Towards Teaching a Robot to Count Objects



system has stopped on the representation of the ex-
act number of ob jects in the scene. The cooperation

0        20        40        60        80       100       120       140       160       180

0        20        40        60        80       100       120       140       160       180

0        20        40        60        80       100       120       140       160       180

Figure 5: Activity of four neurons in the number map.
From top to bottom, these neurons respond preferentially
for the words ’zero’ ’one’ ’two’ and ’three’.

between the two systems has been tested and Figure
5 shows the evolution of the activity of four neurons
on the
number map when the reward activity is pro-
duced by the attention-switching mechanism. These
neurons activate sequentially at each dopamine de-
pletion so that at end of the visual search the only
neuron remaining active is the neuron representing
“three”. This works with one, two or three visual
objects, but for more objects we would just need
more neurons in the
number map thanks to the dis-
tributed architecture of the system.

6. Conclusion

We presented here two computational models that
seem in a first view totally independent as they deal
with different modalities (vision and reproduction of
phonetical sequences), but that can cooperate to pro-
duce a new behaviour, namely a cardinal counting
task. Our hypothesis was that counting objects in a
scene needs to sequentially focus these ob jects (what
is a motor ability) and to associate this sequence
with the remembered ordinal sequence of numbers.
These two models have two separate basal ganglia
channels that communicate via a unique dopamin-
ergic unit, what is coherent with the diffuse inner-
vation of dopamine throughout the Striatum. The
first model works in real-time on a robot but for rea-
sons of computational cost the merging of the two
systems has only been tested in simulation. Never-
theless, there are some problems: the coding of num-
bers from phonological inputs is not enough compact
and stands for numbers up to ten maybe. We would
need another architecture to deal with greater num-
bers. The sequence-learning system also learns the
sequence offline (without the attention mechanism),
it would be an interesting feature if the learning oc-
cured online.

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