Biology subjects

Dragoi, V.

Publications and source records attributed to Dragoi, V..

2 recordsLinked to original sources

bioRxiv2019

The structure of the population code in neural ensembles of V4 shapes pair-wise interactions on different time scales.

In visual areas of primates, neurons activate in parallel while the animal is engaged in a behavioral task. In this study, we examine the structure of the population code while the animal performs delayed match to sample task on complex natural images. The macaque monkeys visualized two consecutive stimuli that were either the same or different, while recorded with laminar arrays across the cortical depth in cortical areas V1 and V4. We decoded correct choice behavior from neural populations of simultaneously recorded units. Utilizing decoding weights, we divide neurons in most informative and less informative, and show that most informative neurons in V4, but not in V1, are more strongly synchronized, coupled and correlated than less informative neurons. As neurons are divided in two coding pools according to their coding preference, in V4, but not in V1, spiking synchrony, coupling and correlations within the coding pool are stronger than across coding pools. HighlightsO_LIIn a match-to-sample visual task, responses of neural populations in V1 and in V4 predict the stimulus class better than chance. C_LIO_LIIn V4, informative neurons are more strongly coupled, correlated and synchronized than less informative neurons. C_LIO_LIIn V4, neurons are more strongly coupled, correlated and synchronized within coding pools compared to across coding pools. C_LIO_LICorrelations within coding pools harm the performance of the classifier in both V1 and V4. C_LI

neuroscience↗

bioRxiv2019

Reading-out task variables as a low-dimensional reconstruction of neural spike trains in single trials.

We propose a new model of the read-out of spike trains that exploits the multivariate structure of responses of neural ensembles. Assuming the point of view of a read-out neuron that receives synaptic inputs from a population of projecting neurons, synaptic inputs are weighted with a heterogeneous set of weights. We propose that synaptic weights reflect the role of each neuron within the population for the computational task that the network has to solve. In our case, the computational task is discrimination of binary classes of stimuli, and weights are such as to maximize the discrimination capacity of the network. We compute synaptic weights as the feature weights of an optimal linear classifier. Once weights have been learned, they weight spike trains and allow to compute the post-synaptic current that modulates the spiking probability of the read-out unit in real time. We apply the model on parallel spike trains from V1 and V4 areas in the behaving monkey macaca mulatta, while the animal is engaged in a visual discrimination task with binary classes of stimuli. The read-out of spike trains with our model allows to discriminate the two classes of stimuli, while population PSTH entirely fails to do so. Splitting neurons in two subpopulations according to the sign of the weight, we show that population signals of the two functional subnetworks are negatively correlated. Disentangling the superficial, the middle and the deep layer of the cortex, we show that in both V1 and V4, superficial layers are the most important in discriminating binary classes of stimuli.

neuroscience↗