Abstract
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.
Highlights
A half century ago, pioneers of neuroscience have stated the following: “At present we have no direct evidence on how the cortex transforms the incoming visual information
All experiments performed in this study were conducted in accordance with protocols approved by The Animal Welfare Committee (AWC) and the Institutional Animal Care and Use Committee (IACUC) for McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), and met or exceeded the standards proposed by the National Institutes of Health’s Guide for the Care and Use of Laboratory Animals
We presented a new model of the read-out of parallel spike trains that exploits the structure of the population code
Summary
A half century ago, pioneers of neuroscience have stated the following: “At present we have no direct evidence on how the cortex transforms the incoming visual information. Reading-out task variables from parallel spike trains the meantime, the question, posed by Hubel and Wiesel, has not yet found a clear answer [2]. Addressing this question requires observing the activity of many neurons simultaneously and has demanded an important progress in recording techniques. On the one hand, spiking patterns of neural populations in sensory areas are highly variable across trials [9], and can be described as a probabilistic process. Our main question here is how does the brain transform a high-dimensional probabilistic signal, enacted by spike trains of cortical populations, into a reliable signal, that presumably underlies coherent animal behavior
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