Direction selectivity in the retina requires the asymmetric wiring of inhibitory inputs onto four subtypes of On-Off direction-selective ganglion cells (DSGCs), each preferring motion in one of four cardinal directions. The primary model for the development of direction selectivity is that patterned activity plays an instructive role. Here, we use a unique, large-scale multielectrode array to demonstrate that DSGCs are present at eye opening, in mice that have been reared in darkness and in mice that lack cholinergic retinal waves. These data suggest that direction selectivity in the retina is established largely independent of patterned activity and is therefore likely to emerge as a result of complex molecular interactions.

Ambient GABA modulates firing patterns in adult neural circuits by tonically activating extrasynaptic GABA A receptors. Here, we demonstrate that during a developmental period when activation of GABA A receptors causes membrane depolarization, tonic activation of GABA A receptors blocks all spontaneous activity recorded in retinal ganglion cells (RGCs) and starburst amacrine cells (SACs). Bath application of the GABA A receptor agonist muscimol blocked spontaneous correlated increases in intracellular calcium concentration and compound postsynaptic currents in RGCs associated with retinal waves. In addition, GABA A receptor agonists activated a tonic current in RGCs that significantly reduced their excitability. Using a transgenic mouse in which green fluorescent protein is expressed under the metabotropic glutamate receptor subtype 2 promoter to target recordings from SACs, we found that GABA A receptor agonists blocked compound postsynaptic currents and also activated a tonic current. GABA A receptor antagonists reduced the holding current in SACs but not RGCs, indicating that ambient levels of GABA tonically activate GABA A receptors in SACs. GABA A receptor antagonists did not block retinal waves but did alter the frequency and correlation structure of spontaneous RGC firing. Interestingly, the drug aminophylline, a general adenosine receptor antagonist used to block retinal waves, induced a tonic GABA A receptor antagonist-sensitive current in outside-out patches excised from RGCs, indicating that aminophylline exerts its action on retinal waves by direct activation of GABA A receptors. These findings have implications for how various neuroactive drugs and neurohormones known to modulate extrasynaptic GABA A receptors may influence spontaneous firing patterns that are critical for the establishment of adult neural circuits.

The metric structure of synaptic connections is obviously an important factor in shaping the properties of neural networks, in particular the capacity to retrieve memories, with which are endowed autoassociative nets operating via attractor dynamics. Qualitatively, some real networks in the brain could be characterized as 'small worlds', in the sense that the structure of their connections is intermediate between the extremes of an orderly geometric arrangement and of a geometry-independent random mesh. Small worlds can be defined more precisely in terms of their mean path length and clustering coefficient; but is such a precise description useful for a better understanding of how the type of connectivity affects memory retrieval? We have simulated an autoassociative memory network of integrate-and-fire units, positioned on a ring, with the network connectivity varied parametrically between ordered and random. We find that the network retrieves previously stored memory patterns when the connectivity is close to random, and displays the characteristic behavior of ordered nets (localized 'bumps' of activity) when the connectivity is close to ordered. Recent analytical work shows that these two behaviors can coexist in a network of simple threshold-linear units, leading to localized retrieval states. We find that they tend to be mutually exclusive behaviors, however, with our integrate-and-fire units. Moreover, the transition between the two occurs for values of the connectivity parameter which are not simply related to the notion of small worlds.

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block the region of the viral envelope protein gp120 critical for the virus binding to cellular receptor CD4 was developed using deep learning methods. The research were carried out to create the  architecture of the neural network, to form  virtual compound library of potential anti-HIV-1 agents for training the neural network, to make  molecular docking of all compounds from this library with gp120, to  calculate the values of binding free energy, to generate molecular fingerprints for chemical compounds from the training dataset. The training the neural network was implemented followed by estimation of the learning outcomes and work of the autoencoder.  The validation of the neural network on a wide range of compounds from the ZINC database was carried out. The use of the neural network in combination with virtual screening of chemical databases was shown to form a productive platform for identifying the basic structures promising for the design of novel antiviral drugs that inhibit the early stages of HIV infection.