5,854 research outputs found
Independent Component Analysis in Spiking Neurons
Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition
Connectivity reflects coding: A model of voltage-based spike-timing-dependent-plasticity with homeostasis
Electrophysiological connectivity patterns in cortex often show a few strong connections in a sea of weak connections. In some brain areas a large fraction of strong connections are bidirectional, in others they are mainly unidirectional. In order to explain these connectivity patterns, we use a model of Spike-Timing-Dependent Plasticity where synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential. The model describes several nonlinear effects in STDP experiments, as well as the voltage dependence of plasticity under voltage clamp and classical paradigms of LTP/LTD induction. We show that in a simulated recurrent network of spiking neurons our plasticity rule leads not only to receptive field development, but also to connectivity patterns that reflect the neural code: for temporal coding paradigms strong connections are predominantly unidirectional, whereas they are bidirectional under rate coding. Thus variable connectivity patterns in the brain could reflect different coding principles across brain areas
Emulating long-term synaptic dynamics with memristive devices
The potential of memristive devices is often seeing in implementing
neuromorphic architectures for achieving brain-like computation. However, the
designing procedures do not allow for extended manipulation of the material,
unlike CMOS technology, the properties of the memristive material should be
harnessed in the context of such computation, under the view that biological
synapses are memristors. Here we demonstrate that single solid-state TiO2
memristors can exhibit associative plasticity phenomena observed in biological
cortical synapses, and are captured by a phenomenological plasticity model
called triplet rule. This rule comprises of a spike-timing dependent plasticity
regime and a classical hebbian associative regime, and is compatible with a
large amount of electrophysiology data. Via a set of experiments with our
artificial, memristive, synapses we show that, contrary to conventional uses of
solid-state memory, the co-existence of field- and thermally-driven switching
mechanisms that could render bipolar and/or unipolar programming modes is a
salient feature for capturing long-term potentiation and depression synaptic
dynamics. We further demonstrate that the non-linear accumulating nature of
memristors promotes long-term potentiating or depressing memory transitions
SuperSpike: Supervised learning in multi-layer spiking neural networks
A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico. Here we revisit the problem of supervised learning
in temporally coding multi-layer spiking neural networks. First, by using a
surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based
three factor learning rule capable of training multi-layer networks of
deterministic integrate-and-fire neurons to perform nonlinear computations on
spatiotemporal spike patterns. Second, inspired by recent results on feedback
alignment, we compare the performance of our learning rule under different
credit assignment strategies for propagating output errors to hidden units.
Specifically, we test uniform, symmetric and random feedback, finding that
simpler tasks can be solved with any type of feedback, while more complex tasks
require symmetric feedback. In summary, our results open the door to obtaining
a better scientific understanding of learning and computation in spiking neural
networks by advancing our ability to train them to solve nonlinear problems
involving transformations between different spatiotemporal spike-time patterns
Slowness: An Objective for Spike-Timing-Dependent Plasticity?
Slow Feature Analysis (SFA) is an efficient algorithm for
learning input-output functions that extract the most slowly varying features from a quickly varying signal. It
has been successfully applied to the unsupervised learning
of translation-, rotation-, and other invariances in a
model of the visual system, to the learning of complex cell
receptive fields, and, combined with a sparseness
objective, to the self-organized formation of place cells
in a model of the hippocampus.
In order to arrive at a biologically more plausible implementation of this learning rule, we consider analytically how SFA could be realized in simple linear continuous and spiking model neurons. It turns out that for the continuous model neuron SFA can be implemented by means of a modified version of standard Hebbian learning. In this framework we provide a connection to the trace learning rule for invariance learning. We then show that for Poisson neurons spike-timing-dependent plasticity (STDP) with a specific learning window can learn the same weight distribution as SFA. Surprisingly, we find that the appropriate learning rule reproduces the typical STDP learning window. The shape as well as the timescale are in good agreement with what has been measured experimentally. This offers a completely novel interpretation for the functional role of spike-timing-dependent plasticity in physiological neurons
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