David B. Grayden

Perceptual characterization of children with auditory neuropathy.

Objective To characterize the perceptual abilities of a group of children with auditory neuropathy (AN)-type hearing loss, correlating results on a range of psychophysical tasks with open-set speech perception performance. Design Frequency resolution, temporal resolution and frequency discrimination ability was assessed in a group of 14 children with AN. Data also were obtained from a cohort of matched subjects with sensorineural hearing loss, and from a group of normally hearing children. Results Frequency resolution (notched noise masking) results for the AN subjects were equivalent to those of the normal-hearing subjects reflecting the “normal” outer hair cell function that characterizes the AN condition. Temporal resolution (TMTF) findings were, however, abnormal in many AN subjects and the degree of temporal disruption was correlated with speech discrimination (CNC) score. Frequency discrimination ability (for both fixed and frequency modulated stimuli) was also affected in those children with poor temporal resolution Conclusions The findings of this study indicate that the perceptual profiles of children with AN are quite different from those with sensorineural hearing loss. Where subjects in the latter group presented with impaired frequency resolution and normal temporal processing, the AN subjects typically showed normal frequency resolution and varying degrees of temporal disruption. The severity of this temporal abnormality, which appeared to affect both temporal resolution/amplitude modulation detection and the temporal aspects of frequency discrimination (such as phase locking), was strongly correlated to speech perception performance.

Spike-timing-dependent plasticity: the relationship to rate-based learning for models with weight dynamics determined by a stable fixed point

Experimental evidence indicates that synaptic modification depends on the timing relationship between the presynaptic inputs and the output spikes that they generate. In this letter, results are presented for models of spike-timing-dependent plasticity (STDP) whose weight dynamics is determined by a stable fixed point. Four classes of STDP are identified on the basis of the time extent of their input-output interactions. The effect on the potentiation of synapses with different rates of input is investigated to elucidate the relationship of STDP with classical studies of long-term potentiation and depression and rate-based Hebbian learning. The selective potentiation of higher-rate synaptic inputs is found only for models where the time extent of the input-output interactions is input restricted (i.e., restricted to time domains delimited by adjacent synaptic inputs) and that have a time-asymmetric learning window with a longer time constant for depression than for potentiation. The analysis provides an account of learning dynamics determined by an input-selective stable fixed point. The effect of suppressive interspike interactions on STDP is also analyzed and shown to modify the synaptic dynamics.

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways

Spike-timing-dependent plasticity (STDP) determines the evolution of the synaptic weights according to their pre- and post-synaptic activity, which in turn changes the neuronal activity. In this paper, we extend previous studies of input selectivity induced by (STDP) for single neurons to the biologically interesting case of a neuronal network with fixed recurrent connections and plastic connections from external pools of input neurons. We use a theoretical framework based on the Poisson neuron model to analytically describe the network dynamics (firing rates and spike-time correlations) and thus the evolution of the synaptic weights. This framework incorporates the time course of the post-synaptic potentials and synaptic delays. Our analysis focuses on the asymptotic states of a network stimulated by two homogeneous pools of “steady” inputs, namely Poisson spike trains which have fixed firing rates and spike-time correlations. The (STDP) model extends rate-based learning in that it can implement, at the same time, both a stabilization of the individual neuron firing rates and a slower weight specialization depending on the input spike-time correlations. When one input pathway has stronger within-pool correlations, the resulting synaptic dynamics induced by (STDP) are shown to be similar to those arising in the case of a purely feed-forward network: the weights from the more correlated inputs are potentiated at the expense of the remaining input connections.

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: Structuring synaptic pathways among recurrent connections

In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network.

Study of neuronal gain in a conductance-based leaky integrate-and-fire neuron model with balanced excitatory and inhibitory synaptic input

Abstract.Neurons receive a continual stream of excitatory and inhibitory synaptic inputs. A conductance-based neuron model is used to investigate how the balanced component of this input modulates the amplitude of neuronal responses. The output spiking rate is well described by a formula involving three parameters: the mean μ and variance σ of the membrane potential and the effective membrane time constant τQ. This expression shows that, for sufficiently small τQ, the level of balanced excitatory-inhibitory input has a nonlinear modulatory effect on the neuronal gain.

A data-driven framework for neural field modeling.

This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch-Tung-Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.

