A Heterosynaptic Learning Rule for Neural Networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.Comment: 19 page

Beyond Hebb: Exclusive-OR and Biological Learning

A learning algorithm for multilayer neural networks based on biologically plausible mechanisms is studied. Motivated by findings in experimental neurobiology, we consider synaptic averaging in the induction of plasticity changes, which happen on a slower time scale than firing dynamics. This mechanism is shown to enable learning of the exclusive-OR (XOR) problem without the aid of error back-propagation, as well as to increase robustness of learning in the presence of noise.Comment: 4 pages RevTeX, 2 figures PostScript, revised versio

Encoding of Spatio-Temporal Input Characteristics by a CA1 Pyramidal Neuron Model

The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code

Prospects of molecular genetic approaches in controlling technological properties of wheat grain in the context of the "grain – flour – bread" chain

The potential of molecular approaches in the management of the technological properties of wheat grain affecting the quality of the end product of bread industry is considered. Currently, along with the growth of grain production, the traditional range of staples is crowded out, the quality of most popular bread varieties deteriorates, and dozens of different substances of biological and chemical origin are used as bread improvers. Meanwhile, the genetic potential of wheat allows the development of varieties with technological parameters of grain suitable for production of high-quality bread. In Russia, multiple examples of the creation of varieties for the production of first and second-class grain are known, and modern molecular genetics offers techniques that can supplement classical breeding approaches and accelerate the development of new varieties adapted to the conditions and requirements of the baking industry, using the natural genetic potential of wheat. We summarize the diversity of requirements for grain and flour for different end-use products. Statistics on the volume and structure of grain quality in Russia in 2011–2014 is analyzed. An essential deformation of the quality structure of the produced wheat grain in favor of less valuable classes is observed. A brief retrospective analysis of research in wheat genetics, demonstrating the contribution of genetic factors to grain and flour technological properties, is performed. Various approaches to rapid breeding of varieties with desired properties are considered in relation with the development of plant molecular genetics. The paper provides examples illustrating the feasibility of using methods of DNA diagnostics in various stages of the process, during which the genetic potential of food crops expresses itself and affects the quality of the end product. The results of molecular studies on the localization and isolation of genes determining technological properties (protein and wet gluten contents, milling properties, rheological properties, flour color, and starch properties) are reviewed. The data on diagnostic DNA markers, which are suitable for efficient selection of genotypes instead of the time-consuming analysis of technological properties during breeding process, are summarized. Thus, the information about the genetic potential of bread wheat and modern technologies that provide grounds for moving from excessive use of chemicals to a more benign and organic effect on the quality of the products throughout the "grain – flour – bread" chain, is summarized