401 research outputs found
The high-conductance state enables neural sampling in networks of LIF neurons
The apparent stochasticity of in-vivo neural circuits has long been
hypothesized to represent a signature of ongoing stochastic inference in the
brain. More recently, a theoretical framework for neural sampling has been
proposed, which explains how sample-based inference can be performed by
networks of spiking neurons. One particular requirement of this approach is
that the neural response function closely follows a logistic curve.
Analytical approaches to calculating neural response functions have been the
subject of many theoretical studies. In order to make the problem tractable,
particular assumptions regarding the neural or synaptic parameters are usually
made. However, biologically significant activity regimes exist which are not
covered by these approaches: Under strong synaptic bombardment, as is often the
case in cortex, the neuron is shifted into a high-conductance state (HCS)
characterized by a small membrane time constant. In this regime, synaptic time
constants and refractory periods dominate membrane dynamics.
The core idea of our approach is to separately consider two different "modes"
of spiking dynamics: burst spiking and transient quiescence, in which the
neuron does not spike for longer periods. We treat the former by propagating
the PDF of the effective membrane potential from spike to spike within a burst,
while using a diffusion approximation for the latter. We find that our
prediction of the neural response function closely matches simulation data.
Moreover, in the HCS scenario, we show that the neural response function
becomes symmetric and can be well approximated by a logistic function, thereby
providing the correct dynamics in order to perform neural sampling. We hereby
provide not only a normative framework for Bayesian inference in cortex, but
also powerful applications of low-power, accelerated neuromorphic systems to
relevant machine learning tasks.Comment: 3 pages, 1 figur
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artificial
neural networks have diverged from their biological archetypes as they have
been remodeled to fit particular tasks. In this paper, we review several
possibilites to reverse map these architectures to biologically more realistic
spiking networks with the aim of emulating them on fast, low-power neuromorphic
hardware. Since many of these devices employ analog components, which cannot be
perfectly controlled, finding ways to compensate for the resulting effects
represents a key challenge. Here, we discuss three different strategies to
address this problem: the addition of auxiliary network components for
stabilizing activity, the utilization of inherently robust architectures and a
training method for hardware-emulated networks that functions without perfect
knowledge of the system's dynamics and parameters. For all three scenarios, we
corroborate our theoretical considerations with experimental results on
accelerated analog neuromorphic platforms.Comment: accepted at ISCAS 201
Stochasticity from function -- why the Bayesian brain may need no noise
An increasing body of evidence suggests that the trial-to-trial variability
of spiking activity in the brain is not mere noise, but rather the reflection
of a sampling-based encoding scheme for probabilistic computing. Since the
precise statistical properties of neural activity are important in this
context, many models assume an ad-hoc source of well-behaved, explicit noise,
either on the input or on the output side of single neuron dynamics, most often
assuming an independent Poisson process in either case. However, these
assumptions are somewhat problematic: neighboring neurons tend to share
receptive fields, rendering both their input and their output correlated; at
the same time, neurons are known to behave largely deterministically, as a
function of their membrane potential and conductance. We suggest that spiking
neural networks may, in fact, have no need for noise to perform sampling-based
Bayesian inference. We study analytically the effect of auto- and
cross-correlations in functionally Bayesian spiking networks and demonstrate
how their effect translates to synaptic interaction strengths, rendering them
controllable through synaptic plasticity. This allows even small ensembles of
interconnected deterministic spiking networks to simultaneously and
co-dependently shape their output activity through learning, enabling them to
perform complex Bayesian computation without any need for noise, which we
demonstrate in silico, both in classical simulation and in neuromorphic
emulation. These results close a gap between the abstract models and the
biology of functionally Bayesian spiking networks, effectively reducing the
architectural constraints imposed on physical neural substrates required to
perform probabilistic computing, be they biological or artificial
Spiking neurons with short-term synaptic plasticity form superior generative networks
Spiking networks that perform probabilistic inference have been proposed both
as models of cortical computation and as candidates for solving problems in
machine learning. However, the evidence for spike-based computation being in
any way superior to non-spiking alternatives remains scarce. We propose that
short-term plasticity can provide spiking networks with distinct computational
advantages compared to their classical counterparts. In this work, we use
networks of leaky integrate-and-fire neurons that are trained to perform both
discriminative and generative tasks in their forward and backward information
processing paths, respectively. During training, the energy landscape
associated with their dynamics becomes highly diverse, with deep attractor
basins separated by high barriers. Classical algorithms solve this problem by
employing various tempering techniques, which are both computationally
demanding and require global state updates. We demonstrate how similar results
can be achieved in spiking networks endowed with local short-term synaptic
plasticity. Additionally, we discuss how these networks can even outperform
tempering-based approaches when the training data is imbalanced. We thereby
show how biologically inspired, local, spike-triggered synaptic dynamics based
simply on a limited pool of synaptic resources can allow spiking networks to
outperform their non-spiking relatives.Comment: corrected typo in abstrac
The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study
High-level brain function such as memory, classification or reasoning can be
realized by means of recurrent networks of simplified model neurons. Analog
neuromorphic hardware constitutes a fast and energy efficient substrate for the
implementation of such neural computing architectures in technical applications
and neuroscientific research. The functional performance of neural networks is
often critically dependent on the level of correlations in the neural activity.
