Figures & data
Figure 1. Model of a neuron with active dendrites and dynamic synapses.
Figure 2. (A) Membrane potential at the soma generated by a single input spike train arriving at a single synapse (two cases of synapses with different weights); (B) Zoom-in around the peak of membrane potential for w = 0.8.
Figure 3. Integration of two spike trains through two dynamic synapses with different strength attached to separate active dendrites. The onset of one of the spike trains is delayed by 300 ms. Top: Both spike trains arrive at two synapses with equal strength, w 1, 2 = 0.45 each. Bottom: The first spike train arrives at a synapse with strength w 1 = 0.3 and generates a peak of the partial membrane potential around 800 ms after the onset time of the stimulus. The second spike train arrives at synapse with strength w 2 = 0.6 and generates a peak partial potential around 500 ms after the onset time. The response of the neuron to the first spike train is delayed (prolonged) compared to the response to the second one. As a result, the neuron has achieved a quasi-synchronization of the partial membrane potentials generated from the spike trains (middle column, bottom graph), and its response (the membrane potential at the soma, bottom right graph) is much stronger.
Figure 4. (A) The correction signal that would be sent from the dendrite to a synapse with weight 0.8 or 0.3 in the event of a post-synaptic spike.
, i.e. no change in the weight occurs, if the post-synaptic spike is at the point of the maximum of
. (B)
plotted against the relative time between the pre- and post-synaptic spikes (t
post
−t
pre
) and the synaptic weight
.
Figure 5. The asymmetric learning window for synapses attached to the soma. A = e, B = 0.6 and t lwin = 100 ms.
Figure 6. The network architecture for short word phoneme sequence recognition. All word recognition neurons receive connections from the phoneme neurons via dynamic synapses attached to active dendrites and are connected to each other via synapses attached to the soma.
Figure 7. Two typical map formations of the words ‘bat’, ‘tab’, ‘babat’ and ‘tatab’. Each neuron responds only to one particular word. Words that sound similar, i.e. have similar phoneme sequences are recognized by neurons in neighbouring clusters.
Figure 8. Recognizing the words ‘bat’ (top), ‘tab’ (middle) and ‘babat’ (bottom). Left column: The input spike trains for the phonemes representing the three words. Middle column: Output spikes of the small clusters of neurons recognizing the particular word. Each plot line represents the activity of the 16 neurons from map 1 on . Neuron number 0 is the bottom left unit from the map, neuron 3 is the bottom right unit and neuron 15 is the top right unit. Right column: Total membrane potentials at the soma for three neurons recognizing the three words. Neuron 13 responds only to the word ‘bat’, neuron 10 responds only to the word ‘tab’ and neuron 0 responds only to the word ‘babat’.
Figure 9. Processing ‘tatab’ with noise in the onset times(up to 40%); and ‘bat’ with noise in the onset times and lower frequency random spikes from the non-active phonemes.
Figure 10. Robot’s language instruction system.
Table 1. Phoneme sequences of the words.
Figure 11. Network architecture and self-organized map of the word recognition neurons after training.
Figure 12. Processing the words ‘drop’, ‘stop’, and ‘right’. Top and middle: small clusters of neurons responding to noisy input for the words ‘drop’ and ‘stop’. Bottom: no output neurons from the network responded to the [riht] sequence. Although the neurons recognizing the word ‘right’ [rayt] are still significantly potentiated, the input stimulus is not sufficient to trigger a post-synaptic spike.
Figure 13. Processing the words ‘Bot turn left’.
Figure A14. τ d and R d plotted as a function of the weight of the synapse at which a single spike has arrived (with τ s = 2 ms and τ m = 60 ms).