Phonotaxis is the ability to orient towards or away from sound sources. Crickets can locate conspecifics by phonotaxis to the calling (mating) song they produce, and can evade bats by negative phonotaxis from echolocation calls. The behaviour and underlying physiology have been studied in some depth, and the auditory system solves this complex problem in a unique manner. Experiments conducted on a simulation model of the system indicated that the mechanism output a directional signal to sounds ahead at calling song frequency and to sounds behind at echolocation frequencies. We suggest that this combination of responses helps simplify later processing in the cricket. To further explore this result, an analogue, very large scale integrated (aVLSI) circuit model of the mechanism was designed and built; results from testing this agreed with the simulation. The aVLSI circuit was used to test a further hypothesis about the potential advantages of the positioning of the acoustic inputs for sound localisation during walking. There was no clear advantage to the directionality of the system in their location. The aVLSI circuitry is now being extended to use on a robot along with previously modelled neural circuitry to better understand the complete sensorimotor pathway.
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http://dx.doi.org/10.1016/j.biosystems.2006.09.027 | DOI Listing |
Neurosci J
May 2016
Dayalbagh Educational Institute, Dayalbagh, Agra 282005, India.
Stereopsis or depth perception is a critical aspect of information processing in the brain and is computed from the positional shift or disparity between the images seen by the two eyes. Various algorithms and their hardware implementation that compute disparity in real time have been proposed; however, most of them compute disparity through complex mathematical calculations that are difficult to realize in hardware and are biologically unrealistic. The brain presumably uses simpler methods to extract depth information from the environment and hence newer methodologies that could perform stereopsis with brain like elegance need to be explored.
View Article and Find Full Text PDFFront Neurosci
February 2015
Bioinspired VLSI Circuits and Systems Group, Department of Bioengineering, Imperial College London London, UK.
The field of neuromorphic silicon synapse circuits is revisited and a parsimonious mathematical framework able to describe the dynamics of this class of log-domain circuits in the aggregate and in a systematic manner is proposed. Starting from the Bernoulli Cell Formalism (BCF), originally formulated for the modular synthesis and analysis of externally linear, time-invariant logarithmic filters, and by means of the identification of new types of Bernoulli Cell (BC) operators presented here, a generalized formalism (GBCF) is established. The expanded formalism covers two new possible and practical combinations of a MOS transistor (MOST) and a linear capacitor.
View Article and Find Full Text PDFIEEE Trans Biomed Circuits Syst
June 2013
Institute of Neuroinformatics, University of Zürich and ETH Zürich, CH-8057 Zürich, Switzerland.
Capturing the functionality of active dendritic processing into abstract mathematical models will help us to understand the role of complex biophysical neurons in neuronal computation and to build future useful neuromorphic analog Very Large Scale Integrated (aVLSI) neuronal devices. Previous work based on an aVLSI multi-compartmental neuron model demonstrates that the compartmental response in the presence of either of two widely studied classes of active mechanisms, is a nonlinear sigmoidal function of the degree of either input temporal synchrony OR input clustering level. Using the same silicon model, this work expounds the interaction between both active mechanisms in a compartment receiving input patterns of varying temporal AND spatial clustering structure and demonstrates that this compartmental response can be captured by a combined sigmoid and radial-basis function over both input dimensions.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
August 2012
Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Spike-timing-dependent plasticity (STDP) is the ability of a synapse to increase or decrease its efficacy in response to specific temporal pairing of pre- and post-synaptic activities. It is widely believed that such activity-dependent long-term changes in synaptic connection strength underlie the brain's capacity of learning and memory. However, current phenomenological models of STDP fail to reproduce classical forms of synaptic plasticity that are based on stimulus frequency (BCM rule) instead of timing (STDP rule).
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
March 2011
Qualcomm Incorporated, San Diego, CA 92121, USA.
We present a circuit architecture for compact analog VLSI implementation of the Izhikevich neuron model, which efficiently describes a wide variety of neuron spiking and bursting dynamics using two state variables and four adjustable parameters. Log-domain circuit design utilizing MOS transistors in subthreshold results in high energy efficiency, with less than 1pJ of energy consumed per spike. We also discuss the effects of parameter variations on the dynamics of the equations, and present simulation results that replicate several types of neural dynamics.
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