feedback signal fades out. So, one can remark that the activation of the feedback reinforcement is very sensitive to the spatio-temporal structure of the input signal, i.e. is stimulus specific. It grounds on a coincidence detection principle, i.e. to the detection of a specific sequence of activations in the secondary layer. This secondary layer organization is also stimulus-specific, and is in particular highly sensitive to the time order of the input signal. For that reason, a change in the time order of the spatial inputs modifies the inner organization and thus deactivates the feedback reinforcement. The system has thus learned to discriminate between one relevant stimulus and other non-relevant (non-learned) stimuli (spatial and/or temporal mismatch - we have verified that a spatial mismatch deactivates the feedback signal in the same fashion). When can we say that a system ``recognizes'' the input pattern? Most models that rely on resonant principles have to use a global attentional threshold which determines whether a new input pattern is coherent with previously learned patterns or not [5]. In our system, we define the recognition as the dynamic process which corresponds to the activation of a specific feedback signal. Recognition thus takes place without global control. It relies on the emergence of a specific dynamical configuration. It is thus more simple (or more ``natural'') in its principle: the gating function grounds on the coherent behavior of the whole population of neurons. The ``decision'' to recognize (or to ignore) a given stimulus comes from the collective activity of all the neurons belonging to the perceptual system.