THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.

Behavioral and neural correlates of perceived and imagined musical timbre

The generality of findings implicating secondary auditory areas in auditory imagery was tested by using a timbre imagery task with fMRI. Another aim was to test whether activity in supplementary motor area (SMA) seen in prior studies might have been related to subvocalization. Participants with moderate musical background were scanned while making similarity judgments about the timbre of heard or imagined musical instrument sounds. The critical control condition was a visual imagery task. The pattern of judgments in perceived and imagined conditions was similar, suggesting that perception and imagery access similar cognitive representations of timbre. As expected, judgments of heard timbres, relative to the visual imagery control, activated primary and secondary auditory areas with some right-sided asymmetry. Timbre imagery also activated secondary auditory areas relative to the visual imagery control, although less strongly, in accord with previous data. Significant overlap was observed in these regions between perceptual and imagery conditions. Because the visual control task resulted in deactivation of auditory areas relative to a silent baseline, we interpret the timbre imagery effect as a reversal of that deactivation. Despite the lack of an obvious subvocalization component to timbre imagery, some activity in SMA was observed, suggesting that SMA may have a more general role in imagery beyond any motor component.

Examining The Genetic, Social-Cognitive, And Neural Correlates Of Autism Spectrum Disorder Behaviors In Typically Developing Chi

Autism spectrum disorders (ASD) are pervasive developmental disorders that affect approximately 1 in 50 children (Blumberg et al., 2013). Due to the social nature of the deficits that characterize the disorders, many have classified them as disorders of social cognition, which is the process that individuals use in order to successfully interact with members of their own species (Frith & Frith, 2007). Previous research has typically neglected the spectrum nature of ASD in favor of a more categorical approach of ¿autistic¿ versus ¿non-autistic,¿ but the spectrum requires a more continuous approach. Thus, the present study sought to examine the genetic, social-cognitive, and neural correlates of ASD-like traits as well as the relationship between these dimensions in typically developing children. Parents and children completed several quantitative measures examining several areas of social-cognitive functioning, including theory of mind and social functioning, restricted/repetitive behaviors and interests, and adaptive/maladaptive functioning. Children were also asked to undergo an EEG and both parents and children contributed a saliva sample that was used to sequence four single nucleotide polymorphisms (SNPs) of the OXTR gene, rs1042778, rs53576, rs2254298, and rs237897. We successfully demonstrated a significant relationship between behavioral measures of social-cognition and differences in face perception via the N170. However, the directionality of these relationships varied based on the behavioral measure and particular N170 difference scores. We also found support for the associations between the G_G allelic combination of rs1042778 and the A_A and A_G allelic combinations of rs2254298 and increased ASD-like behavior with decreased social-cognitive functioning. In contrast, our results contradict previous findings with rs237897 and imply that individuals with the A_A and A_G genotypes are less similar to those with ASD and have higher social cognitive functioning than those with the G_G genotype. In conclusion, we have demonstrated the existence of ASD-like traits in typically developing children and have shown a link between behavioral, genetic, and neural correlates of social-cognition. These findings demonstrate the importance of considering autism as a spectrum disorder and provide support for the move to a more continuous approach to neurodevelopmental disorders.

Social Cognition and Neural Substrates of Face Perception: Implications for Neurodevelopmental and Neuropsychiatric Disorders

BACKGROUND: Social cognition is an important aspect of social behavior in humans. Social cognitive deficits are associated with neurodevelopmental and neuropsychiatric disorders. In this study we examine the neural substrates of social cognition and face processing in a group of healthy young adults to examine the neural substrates of social cognition. METHODS: Fifty-seven undergraduates completed a battery of social cognition tasks and were assessed with electroencephalography (EEG) during a face-perception task. A subset (N=22) were administered a face-perception task during functional magnetic resonance imaging. RESULTS: Variance in the N170 EEG was predicted by social attribution performance and by a quantitative measure of empathy. Neurally, face processing was more bilateral in females than in males. Variance in fMRI voxel count in the face-sensitive fusiform gyrus was predicted by quantitative measures of social behavior, including the Social Responsiveness Scale (SRS) and the Empathizing Quotient. CONCLUSIONS: When measured as a quantitative trait, social behaviors in typical and pathological populations share common neural pathways. The results highlight the importance of viewing neurodevelopmental and neuropsychiatric disorders as spectrum phenomena that may be informed by studies of the normal distribution of relevant traits in the general population. Copyright 2014 Elsevier B.V. All rights reserved.