Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

Abstract

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

An application of neighbourhoods in digraphs to the classification of binary dynamics

“…In particular it is shown there that an informed choice of neighbourhood improves predictability when compared to traditional methods. Interestingly, selection of neighbourhoods that improved performance with the technique presented in [19], show reduced performance with the techniques presented in this article, and vice versa. In both projects a classification accuracy of nearly 90% was achievable, but with different sorting parameters.…”

“…In this paper we will consider particularly closed neighbourhoods, and for the sake of lighter nomenclature we introduce the term S-tribe. The same terminology is used in the paper [19], which is closely related to this article.…”

“…The dataset thus consists of 4456 binary dynamics functions, each defined on an interval of 200ms. More detail on the source of data, biological analysis and an alternative approach to classification of the same data is in [19].…”

“…To test our methods, we apply them to data generated by the Blue Brain Project team [3] that was used in [19] for signal classification by established neuroscience methodology. The data consists of eight families of neuronal stimuli that are injected in a random sequence to the digital reconstruction of the neocortical column of a young rat.…”

“…In [19] the same dataset is studied by standard techniques of computational neuroscience combined with the ideas presented in this paper. In particular it is shown there that an informed choice of neighbourhood improves predictability when compared to traditional methods.…”