Effect of Age and Cognition on Childhood Speech in Noise Perception Abilities

This research on children’s speech in noise and cognitive abilities aimed to determine the age-related trends in speech in noise perception abilities and the relationship between speech in noise perception and cognitive abilities. Monosyllabic distinguishable (consonant-vowel-consonant) words was the most recognisable word category, followed by monosyllabic confusable words (consonant-vowel-consonant), disyllabic non-words (/aCa/) and monosyllabic syllables (/Ca/), demonstrating that phoneme distinctiveness and a reduction in word confusability contribute to their recognition. Older children outperformed younger children on all speech in noise tasks, indicating that there are age-related trends in speech in noise abilities. Children with higher cognitive abilities did not outperform children with lower cognitive abilities on speech in noise tasks, indicating that the ability to hear speech in noise may be an intrinsic feature of the auditory system that matures with age.

Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity

High-fidelity intracranial electrode arrays for recording and stimulating brain activity have facilitated major advances in the treatment of neurological conditions over the past decade. Traditional arrays require direct implantation into the brain via open craniotomy, which can lead to inflammatory tissue responses, necessitating development of minimally invasive approaches that avoid brain trauma. Here we demonstrate the feasibility of chronically recording brain activity from within a vein using a passive stent-electrode recording array (stentrode). We achieved implantation into a superficial cortical vein overlying the motor cortex via catheter angiography and demonstrate neural recordings in freely moving sheep for up to 190 d. Spectral content and bandwidth of vascular electrocorticography were comparable to those of recordings from epidural surface arrays. Venous internal lumen patency was maintained for the duration of implantation. Stentrodes may have wide ranging applications as a neural interface for treatment of a range of neurological conditions.

Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons

This paper evaluates the patient-specific seizure prediction performance of pre-ictal changes in bivariate-synchrony between pairs of intracranial electroencephalographic (iEEG) signals within 15min of a seizure in patients with pharmacoresistant focal epilepsy. Prediction horizons under 15min reduce the durations of warning times and should provide adequate time for a seizure control device to intervene. Long-term continuous iEEG was obtained from 6 patients. The seizure prediction performance was evaluated for all possible channel pairs and for different prediction methods to find the best performing channel pairs and methods for both pre-ictal decreases and increases in synchrony. The different prediction methods involved changes in window duration, signal filtering, thresholding approach, and prediction horizon durations. Performance for each patient, for all seizures, was first compared with an analytical-Poisson-based random predictor. The performance of the top 5% of channel pairs for each patient closely matched the top 5% of analytical-Poisson-based random predictor performance indicating that patient-specific, bivariate-synchrony-based seizure prediction could be random in general (under the assumption that channel-pair prediction times are statistically independent). Analysis of the spatial patterns of performance showed no clear relationship to the seizure onset zone. For each patient the best channel pair showed better performance than Poisson-based random prediction for a selected subset of prediction thresholds. Given the caveats of comparing with this form of random prediction, alarm time surrogates were employed to assess statistical significance of a four-fold out-of-sample cross-validation analysis applied to the best channel-pairs. The cross-validation analysis obtained reasonable testing performance for most patients when performance was compared to random prediction based on alarm time surrogates. The most significant case was a patient whose testing set sensitivity and false positive rate were 0.67±0.09 and 3.04±0.29h(-1), respectively, for decreases in synchrony, an intervention time of 15min and a seizure onset period of 5min. For each testing set for this patient, performance was better than that obtained by random prediction at the significance level of 0.05 (average sensitivity of 0.47±0.05). Moreover, there were 9 seizures in each testing set which gives greater power to this cross-validation result, although the cross-validation was performed on the best channel pair selected by within-sample optimization for all seizures of the patient. Further validation with larger datasets from individual patients is needed. Improvements in prediction performance should be achievable through investigations of multivariate synchrony combined with non-linear classification methods.

An Analytical Model for the 'Large, Fluctuating Synaptic Conductance State' Typical of Neocortical Neurons In Vivo

A model of in vivo-like neocortical activity is studied analytically in relation to experimental data and other models in order to understand the essential mechanisms underlying such activity. The model consists of a network of sparsely connected excitatory and inhibitory integrate-and-fire (IF) neurons with conductance-based synapses. It is shown that the model produces values for five quantities characterizing in vivo activity that are in agreement with both experimental ranges and a computer-simulated Hodgkin-Huxley model adapted from the literature (Destexhe et al. (2001) Neurosci. 107(1): 13–24). The analytical model builds on a study by Brunel (2000) (J. Comput. Neurosci. 8: 183–208), which used IF neurons with current-based synapses, and therefore does not account for the full range of experimental data. The present results suggest that the essential mechanism required to explain a range of data on in vivo neocortical activity is the conductance-based synapse and that the particular model of spike initiation used is not crucial. Thus the IF model with conductance-based synapses may provide a basis for the analytical study of the ‘large, fluctuating synaptic conductance state’ typical of neocortical neurons in vivo.