6 research outputs found
Virtual Neurorobotics (VNR) to Accelerate Development of Plausible Neuromorphic Brain Architectures
Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system. More recently the field of social robotics has been advanced to capture the important dynamics of human cognition and interaction. An overarching societal goal of this research is to incorporate the resultant knowledge about intelligence into technology for prosthetic, assistive, security, and decision support applications. However, despite many decades of investment in learning and classification systems, this paradigm has yet to yield truly “intelligent” systems. For this reason, many investigators are now attempting to incorporate more realistic neuromorphic properties into machine learning systems, encouraged by over two decades of neuroscience research that has provided parameters that characterize the brain's interdependent genomic, proteomic, metabolomic, anatomic, and electrophysiological networks. Given the complexity of neural systems, developing tenable models to capture the essence of natural intelligence for real-time application requires that we discriminate features underlying information processing and intrinsic motivation from those reflecting biological constraints (such as maintaining structural integrity and transporting metabolic products). We propose herein a conceptual framework and an iterative method of virtual neurorobotics (VNR) intended to rapidly forward-engineer and test progressively more complex putative neuromorphic brain prototypes for their ability to support intrinsically intelligent, intentional interaction with humans. The VNR system is based on the viewpoint that a truly intelligent system must be driven by emotion rather than programmed tasking, incorporating intrinsic motivation and intentionality. We report pilot results of a closed-loop, real-time interactive VNR system with a spiking neural brain, and provide a video demonstration as online supplemental material
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
Experimentally verified reduced models of neocortical pyramidal cells
Reduced neuron models are essential tools in computational neuroscience to aid understanding from the single cell to network level. In this thesis I use these models to address two key challenges: introducing experimentally verifi�ed heterogeneity into neocortical network models, and furthering understanding of post-spike refractory mechanisms.
Neocortical network models are increasingly including cell class diversity. However, within these classes significant heterogeneity is displayed, an aspect often neglected in modelling studies due to the lack of empirical constraints on the variance and covariance of neuronal parameters. To address this I quantified the response of pyramidal cells in neocortical layers 2/3-5 to square-pulse and naturalistic current stimuli. I used standard and dynamic I-V protocols to measure electrophysiological parameters, a byproduct of which is the straightforward extraction of reduced neuron models. I examined the between- and within-class heterogeneity, culminating in an algorithm to generate populations of exponential integrate-and-�re (EIF) neurons adhering to the empirical marginal distributions and covariance structure. This provides a novel tool for investigating heterogeneity in neocortical network models.
Spike threshold is dynamic and, on spike initiation, displays a jump and subsequent exponential decay back to baseline. I examine extensions to the EIF model that include these dynamics, fi�nding that a simple renewal process model well captures the cell's response. It has been previously noted that a two-variable EIF model describing the voltage and threshold dynamics can be reduced to a single-variable system when the membrane and threshold time constants are similar. I examine the response properties of networks of these models by taking a perturbative approach to solving the corresponding Fokker-Planck equation, �finding the results in agreement with simulations over the physiological range of the membrane to threshold time constant ratio. Finally, I found that the observed threshold dynamics are not fully described by the inclusion of slow sodium-channel inactivation
Serotonergic modulation of the ventral pallidum by 5HT1A, 5HT5A, 5HT7 AND 5HT2C receptors
Introduction: Serotonin's involvement in reward processing is controversial. The large number of serotonin receptor
sub-types and their individual and unique contributions have been difficult to dissect out, yet understanding how
specific serotonin receptor sub-types contribute to its effects on areas associated with reward processing is an
essential step.
Methods: The current study used multi-electrode arrays and acute slice preparations to examine the effects of
serotonin on ventral pallidum (VP) neurons.
Approach for statistical analysis: extracellular recordings were spike sorted using template matching and principal
components analysis, Consecutive inter-spike intervals were then compared over periods of 1200 seconds for each
treatment condition using a student’s t test.
Results and conclusions: Our data suggests that excitatory responses to serotonin application are pre-synaptic in
origin as blocking synaptic transmission with low-calcium aCSF abolished these responses. Our data also suggests
that 5HT1a, 5HT5a and 5HT7 receptors contribute to this effect, potentially forming an oligomeric complex, as 5HT1a
antagonists completely abolished excitatory responses to serotonin application, while 5HT5a and 5HT7 only reduced
the magnitude of excitatory responses to serotonin. 5HT2c receptors were the only serotonin receptor sub-type
tested that elicited inhibitory responses to serotonin application in the VP. These findings, combined with our
previous data outlining the mechanisms underpinning dopamine's effects in the VP, provide key information, which
will allow future research to fully examine the interplay between serotonin and dopamine in the VP. Investigation of
dopamine and serotonins interaction may provide vital insights into our understanding of the VP's involvement in
reward processing. It may also contribute to our understanding of how drugs of abuse, such as cocaine, may hijack
these mechanisms in the VP resulting in sensitization to drugs of abuse