Throughout life, new neurons are added to the brain. Just like people arriving late to a cocktail party, the newbies need to figure out how to integrate with those already embroiled in conversations. The zebrafish brain, already capable of complex visual processing at larval stages, accepts new neurons throughout the fish life span. Boulanger-Weill et al. tracked the location, movement, and functional integration of single newborn neurons in developing zebrafish larvae. Following their own developmental trajectories, newborn neurons began with limited dendritic arbors, no neurotransmitter identity, and spontaneous, but not directed, activity.
This paper presents a constructive algorithm that achieves successful one-shot learning of hidden spike-patterns in a competitive detection task. It has previously been shown (Masquelier et al., 2008) that spike-timing-dependent plasticity (STDP) and lateral inhibition can result in neurons competitively tuned to repeating spike-patterns concealed in high rates of overall presynaptic activity. One-shot construction of neurons with synapse weights calculated as estimates of converged STDP outcomes results in immediate selective detection of hidden spike-patterns. The capability of continual learning is demonstrated through the successful one-shot detection of new sets of spike-patterns introduced after long intervals in the simulation time. Simulation expansion (Lightheart et al., 2013) has been proposed as an approach to the development of constructive algorithms that are compatible with simulations of biological neural networks. A simulation of a biological neural network may have orders of magnitude fewer neurons and connections than the related biological neural systems; therefore, simulated neural networks can be assumed to be a subset of a larger neural system. The constructive algorithm is developed using simulation expansion concepts to perform an operation equivalent to the exchange of neurons between the simulation and the larger hypothetical neural system. The dynamic selection of neurons to simulate within a larger neural system (hypothetical or stored in memory) may be a starting point for a wide range of developments and applications in machine learning and the simulation of biology.
A COMPLETE map of all the neurons and their connections in both sexes of an animal has been described for the first time. This "connectome" will not only help us understand how neurons work, but could also improve our understanding of human mental-health problems. The tiny soil-dwelling nematode worm Caenorhabditis elegans has long been used for research because it has so few neurons.
What do we say first when we talk about brain? We say that it is so complex that we still couldn't understand it completely. I will make an assumption for the structure of the brain and, I will examine the case which is mentioned in "The Brain That Remade Itself". Nerves are just binary switches. When they are used for an information, they are 1.
Experiments reveal that in the dorsal medial superior temporal (MSTd) and the ventral intraparietal (VIP) areas, where visual and vestibular cues are integrated to infer heading direction, there are two types of neurons with roughly the same number. One is "congruent" cells, whose preferred heading directions are similar in response to visual and vestibular cues; and the other is "opposite" cells, whose preferred heading directions are nearly "opposite" (with an offset of 180 degree) in response to visual vs. vestibular cues. Congruent neurons are known to be responsible for cue integration, but the computational role of opposite neurons remains largely unknown. Here, we propose that opposite neurons may serve to encode the disparity information between cues necessary for multisensory segregation. We build a computational model composed of two reciprocally coupled modules, MSTd and VIP, and each module consists of groups of congruent and opposite neurons.