In this paper, we present neuro-robotics models with a deep artificial neural network capable of generating finger counting positions and number estimation. We first train the model in an unsupervised manner where each layer is treated as a Restricted Boltzmann Machine or an autoencoder. Such a model is further trained in a supervised way. This type of pre-training is tested on our baseline model and two methods of pre-training are compared. The network is extended to produce finger counting positions. The performance in number estimation of such an extended model is evaluated. We test the hypothesis if the subitizing process can be obtained by one single model used also for estimation of higher numerosities. The results confirm the importance of unsupervised training in our enumeration task and show some similarities to human behaviour in the case of subitizing.

To dance is human; people of all ages and levels of motor ability express movements in response to music. Professional dancers exert a great deal of creativity and energy toward developing their skills and different styles of dance. How dancers move in beautiful and sometimes unexpected ways can delight, and the synchrony between dancers moving together can be entrancing. To us as a neuroscientist and biomechanist (Lena), and a rehabilitation scientist and dancer (Madeleine), understanding the complexities of motor skill in a ballet move, or the physical language of coordination in partner dance, is an inspiring and daunting challenge. Understanding how dancers move has important real-world implications, too. In our work, we're studying gait and balance in different populations, as well as how holding hands – such as in partner dance – can actually help people walk and balance better.

To dance is human; people of all ages and levels of motor ability express movements in response to music. Professional dancers exert a great deal of creativity and energy toward developing their skills and different styles of dance. How dancers move in beautiful and sometimes unexpected ways can delight, and the synchrony between dancers moving together can be entrancing. To us as a neuroscientist and biomechanist (Lena), and a rehabilitation scientist and dancer (Madeleine), understanding the complexities of motor skill in a ballet move, or the physical language of coordination in partner dance, is an inspiring and daunting challenge. Understanding how dancers move has important real-world implications, too.

To us as a neuroscientist and biomechanist ( Lena), and a rehabilitation scientist and dancer ( Madeleine), understanding the complexities of motor skill in a ballet move, or the physical language of coordination in partner dance, is an inspiring and daunting challenge. Adapted tango rehabilitation class improves gait and balance in people with Parkinson's disease. Lucas McKay, an assistant professor in Biomedical Engineering at Emory specializing in mechanisms of balance impairment in Parkinson's disease, showed that participants improved muscle activity for balance after adapted tango. That is, as they practiced their tango dancing skills, they developed motor modules that also helped them walk and balance in everyday situations.

Motor control depends on sensory feedback in multiple modalities with different latencies. In this paper we consider within the framework of reinforcement learning how different sensory modalities can be combined and selected for real-time, optimal movement control. We propose an actor-critic architecture with multiple modules, whose output are combined using a softmax function. We tested our architecture in a simulation of a sequential reaching task. Reaching was initially guided by visual feedback with a long latency. Our learning scheme allowed the agent to utilize the somatosensory feedback with shorter latency when the hand is near the experienced trajectory. In simulations with different latencies for visual and somatosensory feedback, we found that the agent depended more on feedback with shorter latency.

Motor control depends on sensory feedback in multiple modalities with different latencies. In this paper we consider within the framework of reinforcement learning how different sensory modalities can be combined and selected for real-time, optimal movement control. We propose an actor-critic architecture with multiple modules, whose output are combined using a softmax function. We tested our architecture in a simulation of a sequential reaching task. Reaching was initially guided by visual feedback with a long latency. Our learning scheme allowed the agent to utilize the somatosensory feedback with shorter latency when the hand is near the experienced trajectory. In simulations with different latencies for visual and somatosensory feedback, we found that the agent depended more on feedback with shorter latency.

We tested our architecture in a simulation of a sequential reaching task.