Modulating the Granularity of Category Formation by Global Cortical States

The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category formation, we consider an unbroken continuum of stimuli without intrinsic category boundaries. We show that a competitive network, shaped by recurrent inhibition and endowed with Hebbian and homeostatic synaptic plasticity, can enforce stimulus categorization. The degree of competition is internally controlled by the neuronal gain and the strength of inhibition. Strong competition leads to the formation of many attracting network states, each being evoked by a distinct subset of stimuli and representing a category. Weak competition allows more neurons to be co-active, resulting in fewer but larger categories. We conclude that the granularity of cortical category formation, i.e., the number and size of emerging categories, is not simply determined by the richness of the stimulus environment, but rather by some global internal signal modulating the network dynamics. The model also explains the salient non-additivity of visual object representation observed in the monkey inferotemporal (IT) cortex. Furthermore, it offers an explanation of a previously observed, demand-dependent modulation of IT activity on a stimulus categorization task and of categorization-related cognitive deficits in schizophrenic patients

Stimulus sampling as an exploration mechanism for fast reinforcement learning

Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types of noise tend to almost average out—precluding or significantly hindering learning —when coding in neuronal populations or by mean firing rates is considered. Furthermore, careful tuning is required to find the elusive balance between the often conflicting demands of speed and reliability of learning. Here we show that there is in fact no need to rely on intrinsic noise. Instead, ongoing synaptic plasticity triggered by the naturally occurring online sampling of a stimulus out of an entire stimulus set produces enough fluctuations in the synaptic efficacies for successful learning. By combining stimulus sampling with reward attenuation, we demonstrate that a simple Hebbian-like learning rule yields the performance that is very close to that of primates on visuomotor association tasks. In contrast, learning rules based on intrinsic noise (node and weight perturbation) are markedly slower. Furthermore, the performance advantage of our approach persists for more complex tasks and network architectures. We suggest that stimulus sampling and reward attenuation are two key components of a framework by which any single-cell supervised learning rule can be converted into a reinforcement learning rule for networks without requiring any intrinsic noise sourc

Do the processes in near-earth space influence weather and climate?

The validity of the point of view about predominance of the influence of solar activity on the Earth's climate is shown. Complex and spatially inhomogeneous meteoparameters dynamic changes including temperature, correlate with Space weather parameters. A straight dependence of the surface temperature from Solar activity, as it was in epochs of Grand Minima of Solar activity, allows us to construct the temperature prediction in accordance with solar activity forecast for the XIX century. It leads to the conclusion about soon next temperature decrease period on a global scale, in contrast to the predictions global warming. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Stimulus sampling as an exploration mechanism for fast reinforcement learning

Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types of noise tend to almost average out—precluding or significantly hindering learning —when coding in neuronal populations or by mean firing rates is considered. Furthermore, careful tuning is required to find the elusive balance between the often conflicting demands of speed and reliability of learning. Here we show that there is in fact no need to rely on intrinsic noise. Instead, ongoing synaptic plasticity triggered by the naturally occurring online sampling of a stimulus out of an entire stimulus set produces enough fluctuations in the synaptic efficacies for successful learning. By combining stimulus sampling with reward attenuation, we demonstrate that a simple Hebbian-like learning rule yields the performance that is very close to that of primates on visuomotor association tasks. In contrast, learning rules based on intrinsic noise (node and weight perturbation) are markedly slower. Furthermore, the performance advantage of our approach persists for more complex tasks and network architectures. We suggest that stimulus sampling and reward attenuation are two key components of a framework by which any single-cell supervised learning rule can be converted into a reinforcement learning rule for networks without requiring any intrinsic noise source

Episodic activity in a heterogeneous excitatory network, from spiking neurons to mean field

This article describes a study of the preschool children’s communication with their mates in joint mental activity, one of the results of our study was the establishment a correlation between the intensity and effectiveness of the communication.&nbsp

RANKING RUSSIAN UNIVERSITIES: HOW TO MEASURE?

The authors propose to use vector representation of university areas of activities for ranking universities. Two novel indices reflecting UN experts’ recommendations are introduced. Based on expert estimates, a priority vector of weights enables to represent the university activity

DEVELOPMENT AND IMPLEMENTATION OF MODERN EDUCATION POLICY OF HIGHER EDUCATION INSTITUTION

This article considers the main requirements to be taken into account when developing a university’s education policy. The authors discuss conditions for the implementation of an innovative education policy and dwell on internal and external criteria for the evaluation of an education policy. The article substantiates the necessity of using different time scales when implementing a university education policy. Attention is drawn to the need for a gradual but significant overhaul of the curricula and syllabi, whereby only the universally useful knowledge and skills would remain. Three-level decision-making system to implement an education policy is introduced. Any university ought to combine in a holistic way all of its policies, such as education, research, financial, and planning ones. Therefore, their optimization should be carried out simultaneously

Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding

<div><p>Predictive coding has been previously introduced as a hierarchical coding framework for the visual system. At each level, activity predicted by the higher level is dynamically subtracted from the input, while the difference in activity continuously propagates further. Here we introduce modular predictive coding as a feedforward hierarchy of prediction modules without back-projections from higher to lower levels. Within each level, recurrent dynamics optimally segregates the input into novelty and familiarity components. Although the anatomical feedforward connectivity passes through the novelty-representing neurons, it is nevertheless the familiarity information which is propagated to higher levels. This modularity results in a twofold advantage compared to the original predictive coding scheme: the familiarity-novelty representation forms quickly, and at each level the full representational power is exploited for an optimized readout. As we show, natural images are successfully compressed and can be reconstructed by the familiarity neurons at each level. Missing information on different spatial scales is identified by novelty neurons and complements the familiarity representation. Furthermore, by virtue of the recurrent connectivity within each level, non-classical receptive field properties still emerge. Hence, modular predictive coding is a biologically realistic metaphor for the visual system that dynamically extracts novelty at various scales while propagating the familiarity information.</p></div

Do the processes in near-earth space influence weather and climate?

The validity of the point of view about predominance of the influence of solar activity on the Earth's climate is shown. Complex and spatially inhomogeneous meteoparameters dynamic changes including temperature, correlate with Space weather parameters. A straight dependence of the surface temperature from Solar activity, as it was in epochs of Grand Minima of Solar activity, allows us to construct the temperature prediction in accordance with solar activity forecast for the XIX century. It leads to the conclusion about soon next temperature decrease period on a global scale, in contrast to the predictions global warming. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only