The direction of image motion is coded by direction-selective (DS) ganglion cells in the retina. Particularly, the ON DS ganglion cells project their axons specifically to terminal nuclei of the accessory optic system (AOS) responsible for optokinetic reflex (OKR). We recently generated a knock-in mouse in which SPIG1 (SPARC-related protein containing immunoglobulin domains 1)-expressing cells are visualized with GFP, and found that retinal ganglion cells projecting to the medial terminal nucleus (MTN), the principal nucleus of the AOS, are comprised of SPIG1+ and SPIG1− ganglion cells distributed in distinct mosaic patterns in the retina. Here we examined light responses of these two subtypes of MTN-projecting cells by targeted electrophysiological recordings. SPIG1+ and SPIG1− ganglion cells respond preferentially to upward motion and downward motion, respectively, in the visual field. The direction selectivity of SPIG1+ ganglion cells develops normally in dark-reared mice. The MTN neurons are activated by optokinetic stimuli only of the vertical motion as shown by Fos expression analysis. Combination of genetic labeling and conventional retrograde labeling revealed that axons of SPIG1+ and SPIG1− ganglion cells project to the MTN via different pathways. The axon terminals of the two subtypes are organized into discrete clusters in the MTN. These results suggest that information about upward and downward image motion transmitted by distinct ON DS cells is separately processed in the MTN, if not independently. Our findings provide insights into the neural mechanisms of OKR, how information about the direction of image motion is deciphered by the AOS.
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Neuronal avalanches and asynchronous irregular (AI) firing patterns have been thought to represent distinct frameworks to understand the brain spontaneous activity. The former is typically present in systems where there is a balance between the slow accumulation of tension and its fast dissipation, whereas the latter is accompanied by the balance between synaptic excitation and inhibition (E/I). Here, we develop a new theory of E/I balance that relies on two homeostatic adaptation mechanisms: the short-term depression of inhibition and the spike-dependent threshold increase. First, we turn off the adaptation and show that the so-called static system has a typical critical point commonly attributed to self-organized critical models. Then, we turn on the adaptation and show that the network evolves to a dynamic regime in which: (I) E/I synapses balance regardless of any parameter choice; (II) an AI firing pattern emerges; and (III) neuronal avalanches display visually accurate power laws. This is the first time that these three phenomena appear simultaneously in the same network activity. Thus, we show that the once thought opposing frameworks may be unified into a single dynamics, provided that adaptation mechanisms are in place. In our model, the AI firing pattern is a direct consequence of the hovering close to the critical line where external inputs are compensated by threshold growth, creating synaptic balance for any E/I weight ratio.Author summaryTwo competing frameworks are employed to understand the brain spontaneous activity, both of which are backed by computational and experimental evidence: globally asynchronous and locally irregular (AI) activity arises in excitatory/inhibitory balanced networks subjected to external stimuli, whereas avalanche activity emerge in excitable systems on the critical point between active and inactive states. Here, we develop a new theory for E/I networks and show that there is a state where synaptic balance coexists with AI firing and power-law distributed neuronal avalanches. This regime is achieved through the introducing of short-time depression of inhibitory synapses and spike-dependent threshold adaptation. Thus, the system self-organizes towards the balance point, such that its AI activity arises from quasicritical fluctuations. The need for two independent adaptive mechanisms explains why different dynamical states are observed in the brain.
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