Creative adaptations: Integrating Feldenkrais principles in contemporary dance technique to facilitate the transition into tertiary dance education

As conservatoire-style dance teaching has traditionally utilised a hierarchical approach through which the student must conform to the ideal requirements of the conventional technique, current discourse is beginning to question how dance training can develop technical acuity without stifling students' ability to engage creatively. In recent years, there has been growing interest in the field of somatics and its relationship to tertiary dance training due to the understanding that this approach supports creative autonomy by radically repositioning the student's relationship to embodied learning, skill acquisition, enquiry and performance. This research addresses an observable disjuncture between the skills of dancers graduating from tertiary training and Australian dance industry needs, which increasingly demand the co-creative input of the dancer in choreographic practice. Drawing from Action Research, this paper will discuss a project which introduces somatic learning approaches, primarily from Feldenkrais Method and Hanna Somatics, to first-year dance students in their transition into tertiary education. This paper acknowledges previous research undertaken, most specifically the Somdance Manual by the University of Western Sydney, while directing focus to the first-year student transition from private dance studio training into the pre-professional arena.

From “somatic scandals” to “a constant potential for violence”? The culture of dissection, brain-based learning, and the rewriting/rewiring of “the child”

This article analyzes the “messy and numberless beginnings” of the hope placed upon neurological foundationalism to provide a solution to the “problem” of differences between students and to the achievement of educational goals. Rather than arguing for or against educational neuroscience, the article moves through five levels to examine the conditions of possibility for subscribing to the brain as a causal organological locus of learning.

Learning by the Dendritic Prediction of Somatic Spiking

Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.

Enhancing Student Learning Experiences in Sport Psychology Modules Using Practical Activities

The University of Worcester states in its most recent strategic plan (2013 – 2018) a set of enduring values that guide and direct the activities of the institution. The first listed, and perhaps the most important value is the striving to be “an outstanding university at which to be a student”. This is further supplemented by values such as “to inspire our students to reach their full potential through excellent, innovative teaching, scholarship and research” (University of Worcester 2013: p.4). One of the many ways in which the institution strives to provide this outstanding educational experience is through regular engagement, both formal and informal, with students at a number of points in each semester. Regular experiences of collating formal and informal feedback has led to the identification of a common theme amongst Higher National Diploma (HND) students in the Institute of Sport and Exercise Sciences (ISES), where they consistently request ‘more practicals’. The ISES modules however are designed to incorporate a high degree of interaction, practical activities and tasks. This is especially important for those studying at HND level as research suggests differences in learning preferences exist when compared to undergraduate students, the former preferring a more tactile style of learning (Peters et al. 2008). Using an introductory Sport Psychology HND module as an example, practical activities and tasks are fully embedded in the taught sessions to enable contextual links to be made between the learning outcomes and their subsequent use. Examples of these include: a. interviewing athletes to produce a performance profile (Butler & Hardy 1992); b. completing psychometric instruments such as the Competitive State Anxiety Inventory-2 (CSAI-2) to measure competitive anxiety in sport (Martens et al. 1990) and demonstrate data collection and construct measurement; c. performing relaxation interventions on the students to demonstrate how specific techniques (in this instance, decreasing somatic anxiety) might work in practice; d. demonstrating how observational learning facilitates skill acquisition by creating experimental conditions that the students participate in, in teaching a new skill. Nevertheless owing to the students' previously stated on-going requests for more practical activities, it became evident that assumptions about what students consider an effective means of experiential or active learning in the context of sport-related disciplines of study needed to be investigated. This is where the opportunity to undertake an action research project arose, this being a practical method commonly employed in pedagogical enquiry to aid reflection on teaching and assessment practice for the purposes of working towards continuous improvement.

Reinforcement learning in dendritic structures

The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.

Reinforcement learning in dendritic structures

The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.