23 research outputs found
Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks
We study in this paper the effect of an unique initial stimulation on random
recurrent networks of leaky integrate and fire neurons. Indeed given a
stochastic connectivity this so-called spontaneous mode exhibits various non
trivial dynamics. This study brings forward a mathematical formalism that
allows us to examine the variability of the afterward dynamics according to the
parameters of the weight distribution. Provided independence hypothesis (e.g.
in the case of very large networks) we are able to compute the average number
of neurons that fire at a given time -- the spiking activity. In accordance
with numerical simulations, we prove that this spiking activity reaches a
steady-state, we characterize this steady-state and explore the transients.Comment: 28 pages, 7 figure
Event-driven simulation of spiking neurons with stochastic dynamics
We present a new technique, based on a proposed event-based strategy (Mattia & Del Giudice, 2000), for efficiently simulating large networks of simple model neurons. The strategy was based on the fact that interactions among neurons occur by means of events that are well localized in time (the action potentials) and relatively rare. In the interval between two of these events, the state variables associated with a model neuron or a synapse evolved deterministically and in a predictable way. Here, we extend the event-driven simulation strategy to the case in which the dynamics of the state variables in the inter-event intervals are stochastic. This extension captures both the situation in which the simulated neurons are inherently noisy and the case in which they are embedded in a very large network and receive a huge number of random synaptic inputs. We show how to effectively include the impact of large background populations into neuronal dynamics by means of the numerical evaluation of the statistical properties of single-model neurons under random current injection. The new simulation strategy allows the study of networks of interacting neurons with an arbitrary number of external afferents and inherent stochastic dynamics
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Recent studies have shown that synaptic unreliability is a robust and
sufficient mechanism for inducing the stochasticity observed in cortex. Here,
we introduce Synaptic Sampling Machines, a class of neural network models that
uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised
learning. Similar to the original formulation of Boltzmann machines, these
models can be viewed as a stochastic counterpart of Hopfield networks, but
where stochasticity is induced by a random mask over the connections. Synaptic
stochasticity plays the dual role of an efficient mechanism for sampling, and a
regularizer during learning akin to DropConnect. A local synaptic plasticity
rule implementing an event-driven form of contrastive divergence enables the
learning of generative models in an on-line fashion. Synaptic sampling machines
perform equally well using discrete-timed artificial units (as in Hopfield
networks) or continuous-timed leaky integrate & fire neurons. The learned
representations are remarkably sparse and robust to reductions in bit precision
and synapse pruning: removal of more than 75% of the weakest connections
followed by cursory re-learning causes a negligible performance loss on
benchmark classification tasks. The spiking neuron-based synaptic sampling
machines outperform existing spike-based unsupervised learners, while
potentially offering substantial advantages in terms of power and complexity,
and are thus promising models for on-line learning in brain-inspired hardware
A spike-based head-movement and echolocation model of the bat superior colliculus
Echolocating bats use sonar to sense their environment and hunt for food in darkness. To understand this unusual sensory system from a computational perspective with aspirations towards developing high performance electronic implementations, we study the bat brain. The midbrain superior colliculus (SC) has been shown (in many species) to support multisensory integration and orientation behaviors, namely eye saccades and head turns. Previous computational models of the SC have emphasized the behavior typical to monkeys, barn owls, and cats. Using unique neurobiological data for the bat and incorporating knowledge from other species, a computational spiking model has been developed to produce both head-movement and sonar vocalization. The model accomplishes this with simple neuron equations and synapses, which is promising for implementation on a VLSI chip. This model can serve as a foundation for further developments, using new data from bat experiments, and be easily connected to spiking motor and vocalization systems
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network