9,515 research outputs found
Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface
Experiments show that spike-triggered stimulation performed with
Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen
connections between separate neural sites in motor cortex (MC). What are the
neuronal mechanisms responsible for these changes and how does targeted
stimulation by a BBCI shape population-level synaptic connectivity? The present
work describes a recurrent neural network model with probabilistic spiking
mechanisms and plastic synapses capable of capturing both neural and synaptic
activity statistics relevant to BBCI conditioning protocols. When spikes from a
neuron recorded at one MC site trigger stimuli at a second target site after a
fixed delay, the connections between sites are strengthened for spike-stimulus
delays consistent with experimentally derived spike time dependent plasticity
(STDP) rules. However, the relationship between STDP mechanisms at the level of
networks, and their modification with neural implants remains poorly
understood. Using our model, we successfully reproduces key experimental
results and use analytical derivations, along with novel experimental data. We
then derive optimal operational regimes for BBCIs, and formulate predictions
concerning the efficacy of spike-triggered stimulation in different regimes of
cortical activity.Comment: 35 pages, 9 figure
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
Branch-specific plasticity enables self-organization of nonlinear computation in single neurons
It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed in order to solve for example the binding problem. However, it is not clear how single neurons could acquire such functionality in a self-organized manner, since most theoretical studies of synaptic plasticity and learning concentrate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticity mechanisms in single neurons has emerged from experiments. In particular, new experimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent STDP and branch-strength potentiation could be integrated to self-organize nonlinear neural computations with dendritic spikes. We provide a mathematical proof that in a simplified setup these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to become member of several neuronal ensembles, and to acquire nonlinear computational capabilities, such as for example the capability to bind multiple input features. Hence our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms
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
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
Synaptic plasticity in medial vestibular nucleus neurons: comparison with computational requirements of VOR adaptation
Background: Vestibulo-ocular reflex (VOR) gain adaptation, a longstanding experimental model of cerebellar learning, utilizes sites of plasticity in both cerebellar cortex and brainstem. However, the mechanisms by which the activity of cortical Purkinje cells may guide synaptic plasticity in brainstem vestibular neurons are unclear. Theoretical analyses indicate that vestibular plasticity should depend upon the correlation between Purkinje cell and vestibular afferent inputs, so that, in gain-down learning for example, increased cortical activity should induce long-term depression (LTD) at vestibular synapses.
Methodology/Principal Findings: Here we expressed this correlational learning rule in its simplest form, as an anti-Hebbian, heterosynaptic spike-timing dependent plasticity interaction between excitatory (vestibular) and inhibitory (floccular) inputs converging on medial vestibular nucleus (MVN) neurons (input-spike-timing dependent plasticity, iSTDP). To test this rule, we stimulated vestibular afferents to evoke EPSCs in rat MVN neurons in vitro. Control EPSC recordings were followed by an induction protocol where membrane hyperpolarizing pulses, mimicking IPSPs evoked by flocculus inputs, were paired with single vestibular nerve stimuli. A robust LTD developed at vestibular synapses when the afferent EPSPs coincided with membrane hyperpolarisation, while EPSPs occurring before or after the simulated IPSPs induced no lasting change. Furthermore, the iSTDP rule also successfully predicted the effects of a complex protocol using EPSP trains designed to mimic classical conditioning.
Conclusions: These results, in strong support of theoretical predictions, suggest that the cerebellum alters the strength of vestibular synapses on MVN neurons through hetero-synaptic, anti-Hebbian iSTDP. Since the iSTDP rule does not depend on post-synaptic firing, it suggests a possible mechanism for VOR adaptation without compromising gaze-holding and VOR performance in vivo
Selectivity and Metaplasticity in a Unified Calcium-Dependent Model
A unified, biophysically motivated Calcium-Dependent Learning model has been shown to account for various rate-based and spike time-dependent paradigms for inducing synaptic plasticity. Here, we investigate the properties of this model for a multi-synapse neuron that receives inputs with different spike-train statistics. In addition, we present a physiological form of metaplasticity, an activity-driven regulation mechanism, that is essential for the robustness of the model. A neuron thus implemented develops stable and selective receptive fields, given various input statistic
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