V. Srinivasa Chakravarthy

An oscillatory neuromotor model of handwriting generation

A neuromotor model of handwritten stroke generation, in which stroke velocities are expressed as a Fourier-style decomposition of oscillatory neural activities, is presented. The neural network architecture consists of an input or stroke-selection layer, an oscillatory layer, and the output layer where stroke velocities are estimated. A separate timing network prepares the network’s initial state, which is crucial for accurate stroke generation. Neurobiological significance of this preparation, and a possible mapping of our architecture onto human motor system is suggested. Interaction between timing network and oscillatory layer closely resembles interaction between Basal Ganglia and Supplementary Motor Area in the brain.

Teaching a humanoid robot to draw `Shapes'

The core cognitive ability to perceive and synthesize ‘shapes’ underlies all our basic interactions with the world, be it shaping one’s fingers to grasp a ball or shaping one’s body while imitating a dance. In this article, we describe our attempts to understand this multifaceted problem by creating a primitive shape perception/synthesis system for the baby humanoid iCub. We specifically deal with the scenario of iCub gradually learning to draw or scribble shapes of gradually increasing complexity, after observing a demonstration by a teacher, by using a series of self evaluations of its performance. Learning to imitate a demonstrated human movement (specifically, visually observed end-effector trajectories of a teacher) can be considered as a special case of the proposed computational machinery. This architecture is based on a loop of transformations that express the embodiment of the mechanism but, at the same time, are characterized by scale invariance and motor equivalence. The following transformations are integrated in the loop: (a) Characterizing in a compact, abstract way the ‘shape’ of a demonstrated trajectory using a finite set of critical points, derived using catastrophe theory: Abstract Visual Program (AVP); (b) Transforming the AVP into a Concrete Motor Goal (CMG) in iCub’s egocentric space; (c) Learning to synthesize a continuous virtual trajectory similar to the demonstrated shape using the discrete set of critical points defined in CMG; (d) Using the virtual trajectory as an attractor for iCub’s internal body model, implemented by the Passive Motion Paradigm which includes a forward and an inverse motor model; (e) Forming an Abstract Motor Program (AMP) by deriving the ‘shape’ of the self generated movement (forward model output) using the same technique employed for creating the AVP; (f) Comparing the AVP and AMP in order to generate an internal performance score and hence closing the learning loop. The resulting computational framework further combines three crucial streams of learning: (1) motor babbling (self exploration), (2) imitative action learning (social interaction) and (3) mental simulation, to give rise to sensorimotor knowledge that is endowed with seamless compositionality, generalization capability and body-effectors/task independence. The robustness of the computational architecture is demonstrated by means of several experimental trials of gradually increasing complexity using a state of the art humanoid platform.

Exploring the cognitive and motor functions of the basal ganglia: an integrative review of computational cognitive neuroscience models.

Many computational models of the basal ganglia (BG) have been proposed over the past twenty-five years. While computational neuroscience models have focused on closely matching the neurobiology of the BG, computational cognitive neuroscience (CCN) models have focused on how the BG can be used to implement cognitive and motor functions. This review article focuses on CCN models of the BG and how they use the neuroanatomy of the BG to account for cognitive and motor functions such as categorization, instrumental conditioning, probabilistic learning, working memory, sequence learning, automaticity, reaching, handwriting, and eye saccades. A total of 19 BG models accounting for one or more of these functions are reviewed and compared. The review concludes with a discussion of the limitations of existing CCN models of the BG and prescriptions for future modeling, including the need for computational models of the BG that can simultaneously account for cognitive and motor functions, and the need for a more complete specification of the role of the BG in behavioral functions.

The shape of handwritten characters

A handwritten character magically survives serious distortions in size, orientation and even structure, justifying perhaps its Sanskrit name--Aksharam, the undecaying. Several traditional approaches model characters in terms of certain shape features like line crossings, T-junctions etc. But there is no sanctity in the choice of these features--which may be specific to a script--nor is there a limit to their number. We address the general problem of defining the shape of a 2D line diagram, with character as a significant special case. To this end we develop a framework based on a branch of mathematics known as the Catastrophe theory. A small set of 11 shape features is derived systematically from our framework. The 11 features are found in several of worlds scripts and may in fact be universal. More complex shapes break down to the above 11 in handwritten scripts. We discuss how our model can be applied to on-line character recognition from pen-based devices.

Understanding parkinsonian handwriting through a computational model of basal ganglia

Handwriting in Parkinsons disease (PD) is typically characterized by micrographia, jagged line contour, and unusual fluctuations in pen tip velocity. Although PD handwriting features have been used for diagnostics, they are not based on a signaling model of basal ganglia (BG). In this letter, we present a computational model of handwriting generation that highlights the role of BG. When PD conditions like reduced dopamine and altered dynamics of the subthalamic nucleus and globus pallidus externa subsystems are simulated, the handwriting produced by the model manifested characteristic PD handwriting distortions like micrographia and velocity fluctuations. Our approach to PD modeling is in tune with the perspective that PD is a dynamic disease.

