6 research outputs found
Bifurcation analysis in a silicon neuron
International audienceIn this paper, we describe an analysis of the nonlinear dynamical phenomenon associated with a silicon neuron. Our silicon neuron integrates Hodgkin-Huxley (HH) model formalism, including the membrane voltage dependency of temporal dynamics. Analysis of the bifurcation conditions allow us to identify different regimes in the parameter space that are desirable for biasing our silicon neuron. This approach of studying bifurcations is useful because it is believed that computational properties of neurons are based on the bifurcations exhibited by these dynamical systems in response to some changing stimulus. We describe numerical simulations and measurements of the Hopf bifurcation which is characteristic of class 2 excitability in the HH model. We also show a phenomenon observed in biological neurons and termed excitation block. Hence, by showing that this silicon neuron has similar bifurcations to a certain class of biological neurons, we can claim that the silicon neuron can also perform similar computation
Delay Learning Architectures for Memory and Classification
We present a neuromorphic spiking neural network, the DELTRON, that can
remember and store patterns by changing the delays of every connection as
opposed to modifying the weights. The advantage of this architecture over
traditional weight based ones is simpler hardware implementation without
multipliers or digital-analog converters (DACs) as well as being suited to
time-based computing. The name is derived due to similarity in the learning
rule with an earlier architecture called Tempotron. The DELTRON can remember
more patterns than other delay-based networks by modifying a few delays to
remember the most 'salient' or synchronous part of every spike pattern. We
present simulations of memory capacity and classification ability of the
DELTRON for different random spatio-temporal spike patterns. The memory
capacity for noisy spike patterns and missing spikes are also shown. Finally,
we present SPICE simulation results of the core circuits involved in a
reconfigurable mixed signal implementation of this architecture.Comment: 27 pages, 20 figure
Hardware-Amenable Structural Learning for Spike-based Pattern Classification using a Simple Model of Active Dendrites
This paper presents a spike-based model which employs neurons with
functionally distinct dendritic compartments for classifying high dimensional
binary patterns. The synaptic inputs arriving on each dendritic subunit are
nonlinearly processed before being linearly integrated at the soma, giving the
neuron a capacity to perform a large number of input-output mappings. The model
utilizes sparse synaptic connectivity; where each synapse takes a binary value.
The optimal connection pattern of a neuron is learned by using a simple
hardware-friendly, margin enhancing learning algorithm inspired by the
mechanism of structural plasticity in biological neurons. The learning
algorithm groups correlated synaptic inputs on the same dendritic branch. Since
the learning results in modified connection patterns, it can be incorporated
into current event-based neuromorphic systems with little overhead. This work
also presents a branch-specific spike-based version of this structural
plasticity rule. The proposed model is evaluated on benchmark binary
classification problems and its performance is compared against that achieved
using Support Vector Machine (SVM) and Extreme Learning Machine (ELM)
techniques. Our proposed method attains comparable performance while utilizing
10 to 50% less computational resources than the other reported techniques.Comment: Accepted for publication in Neural Computatio