Archives pour la catégorie Local learning rules for spiking neurons with dendrite

WCCI conference San Jose 2012

Temporal and rate decoding in spiking neurons with dendrites

The temporal DC neuron specifically recognizes a spatio-temporal pattern of spikes. Any modification to the temporal or the spatial scheme of this pattern leads to a much attenuated response even when the global number of spikes remains unchanged.
On the other hand, the maximum membrane potential reached by a frequential DC neuron, for which the synapses are placed at regular intervals on its dendrite, depends primarily on the number of spikes in the input train (the average frequency) and is less influenced by the precise timing of each spike.
This study has successfully shown that dendrites play a much greater role in transmission than simply increasing the surface area of membrane able to accommodate more synapses. By regarding dendrites as an integration line with time delay, one can understand that the addition of only one dendrite can considerably increase the processing capacities of the point neuron, the main function of which is coincidence detection. Consequentially, the synchronization between neurons becomes the fundamental information medium in a neural network of spiking point neurons. Indeed, synchronization has been the subject of extensive research aimed at understanding information processing in the nervous system. In the case of dendritic computation as presented in this study, synchronization phenomena lose their importance, since a neuron with a dendrite can identify any spatio-temporal pattern specifically, or respond to a defined average spiking rate. It is thus unnecessary to use a network of synchronized point neurons to make this detection; only a single neuron with a dendrite is enough to achieve the same goal.
This study demonstrates that a single neuron with only one dendritic segment can perform either frequency decoding or temporal decoding of spatio-temporal spike patterns. The position and number of synapses on the dendrite are enough to determine this function. With this model of dendritic computation, one is thus left wondering as to whether a biological neuron codes/decodes average frequency or temporal sequences, since now it has now been shown that a neuron with only one dendrite can do both, the choice of which likely depends on the needs of the central nervous system. The nervous system would hence have two functional bricks used according to the required tasks. For instance, to carry out a task of sound or image recognition: the image or sound is recoded by the primary cortices, A1 in the case of sound or V1 in the case of an image, as spatio-temporal patterns of spikes. The identification of a particular spatio-temporal pattern of spikes becomes a key task to recognizing specific schemes or objects in this image or sound. This can be done using the temporal neuron presented in this study. To identify the intensity of a stimulus, as is needed to determine if a sound is close or distant, it would be more interesting to use in this case the frequential neuron since intensities are often coded by firing rate in the central nervous system.