George L. Gerstein - US grants

We propose to study cortical organization and reorganization at the level of neuronal networks and assemblies, using both experimental and theoretical methods. The experimental vehicle will be rat somatosensory cortex subjected to local electrical stimulation; this forces rapid changes in somatic map boundaries. During such changes we propose to make extracellular region. These measurements and their subsequent interpretation in terms of neuronal assembly processes will rest on recording technologies and analytic mathematics that have largely been developed by our laboratory over the last decade. The proposed theoretical work will examine computer simulations of neuronal networks arranged to reproduce the changes in map magnification and boundaries which underlie the experimental work. As information about "effective connectivity" and neuronal assembly properties emerges from the experiments, these new constraints will be incorporated into the ongoing modeling. The models will analysis techniques, and are likely to suggest additional experimental measurements. The overall project should give considerable insight into actual and possible mechanisms for cortical modularity and reorganization; we will here have, for the first time, direct experimental access to the details of the process by which the brain reallocates its computational resources.

We propose to study cortical organization and reorganization at the level of neuronal networks and assemblies, using both experimental and theoretical methods. The experimental vehicle will be rat auditory cortex subjected to local electrical stimulation; in analogy to work done by others as well as in our laboratory on somatosensory cortex, this procedure should force changes in the auditory frequency and ear dominance 'map' arrangement. During such changes we propose to make extracellular recordings simultaneously and separably from 10-30 neurons in the reorganizing cortical region. These measurements and their subsequent interpretation in terms of neuronal assembly processes will rest on recording technologies and analytic mathematics that have largely been developed by our laboratory over the last decade. The proposed theoretical work will examine computer simulations of neuronal networks arranged to reproduce the changes in map magnification and boundaries which underlie the experimental work. As information about 'effective connectivity' and neuronal assembly properties emerges from the experiments. these now constraints will be incorporated into the ongoing modeling. The models will be studied on both a gross level, and on a 'microscopic' scale, using our standard spike train analysis techniques, and are likely to suggest additional experimental measurements. The overall project should give considerable insight into actual and possible mechanisms for cortical modularity and reorganization; we will here have direct experimental access to the details of the process by which the brain allocates its computational resources. Comparison of the auditory work to the analogous somatosensory project will test the extent to which the reorganization processes are universal across sensory systems, or conversely whether they will prove to be unique.

We propose to study cortical organization and reorganization at the level of neuronal networks and assemblies, using both experimental and theoretical methods. The experimental vehicle will be rat somatosensory cortex subjected to local electrical stimulation; this forces rapid changes in somatic map boundaries. During such changes we propose to make extracellular region. These measurements and their subsequent interpretation in terms of neuronal assembly processes will rest on recording technologies and analytic mathematics that have largely been developed by our laboratory over the last decade. The proposed theoretical work will examine computer simulations of neuronal networks arranged to reproduce the changes in map magnification and boundaries which underlie the experimental work. As information about "effective connectivity" and neuronal assembly properties emerges from the experiments, these new constraints will be incorporated into the ongoing modeling. The models will analysis techniques, and are likely to suggest additional experimental measurements. The overall project should give considerable insight into actual and possible mechanisms for cortical modularity and reorganization; we will here have, for the first time, direct experimental access to the details of the process by which the brain reallocates its computational resources.

9873463
Finkel
We Propose an approach to the study of intelligent systems based on the modeling and construction of neuromorphic knowledge systems. Neuroinorphic systems offer a platform for integrating insights from mathematics, neurobiology, and Computer simulation, and represent a new type of neural-based computing. Our approach arises from the premise that the traditional components of intelligence (including Sy requires a new technology; this technology will be provided by together as an integrated system. The study of such complex learning, recognition, and attention) are Properties of a common underlying neural structure, and that these compo ents are best studied neuromorphic systems engineering. Ncuromorphic VLSI chips stems and systems use subthreshold analog circuits to model dendritic computation and asynchronous digital circuits to model axonal communication . These systems can function autonomously or be interfaced with conventional computers. Neuromozphic systems offer the possibility of learning and recognition on real-time, real-world problems such as visual search and the recognition of biological motion.
Our approach is based on spike-based computation: use of the precise timing of ncuronal spikes for representation and computation. Spike-based computations underlie some of the most impressive behaviors in biology, from bat sonar to human vision. In particular, we will focus on neuronal synchronization and will develop models in which learning, attention, and recognition all operate through effects on synchronization. Such models require neural mechanisms for the detection of synchronization (Recognition), enhancement of synchronization (Learning), and dynamic modulation of synchronization (Attention).
We will use a team approach in which faculty and students work together on linked projects. The four investigators have expertise in complementary areas: neuromorphic chip design (Boahen), cellular biophysics and neural network simulation (Finkel), physiological data analysis and modeling (Gerstein), theoretical analysis of learning and spikebascd computation (Hopficid). Together we %ill investigate mechanisms for the control of synchronization based on changes in neuromodulation, on selective modification of
AMPA/NMDA conductance ratios, on temporallyasyminetric synaptic plasticity, and on synaptic delay and spike-frequency adaptation. These simulations @l be tied to analysis of synchronization in spike-train recordings from awake, behaving animals engaged in learning, attention, or recognition tasks. Analysis of physiological and psychophysical data will motivate development of theoretical models of spike-based computation. Most critically, development of a series of neuromorphic chips will be carried out as a means of scaling up the simulations to systems containing over 200,000 spiking neurons each making thousands of connections. To facilitate development and sharing of ideas, we will develop a common simulation environment for analysis of spiking neurons that will allow an interface between large-scale network simulation, neuromorphic chip design, and analysis and prediction of physiological recordings. This enviroranent will be made available (via the Web) for widcr use by physiologists and modelers, and as an educational tool.
Our current retina-based neuromorphic chips (Boahen, 1998) outperform CCD technology as a result of ncurally-derived mechanisms. These chips incorporate biological mechanisms for local gain control and motion detection. Prototype systems of interconnected spikegenerating chips will allow real-time feedback control of connectivity. We outline a plan for interfacing cortically-based ncuromorphic systems with high-speed multiprocessors to allow real-time attention, recognition, and learning on continuous video input streams. Recent ncurophysiological studies suggest that neuronal responses in alert animals viewing rcal-iiorld environments differ radically from responses to simplified stimuli. Such results argue for the study of neural-based systems interacting with real-world environments.
The outcome of these studies will be tools, technology and insights that provide a new domain of interaction between neuroscientists, computer scientists and mathematicians and engineers. They will yield insight into the spike-based computations underlying intelligent behavior. The technology developed will have immediate application to robotics and intelligent neural prostheses.