Abstract
Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the “paradoxical” effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.
Highlights
Across different modalities, sensory cortical neurons share a common response pattern: they respond sparsely, only a few neurons are active at any time, and selectively, a neuron responds to only few stimuli [1]
We found that despite its random architecture the network can exhibit high stimulus selectivity and sparse population response, if the network is in the balanced state [31,32]
Selectivity and Sparseness are Naturally Generated in the Balanced State First we asked whether selectivity and sparseness could be generated in a network with random connectivity, where connection probability and the synaptic strength depend only on whether the presynaptic neuron is excitatory or inhibitory and not on any other feature
Summary
Sensory cortical neurons share a common response pattern: they respond sparsely, only a few neurons are active at any time, and selectively, a neuron responds to only few stimuli [1] This has been observed in visual cortex [2,3,4,5], in olfactory cortex [6,7,8], in auditory cortex [9,10] and in somatosensory cortex [11,12]. There are cases where sparse and selective responses are observed, while no significant preferential connectivity between cells with similar stimulus tuning can be detected experimentally This is true in olfactory cortex [23,24,25,26,27,28,29] or in visual cortices of mice at eye-opening [30]. These examples motivate an important theoretical question: Can selectivity and sparseness be generated and maintained in randomly connected networks? Is it necessary for connection probabilities or strengths to be chosen as a function of stimulus tuning?
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