Amanda Joy Foust | Search bioRxiv

bioRxiv2015

On the distribution and function of synaptic clusters

Local non-linearities in dendrites render neuronal output dependent on the spatial distribution of synapses. A neuron will activate differently depending on whether active synapses are spatially clustered or dispersed. While this sensitivity can in principle expand neuronal computational capacity, it has thus far been employed in very few learning paradigms. To make use of this sensitivity, groups of correlated neurons need to make contact with distinct dendrites, and this requires a mechanism to ensure the correct distribution of synapses contacting from distinct ensembles. To address this problem, we introduce the requirement that on a short time scale, a pre-synaptic neuron makes a constant number of synapses with the same strength on a post-synaptic neuron. We find that this property enables clusters to distribute correctly and guarantees their functionality. Furthermore, we demonstrate that a change in the input statistics can reshape the spatial distribution of synapses. Finally, we show under which conditions clusters do not distribute correctly, e.g. when cross-talk between dendrites is too strong. As well as providing insight into potential biological mechanisms of learning, this work paves the way for new learning algorithms for artificial neural networks that exploit the spatial distribution of synapses.

Preprint↗

bioRxiv2015

Dendrites enable a robust mechanism for neuronal stimulus selectivity

Hearing, vision, touch -- underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Non-linear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of non-preferred stimuli. Using a multi-subunit non-linear model, we demonstrate that selectivity can arise from the spatial distribution of synapses. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Contrary to an equivalent linear model, our model can maintain stimulus selectivity even when 50% of synapses fail or when more than 50% of dendrites fail. We then use a Layer 2/3 biophysical neuron model to show that our implementation is consistent with recent experimental observations, of a mixture of selectivities in dendrites, that can differ from the somatic selectivity, and of hyperpolarization broadening somatic tuning without affecting dendritic tuning. Our model predicts that an initially non-selective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

Neuroscience↗