Spike trains and local field potentials (LFPs) are two different manifestations of neural activity recorded simultaneously from the same electrode array and contain complementary information of stimuli or behaviors. T...
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(纸本)9781424441198
Spike trains and local field potentials (LFPs) are two different manifestations of neural activity recorded simultaneously from the same electrode array and contain complementary information of stimuli or behaviors. This paper proposes a tensor product kernel based decoder, which allows modeling the sample from different sources individually and mapping them onto the same reproducing kernel Hilbert space (RKHS) defined by the tensor product of the individual kernels for each source, where linear regression is conducted to identify the nonlinear mapping from the multi-type neural responses to the stimuli. The decoding results of the rat sensory stimulation experiment show that the tensor-product-kernelbased decoder outperforms the decoders with either single-type neural activities.
Neuronal networks are complex, adaptive systems that typically display oscillatory dynamics. The extent to which these dynamics can be shaped by training remains unknown. We explored this dynamical training in a compu...
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Precise control of neural circuits via microstimulation is an indispensable but challenging objective in neuro-engineering. The effect of electrical stimulation is imprecise and has a spatio-temporal blurring. At the ...
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Precise control of neural circuits via microstimulation is an indispensable but challenging objective in neuro-engineering. The effect of electrical stimulation is imprecise and has a spatio-temporal blurring. At the neuron level, the effects are obfuscated by the complexity of neural dynamics. This paper proposes an online multiple-input-multiple-output (MIMO) adaptive inverse controller for somatosensory microstimulation. The control of the target firing pattern is achieved by including an adaptive controller before the stimulator whose transfer function is always adjusted to be the inverse of the neural circuit transfer function. In this paper a synthetic neural circuit is built from LIF neurons to model the neural circuit. Considering a Poisson model for the target spike train, we identify the LIF neural model using a Generalized Linear Model (GLM) fitted with a maximum likelihood (ML) criterion. The controller architecture becomes the inverse of the GLM and its parameters are periodically adjusted to ensure that the input to the LIF model approximates the target spike time response. In synthetic data, the results show that this control scheme successfully determines the impulse timing and amplitude of the desired stimuli and drives the dynamic neural circuit output to follow the target firing pattern. With the simulated model, the method is able to preserve the temporal precision of neural spike trains.
Neuronal networks are complex, adaptive systems that typically display oscillatory dynamics. The extent to which these dynamics can be shaped by training remains unknown. We explored this dynamical training in a compu...
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Neuronal networks are complex, adaptive systems that typically display oscillatory dynamics. The extent to which these dynamics can be shaped by training remains unknown. We explored this dynamical training in a computer model of 6-layered sensory neocortex with 470 excitatory (E) and inhibitory (I) cells. AMPA, NMDA, and GABAA synapses were provided with Poisson input to provide baseline activation in the network. The learning rule employed spike-timing-dependent plasticity (STDP) at all AMPA synapses. We trained with a 1-16 Hz thalamic afferent signal to E4 cells (layer 4 E cells). At baseline, the power spectrum of the network activity showed oscillations with a low-amplitude peak near 6 Hz. Plasticity in the absence of a training signal (white noise input) attenuated the network response, due to the potentiation of E-to-I synapses. Plasticity coupled with an 8 Hz training signal enhanced the network's oscillations and shifted the peak to ~20 Hz. This was due to increased synaptic connection strengths between E cells caused by the near-synchronous firing of E4 cells. Plasticity coupled with a 16 Hz training signal shifted the network towards epilepsy, with high-amplitude 8 Hz oscillations and synchronous firing across all layers. The shift into epilepsy was caused by further enhancement of E-to-E synapses. In summary, our simulations demonstrate the feasibility of using plasticity and neuroprosthetic input signals to train a neuronal network's oscillatory dynamics. We predict that in order for learning in the brain to avoid transition to epilepsy, homeostatic control mechanisms must balance learning at E-to-E and E-to-I synapses.
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