2,006 research outputs found
Towards a learning-theoretic analysis of spike-timing dependent plasticity
This paper suggests a learning-theoretic perspective on how synaptic
plasticity benefits global brain functioning. We introduce a model, the
selectron, that (i) arises as the fast time constant limit of leaky
integrate-and-fire neurons equipped with spiking timing dependent plasticity
(STDP) and (ii) is amenable to theoretical analysis. We show that the selectron
encodes reward estimates into spikes and that an error bound on spikes is
controlled by a spiking margin and the sum of synaptic weights. Moreover, the
efficacy of spikes (their usefulness to other reward maximizing selectrons)
also depends on total synaptic strength. Finally, based on our analysis, we
propose a regularized version of STDP, and show the regularization improves the
robustness of neuronal learning when faced with multiple stimuli.Comment: To appear in Adv. Neural Inf. Proc. System
Reinforcement learning in populations of spiking neurons
Population coding is widely regarded as a key mechanism for achieving reliable behavioral responses in the face of neuronal variability. But in standard reinforcement learning a flip-side becomes apparent. Learning slows down with increasing population size since the global reinforcement becomes less and less related to the performance of any single neuron. We show that, in contrast, learning speeds up with increasing population size if feedback about the populationresponse modulates synaptic plasticity in addition to global reinforcement. The two feedback signals (reinforcement and population-response signal) can be encoded by ambient neurotransmitter concentrations which vary slowly, yielding a fully online plasticity rule where the learning of a stimulus is interleaved with the processing of the subsequent one. The assumption of a single additional feedback mechanism therefore reconciles biological plausibility with efficient learning
Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding
Precise spike timing as a means to encode information in neural networks is
biologically supported, and is advantageous over frequency-based codes by
processing input features on a much shorter time-scale. For these reasons, much
recent attention has been focused on the development of supervised learning
rules for spiking neural networks that utilise a temporal coding scheme.
However, despite significant progress in this area, there still lack rules that
have a theoretical basis, and yet can be considered biologically relevant. Here
we examine the general conditions under which synaptic plasticity most
effectively takes place to support the supervised learning of a precise
temporal code. As part of our analysis we examine two spike-based learning
methods: one of which relies on an instantaneous error signal to modify
synaptic weights in a network (INST rule), and the other one on a filtered
error signal for smoother synaptic weight modifications (FILT rule). We test
the accuracy of the solutions provided by each rule with respect to their
temporal encoding precision, and then measure the maximum number of input
patterns they can learn to memorise using the precise timings of individual
spikes as an indication of their storage capacity. Our results demonstrate the
high performance of FILT in most cases, underpinned by the rule's
error-filtering mechanism, which is predicted to provide smooth convergence
towards a desired solution during learning. We also find FILT to be most
efficient at performing input pattern memorisations, and most noticeably when
patterns are identified using spikes with sub-millisecond temporal precision.
In comparison with existing work, we determine the performance of FILT to be
consistent with that of the highly efficient E-learning Chronotron, but with
the distinct advantage that FILT is also implementable as an online method for
increased biological realism.Comment: 26 pages, 10 figures, this version is published in PLoS ONE and
incorporates reviewer comment
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules
Most elementary behaviors such as moving the arm to grasp an object or
walking into the next room to explore a museum evolve on the time scale of
seconds; in contrast, neuronal action potentials occur on the time scale of a
few milliseconds. Learning rules of the brain must therefore bridge the gap
between these two different time scales.
Modern theories of synaptic plasticity have postulated that the co-activation
of pre- and postsynaptic neurons sets a flag at the synapse, called an
eligibility trace, that leads to a weight change only if an additional factor
is present while the flag is set. This third factor, signaling reward,
punishment, surprise, or novelty, could be implemented by the phasic activity
of neuromodulators or specific neuronal inputs signaling special events. While
the theoretical framework has been developed over the last decades,
experimental evidence in support of eligibility traces on the time scale of
seconds has been collected only during the last few years.
Here we review, in the context of three-factor rules of synaptic plasticity,
four key experiments that support the role of synaptic eligibility traces in
combination with a third factor as a biological implementation of neoHebbian
three-factor learning rules
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as
a candidate for a learning rule that could explain how behaviorally relevant
adaptive changes in complex networks of spiking neurons could be achieved in a
self-organizing manner through local synaptic plasticity. However, the
capabilities and limitations of this learning rule could so far only be tested
through computer simulations. This article provides tools for an analytic
treatment of reward-modulated STDP, which allows us to predict under which
conditions reward-modulated STDP will achieve a desired learning effect. These
analytical results imply that neurons can learn through reward-modulated STDP to
classify not only spatial but also temporal firing patterns of presynaptic
neurons. They also can learn to respond to specific presynaptic firing patterns
with particular spike patterns. Finally, the resulting learning theory predicts
that even difficult credit-assignment problems, where it is very hard to tell
which synaptic weights should be modified in order to increase the global reward
for the system, can be solved in a self-organizing manner through
reward-modulated STDP. This yields an explanation for a fundamental experimental
result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys
were rewarded for increasing the firing rate of a particular neuron in the
cortex and were able to solve this extremely difficult credit assignment
problem. Our model for this experiment relies on a combination of
reward-modulated STDP with variable spontaneous firing activity. Hence it also
provides a possible functional explanation for trial-to-trial variability, which
is characteristic for cortical networks of neurons but has no analogue in
currently existing artificial computing systems. In addition our model
demonstrates that reward-modulated STDP can be applied to all synapses in a
large recurrent neural network without endangering the stability of the network
dynamics
Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring
Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)–mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia
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