- Computational role of neural feedback [3,4] describes theoretical results which show that feedback endows standard models for neural circuits with the capability to emulate arbitrary Turing machines. In fact, with a suitable feedback they can simulate any dynamical system, in particular any conceivable analog computer. Under realistic noise conditions the computational power of these circuits is necessarily reduced. It was demonstrated through computer simulations that feedback also provides a significant gain in computational power for quite detailed models of cortical microcircuits with in-vivo-like high levels of noise . In particular it enables generic cortical microcircuits to carry out computations that combine information from working memory and persistent internal states in real-time with new information from online input streams.
- Computational Motor Control [5,6] tries to explore how complex movements that take hundreds of milliseconds be generated by stereotypical neural microcircuits consisting of spiking neurons with a much faster dynamics? It was shown that linear readouts from generic neural microcircuit models can be trained to generate basic arm movements. Such movement generation is independent of the arm-model used and the type of feedbacks that the circuit receives. It was demonstrated by considering two different models of a two-jointed arm, a standard model from robotics and a standard model from biology, that each generate different kinds of feedback. Feedbacks that arrive with biologically realistic delays of 50-280 ms turn out to give rise to the best performance. If a feedback with such desirable delay is not available, the neural microcircuit model also achieves good performance if it uses internally generated estimates of such feedback. Existing methods for movement generation in robotics that take the particular dynamics of sensors and actuators into account (``embodiment of motor systems'') are taken one step further with this approach, which provides methods for also using the ``embodiment of motion generation circuitry'', i.e., the inherent dynamics and spatial structure of neural circuits, for the generation of movements.
- Working memory and decision making [2] processes are modeled using generic neural microcircuits with feedback. Classical behavioral experiments to study working memory typically involve three phases. First the subject receives a stimulus, then holds it in the working memory, and finally makes a decision by comparing it with another stimulus. A neurocomputational model using generic neural microcircuits with feedback was presented that integrates the three computational stages into a single unified framework. The architecture was tested using the two-interval discrimination and delayed-match-to-sample experimental paradigms as benchmarks.
- Goal-directed movement in presence of decisions [1] Decision making lies towards the end-stage of one of the most taxing problems organisms face: action selection in an uncertain world [8]. Interval discrimination task is a classical experimental paradigm that is employed to study working memory and decision making and typically involves four phases. First the subject receives a stimulus, then holds it in the working memory, then makes a decision by comparing it with another stimulus, and finally acts on this decision, usually by pressing one of the two buttons corresponding to the binary decision. A neurocomputational algorithm using generic neural microcircuits with feedback was presented that integrates the four computational stages into a single unified framework. The algorithm was tested using two-interval discrimination and delayed-match-to-sample experimental paradigms as benchmarks.
I am a computational neuroscientist, with my main areas of research interest being reservoir computing, computational motor control, computation with spiking neurons, neurocomputational basis of working memory and decision making, learning in biologically realistic circuits, machine learning, adaptive non-linear control and humanoid robots. My research during the last few years explored the role of neural feedback in enhancing the computational power of generic neural microcircuits for tasks involving motor control [1,5,6], working memory [1,2], decision making [1,2], and action selection in presence of decisions [1].
Research
My research is motivated by the desire to understand more about about the functioning of cortex, from the synaptic and neuronal level to that of systems and circuits. I believe that questions have to be asked at microscopic and macroscopic levels simultaneously to further our understanding of the biophysical mechanisms performed by cortical circuits that help the organism in sensori-motor control, decision-making, vision or any of the several other cortical functions. My recent research has touched the following topics:
Future work and goals
After investigating motor control and decision making from a theoretical and modeling perspective, now I want to work close to experimental research as I believe it would give me a broader and balanced scientific perspective. My goal is to create models and theory that reflect experimental realities, and to conduct experiments that help us in asking the right theoretical questions.
I intend to explore the effect of feedback and learning on diverse cortical functions e.g. motor control/imitation learning/vision. Another idea in infancy is to investigate the role of homeostasis mechanisms (e.g. synaptic homeostasis) in motor control (e.g. maintaining posture, maintaining tension in relaxed muscles).