Spatiostructural Features for Recognition of Online Handwritten Characters in Devanagari and Tamil Scripts

The spatiostructural features proposed for recognition of online handwritten characters refer to offline-like features that convey information about both the positional and structural (shape) characteristics of the handwriting unit. This paper demonstrates the effectiveness of representing an online handwritten stroke using spatiostructural features, as indicated by its effect on the stroke classification accuracy by a Support Vector Machine (SVM) based classifier. The study has been done on two major Indian writing systems, Devanagari and Tamil. The importance of localization information of the structural features and handling of translational variance is studied using appropriate approaches to zoning the handwritten character.

Does neighborhood floral display matter? Fruit set in carpenter bee-pollinated heterophragma quadriloculare and beetle-pollinated lasiosiphon eriocephalus

Fruit set is pollen-limited in the self-incompatible tree Heterophragmrna quadriloculare (Bignoniaceae), pollinated by long-distance flying carpenter bees, and in the self-compatible shrub Lasiosiphon eriocephalus (Thymeleaceae), pollinated by weak-flying, sedentary beetles. We studied a single H. quadriloculare population over high and low flowering years and found no difference in bee visitation rates between these years. For H. quadriloculare, neighborhood floral display did not make a significant contribution to reproductive success. We investigated dense and sparse L. eriocephalus populations in the same year. In the low density L. eriocephalus population, individual floral displays were higher than in the dense population, yet reproductive success was low, indicating that plant isolation was a major factor influencing fruit set. This result was due to the extremely low number of beetles per plant and per flower in this population. In the dense L. eriocephalus population, although the displays of individual neighbors were smaller and plants were closer, neighborhood floral display did not contribute significantly to reproductive success, whereas the effect of individual floral display was ambiguous. Species with self-incompatible rather than self-compatible breeding systems are expected to experience neighborhood effects on reproductive success; however, at the spatial scale and floral display levels of plants in this study, only individual floral display affected fruit set in H. quadriloculare, whereas neither individual nor neighborhood display influenced fruit set in L. eriocephalus. Therefore, pollinator type, pollinator behavior, and plant and population isolation, rather than breeding system alone, will determine if neighborhood floral display affects fruit set.

A spiking Basal Ganglia model of synchrony, exploration and decision making

To make an optimal decision we need to weigh all the available options, compare them with the current goal, and choose the most rewarding one. Depending on the situation an optimal decision could be to either “explore” or “exploit” or “not to take any action” for which the Basal Ganglia (BG) is considered to be a key neural substrate. In an attempt to expand this classical picture of BG function, we had earlier hypothesized that the Indirect Pathway (IP) of the BG could be the subcortical substrate for exploration. In this study we build a spiking network model to relate exploration to synchrony levels in the BG (which are a neural marker for tremor in Parkinsons disease). Key BG nuclei such as the Sub Thalamic Nucleus (STN), Globus Pallidus externus (GPe) and Globus Pallidus internus (GPi) were modeled as Izhikevich spiking neurons whereas the Striatal output was modeled as Poisson spikes. The model is cast in reinforcement learning framework with the dopamine signal representing reward prediction error. We apply the model to two decision making tasks: a binary action selection task (similar to one used by Humphries et al., 2006) and an n-armed bandit task (Bourdaud et al., 2008). The model shows that exploration levels could be controlled by STNs lateral connection strength which also influenced the synchrony levels in the STN-GPe circuit. An increase in STNs lateral strength led to a decrease in exploration which can be thought as the possible explanation for reduced exploratory levels in Parkinsons patients. Our simulations also show that on complete removal of IP, the model exhibits only Go and No-Go behaviors, thereby demonstrating the crucial role of IP in exploration. Our model provides a unified account for synchronization, action section, and explorative behavior.

Modular Approach to Recognition of Strokes in Telugu Script

In this paper, we address some issues in developing an online handwritten character recognition(HCR) system for an Indian language script, Telugu. The number of characters in this script is estimated to be around 5000. A character in this script is written as a sequence of strokes. The set of strokes in Telugu consists of 253 unique strokes. As the similarity among several strokes is high, we propose a modular approach for recognition of strokes. Based on the relative position of a stroke in a character, the stroke set has been divided into three subsets, namely, baseline strokes, bottom strokes and top strokes. Classifiers for the different subsets of strokes are built using support vector machines(SVMs). We study the performance of the classifiers for subsets of strokes and propose methods to improve their performance. A comparative study using hidden Markov models(HMMs) shows that the SVM based approach gives a significantly better performance.

An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning

Although empirical and neural studies show that serotonin (5HT) plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL)-framework. The model depicts the roles of dopamine (DA) and serotonin (5HT) in Basal Ganglia (BG). In this model, the DA signal is represented by the temporal difference error (δ), while the 5HT signal is represented by a parameter (α) that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: (1) Risk-sensitive decision making, where 5HT controls risk assessment, (2) Temporal reward prediction, where 5HT controls time-scale of reward prediction, and (3) Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG.