Animals for surviving have developed cognitive abilities allowing them an abstract representation of the environment.
This internal representation (IR) may contain a huge amount of information concerning the evolution and interactions of
the animal and its surroundings. The temporal information is needed for IRs of dynamic environments and is one of the
most subtle points in its implementation as the information needed to generate the IR may eventually increase
dramatically. Some recent studies have proposed the compaction of the spatiotemporal information into only space,
leading to a stable structure suitable to be the base for complex cognitive processes in what has been called Compact
Internal Representation (CIR). The Compact Internal Representation is especially suited to be implemented in
autonomous robots as it provides global strategies for the interaction with real environments. This paper describes an
FPGA implementation of a Causal Neural Network based on a modified FitzHugh-Nagumo neuron to generate a
Compact Internal Representation of dynamic environments for roving robots, developed under the framework of SPARK
and SPARK II European project, to avoid dynamic and static obstacles.
Animals for surviving have developed cognitive abilities allowing them an abstract
representation of the environment. This Internal Representation (IR) could contain a huge
amount of information concerning the evolution and interactions of the elements in their
surroundings. The complexity of this information should be enough to ensure the maximum
fidelity in the representation of those aspects of the environment critical for the agent, but not so
high to prevent the management of the IR in terms of neural processes, i.e. storing, retrieving,
etc. One of the most subtle points is the inclusion of temporal information, necessary in IRs of
dynamic environments. This temporal information basically introduces the environmental
information for each moment, so the information required to generate the IR would eventually
be increased dramatically. The inclusion of this temporal information in biological neural
processes remains an open question. In this work we propose a new IR, the Compact Internal
Representation (CIR), based on the compaction of spatiotemporal information into only space,
leading to a stable structure (with no temporal dimension) suitable to be the base for complex
cognitive processes, as memory or learning. The Compact Internal Representation is especially
appropriate for be implemented in autonomous robots because it provides global strategies for
the interaction with real environments (roving robots, manipulators, etc.). This paper presents
the mathematical basis of CIR hardware implementation in the context of navigation in dynamic
environments. The aim of such implementation is the obtaining of free-collision trajectories
under the requirements of an optimal performance by means of a fast and accurate process.
We propose a novel method for automatic classification of neural spikes recorded extracellularly. The method
makes use of the wavelet multiscale spike decomposition and identification of the most discriminative features
by artificial neural networks. We demonstrate the efficiency of the method on semi-simulated data and using
in-vivo recordings. Advantage of the proposed approach over existing techniques is shown.
We show that robustness of sorting of neural spikes using the wavelet transform depends strongly on the statistics
of experimental noise and the characteristic time scales of spike waveforms. Incorporating adaptive filtering
of the extracellular potential into the wavelet sorting algorithm we propose a novel method, the Parametric
Wavelet sorting with Advanced Filtering (PWAF), whose classification error approaches the theoretical minimum.
Efficiency of the proposed technique is proved with both simulated and real electrophysiological recordings.
KEYWORDS: Sensors, Signal to noise ratio, Robotic systems, Data processing, Environmental sensing, Space robots, Dynamical systems, Signal processing, Sensory processes, Bacteria
Common design of a robot searching for a target emitting sensory stimulus (e.g. odor or sound) makes use of
the gradient of the sensory intensity. However, the intensity may decay rapidly with distance to the source,
then weak signal-to-noise ratio strongly limits the maximal distance at which the robot performance is still
acceptable. We propose a simple deterministic platform for investigation of the searching problem in an uncertain
environment with low signal to noise ratio. The robot sensory layer is given by a differential sensor capable of
comparing the stimulus intensity between two consecutive steps. The sensory output feeds the motor layer
through two parallel sensory-motor pathways. The first "reflex" pathway implements the gradient strategy,
while the second "integrating" pathway processes sensory information by discovering statistical dependences
and eventually correcting the results of the first fast pathway. We show that such parallel sensory information
processing allows greatly improve the robot performance outside of the robot safe area with high signal to noise ratio.
We study mechanisms of information processing in the principalis (Pr5), oralis (Sp5o) and interpolaris (Sp5i)
nuclei of the trigeminal sensory complex of the rat under whisker stimulation by short air puffs. After the
standard electrophysiological description of the neural spiking activity we apply a novel wavelet based method
quantifying the structural stability of firing patterns evoked by a periodic whisker stimulation. We show that
the response stability depends on the puff duration delivered to the vibrissae and differs among the analyzed
nuclei. Pr5 and Sp5i exhibit the maximal stability to an intermediate stimulus duration, whereas Sp5o shows
"preference" for short stimuli.
An experimental study has been carried out on a noisy dissipative-driven ring lattice of units coupled via Morse potentials. An electronic circuit mimicking the lattice dynamics and noise sources is used. We show that inclusion of long range attractive forces facilitates clustering (at variance with the repulsive Toda ring) and van der Waals-like transition phenomena.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.