2,152 research outputs found
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
An efficient automated parameter tuning framework for spiking neural networks
As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier
Towards a Reconfigurable Sense-and-Stimulate Neural Interface Generating Biphasic Interleaved Stimulus
Published versio
Stochastic Resonance of Ensemble Neurons for Transient Spike Trains: A Wavelet Analysis
By using the wavelet transformation (WT), we have analyzed the response of an
ensemble of (=1, 10, 100 and 500) Hodgkin-Huxley (HH) neurons to {\it
transient} -pulse spike trains () with independent Gaussian noises.
The cross-correlation between the input and output signals is expressed in
terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is
evaluated by using the {\it denoising} method within the WT, by which the noise
contribution is extracted from output signals. Although the response of a
single (N=1) neuron to sub-threshold transient signals with noises is quite
unreliable, the transmission fidelity assessed by the cross-correlation and SNR
is shown to be much improved by increasing the value of : a population of
neurons play an indispensable role in the stochastic resonance (SR) for
transient spike inputs. It is also shown that in a large-scale ensemble, the
transmission fidelity for supra-threshold transient spikes is not significantly
degraded by a weak noise which is responsible to SR for sub-threshold inputs.Comment: 20 pages, 4 figure
Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data.
The construction of compartmental models of neurons involves tuning a set of parameters to make the model neuron behave as realistically as possible. While the parameter space of single-compartment models or other simple models can be exhaustively searched, the introduction of dendritic geometry causes the number of parameters to balloon. As parameter tuning is a daunting and time-consuming task when performed manually, reliable methods for automatically optimizing compartmental models are desperately needed, as only optimized models can capture the behavior of real neurons. Here we present a three-step strategy to automatically build reduced models of layer 5 pyramidal neurons that closely reproduce experimental data. First, we reduce the pattern of dendritic branches of a detailed model to a set of equivalent primary dendrites. Second, the ion channel densities are estimated using a multi-objective optimization strategy to fit the voltage trace recorded under two conditions - with and without the apical dendrite occluded by pinching. Finally, we tune dendritic calcium channel parameters to model the initiation of dendritic calcium spikes and the coupling between soma and dendrite. More generally, this new method can be applied to construct families of models of different neuron types, with applications ranging from the study of information processing in single neurons to realistic simulations of large-scale network dynamics
The Islands Project for Managing Populations in Genetic Training of Spiking Neural Networks
The TENNLab software framework enables researchers to explore spiking neuroprocessors, neuromorphic applications and how they are trained. The centerpiece of training in TENNLab has been a genetic algorithm called Evolutionary Optimization For Neuromorphic System (EONS). EONS optimizes a single population of spiking neural networks, and heretofore, many methods to train with multiple populations have been ad hoc, typically consisting of shell scripts that execute multiple independent EONS jobs, whose results are combined and analyzed in another ad hoc fashion. The Islands project seeks to manage and manipulate multiple EONS populations in a controlled way. With Islands, one may spawn off independent EONS populations, each of which is an “Island.” One may define characteristics of a “stagnated” island, where further optimization is unlikely to improve the fitness of the population on the island. The Island software then allows one to create new islands by combining stagnated islands, or to migrate populations from one island to others, all in an attempt to increase diversity among the populations to improve their fitness. This thesis describes the software structure of Islands, its interface, and the functionalities that it implements. We then perform a case study with three neuromorphic control applications that demonstrate the wide variety of features of Islands
A point process framework for modeling electrical stimulation of the auditory nerve
Model-based studies of auditory nerve responses to electrical stimulation can
provide insight into the functioning of cochlear implants. Ideally, these
studies can identify limitations in sound processing strategies and lead to
improved methods for providing sound information to cochlear implant users. To
accomplish this, models must accurately describe auditory nerve spiking while
avoiding excessive complexity that would preclude large-scale simulations of
populations of auditory nerve fibers and obscure insight into the mechanisms
that influence neural encoding of sound information. In this spirit, we develop
a point process model of the auditory nerve that provides a compact and
accurate description of neural responses to electric stimulation. Inspired by
the framework of generalized linear models, the proposed model consists of a
cascade of linear and nonlinear stages. We show how each of these stages can be
associated with biophysical mechanisms and related to models of neuronal
dynamics. Moreover, we derive a semi-analytical procedure that uniquely
determines each parameter in the model on the basis of fundamental statistics
from recordings of single fiber responses to electric stimulation, including
threshold, relative spread, jitter, and chronaxie. The model also accounts for
refractory and summation effects that influence the responses of auditory nerve
fibers to high pulse rate stimulation. Throughout, we compare model predictions
to published physiological data and explain differences in auditory nerve
responses to high and low pulse rate stimulation. We close by performing an
ideal observer analysis of simulated spike trains in response to sinusoidally
amplitude modulated stimuli and find that carrier pulse rate does not affect
modulation detection thresholds.Comment: 1 title page, 27 manuscript pages, 14 figures, 1 table, 1 appendi
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