In finite networks, correlations are typically inevitable due to shared
presynaptic input. Recent theoretical studies have shown that inhibitory
feedback, abundant in biological neural networks, can actively suppress these
shared-input correlations and thereby enable neurons to fire nearly
independently. For networks of spiking neurons, the decorrelating effect of
inhibitory feedback has so far been explicitly demonstrated only for
homogeneous networks of neurons with linear sub-threshold dynamics. Theory,
however, suggests that the effect is a general phenomenon, present in any
system with sufficient inhibitory feedback, irrespective of the details of the
network structure or the neuronal and synaptic properties. Here, we investigate
the effect of network heterogeneity on correlations in sparse, random networks
of inhibitory neurons with non-linear, conductance-based synapses. Emulations
of these networks on the analog neuromorphic hardware system Spikey allow us to
test the efficiency of decorrelation by inhibitory feedback in the presence of
hardware-specific heterogeneities. The configurability of the hardware
substrate enables us to modulate the extent of heterogeneity in a systematic
manner. We selectively study the effects of shared input and recurrent
connections on correlations in membrane potentials and spike trains. Our
results confirm ...Comment: 20 pages, 10 figures, supplement
Solving Machine Learning Problems with Biological Principles
Spiking neural networks (SNNs) have been proposed both as models of cortical computation and as candidates for solving problems in machine learning. While increasing recent works have improved their performances in benchmark discriminative tasks, most of them learn by surrogates of backpropagation where biological features such as spikes are regarded more as defects than merits. In this thesis, we explore the enerative abilities of SNNs with built-in biological mechanisms. When sampling from high-dimensional multimodal distributions, models based on general Markov chain Monte Carlo methods often have the mixing problem that the sampler is easy to get trapped in local minima. Inspired from traditional annealing or tempering approaches, we demonstrate that increasing the rate of background Poisson noise in an SNN can flatten the energy landscape and facilitate mixing of the system. In addition, we show that with synaptic short-term plasticity (STP) the SNN can achieve more efficient mixing by local modulation of active
attractors and eventually outperforming traditional benchmark models. We reveal diverse sampling statistics of SNNs induced by STP and finally study its implementation
on conventional machine learning methods. Our work thereby highlights important computational consequences of biological features that might otherwise appear as artifacts
of evolution
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Neuromorphic devices represent an attempt to mimic aspects of the brain's
architecture and dynamics with the aim of replicating its hallmark functional
capabilities in terms of computational power, robust learning and energy
efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic
system to implement a proof-of-concept demonstration of reward-modulated
spike-timing-dependent plasticity in a spiking network that learns to play the
Pong video game by smooth pursuit. This system combines an electronic
mixed-signal substrate for emulating neuron and synapse dynamics with an
embedded digital processor for on-chip learning, which in this work also serves
to simulate the virtual environment and learning agent. The analog emulation of
neuronal membrane dynamics enables a 1000-fold acceleration with respect to
biological real-time, with the entire chip operating on a power budget of 57mW.
Compared to an equivalent simulation using state-of-the-art software, the
on-chip emulation is at least one order of magnitude faster and three orders of
magnitude more energy-efficient. We demonstrate how on-chip learning can
mitigate the effects of fixed-pattern noise, which is unavoidable in analog
substrates, while making use of temporal variability for action exploration.
Learning compensates imperfections of the physical substrate, as manifested in
neuronal parameter variability, by adapting synaptic weights to match
respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about
journal publication. Frontiers in Neuromorphic Engineering (2019
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