The Interplay between Reproductive Social Stimuli and Adult Olfactory Bulb Neurogenesis

Adult neurogenesis is a striking form of structural plasticity that adapts the brain to the changing world. Accordingly, new neuron production is involved in cognitive functions, such as memory, learning, and pattern separation. Recent data in rodents indicate a close link between adult neurogenesis and reproductive social behavior. This provides a key to unravel the functional meaning of adult neurogenesis in biological relevant contexts and, in parallel, opens new perspectives to explore the way the brain is processing social stimuli. In this paper we will summarize some of the major achievements on cues and mechanisms modulating adult neurogenesis during social behaviors related to reproduction and possible role/s played by olfactory newborn neurons in this context. We will point out that newborn interneurons in the accessory olfactory bulb (AOB) represent a privileged cellular target for social stimuli that elicit reproductive behaviors and that such cues modulate adult neurogenesis at two different levels increasing both proliferation of neuronal progenitors in the germinative regions and integration of newborn neurons into functional circuits. This dual mechanism provides fresh neurons that can be involved in critical activities for the individual fitness, that is, the processing of social stimuli driving the parental behavior and partner recognition

An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization

Muscle synergy analysis is a useful tool for the evaluation of the motor control strategies and for the quantification of motor performance. Among the parameters that can be extracted, most of the information is included in the rank of the modular control model (i.e. the number of muscle synergies that can be used to describe the overall muscle coordination). Even though different criteria have been proposed in literature, an objective criterion for the model order selection is needed to improve reliability and repeatability of MSA results. In this paper, we propose an Akaike Information Criterion (AIC)-based method for model order selection when extracting muscle synergies via the original Gaussian Non-Negative Matrix Factorization algorithm. The traditional AIC definition has been modified based on a correction of the likelihood term, which includes signal dependent noise on the neural commands, and a Discrete Wavelet decomposition method for the proper estimation of the number of degrees of freedom of the model, reduced on a synergy-by-synergy and event-by-event basis. We tested the performance of our method in comparison with the most widespread ones, proving that our criterion is able to yield good and stable performance in selecting the correct model order in simulated EMG data. We further evaluated the performance of our AIC-based technique on two distinct experimental datasets confirming the results obtained with the synthetic signals, with performances that are stable and independent from the nature of the analysed task, from the signal quality and from the subjective EMG pre-processing steps