Aryeh H. Taub

Cortical Balance of Excitation and Inhibition Is Regulated by the Rate of Synaptic Activity

Cortical activity is determined by the balance between excitation and inhibition. To examine how shifts in brain activity affect this balance, we recorded spontaneous excitatory and inhibitory synaptic inputs into layer 4 neurons from rat somatosensory cortex while altering the depth of anesthesia. The rate of excitatory and inhibitory events was reduced by ∼50% when anesthesia was deepened. However, whereas both the amplitude and width of inhibitory synaptic events profoundly increased under deep anesthesia, those of excitatory events were unaffected. These effects were found using three different types of anesthetics, suggesting that they are caused by the network state and not by local specific action of the anesthetics. To test our hypothesis that the size of inhibitory events increased because of the decreased rate of synaptic activity under deep anesthesia, we blocked cortical excitation and replayed the slow and fast patterns of inhibitory inputs using intracortical electrical stimulation. Evoked inhibition was larger under low-frequency stimulation, and, importantly, this change occurred regardless of the depth of anesthesia. Hence, shifts in the balance between excitation and inhibition across distinct states of cortical activity can be explained by the rate of inhibitory inputs combined with their short-term plasticity properties, regardless of the actual global brain activity.

A VLSI Field-Programmable Mixed-Signal Array to Perform Neural Signal Processing and Neural Modeling in a Prosthetic System

A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.

A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats

Neuroprostheses could potentially recover functions lost due to neural damage. Typical neuroprostheses connect an intact brain with the external environment, thus replacing damaged sensory or motor pathways. Recently, closed-loop neuroprostheses, bidirectionally interfaced with the brain, have begun to emerge, offering an opportunity to substitute malfunctioning brain structures. In this proof-of-concept study, we demonstrate a neuro-inspired model-based approach to neuroprostheses. A VLSI chip was designed to implement essential cerebellar synaptic plasticity rules, and was interfaced with cerebellar input and output nuclei in real time, thus reproducing cerebellum-dependent learning in anesthetized rats. Such a model-based approach does not require prior system identification, allowing for de novo experience-based learning in the brain-chip hybrid, with potential clinical advantages and limitations when compared to existing parametric “black box” models.

Behavioral rehabilitation of the eye closure reflex in senescent rats using a real-time biosignal acquisition system

In this paper the replacement of a lost learning function of rats through a computer-based real-time recording and feedback system is shown. In an experiment two recording electrodes and one stimulation electrode were implanted in an anesthetized rat. During a classical-conditioning paradigm, which includes tone and airpuff stimulation, biosignals were recorded and the stimulation events detected. A computational model of the cerebellum acquired the association between the stimuli and gave feedback to the brain of the rat using deep brain stimulation in order to close the eyelid of the rat. The study shows that replacement of a lost brain function using a direct bidirectional interface to the brain is realizable and can inspire future research for brain rehabilitation.

A Cerebellar Neuroprosthetic System: Computational Architecture and in vivo Test

Emulating the input–output functions performed by a brain structure opens the possibility for developing neuroprosthetic systems that replace damaged neuronal circuits. Here, we demonstrate the feasibility of this approach by replacing the cerebellar circuit responsible for the acquisition and extinction of motor memories. Specifically, we show that a rat can undergo acquisition, retention, and extinction of the eye-blink reflex even though the biological circuit responsible for this task has been chemically inactivated via anesthesia. This is achieved by first developing a computational model of the cerebellar microcircuit involved in the acquisition of conditioned reflexes and training it with synthetic data generated based on physiological recordings. Secondly, the cerebellar model is interfaced with the brain of an anesthetized rat, connecting the model’s inputs and outputs to afferent and efferent cerebellar structures. As a result, we show that the anesthetized rat, equipped with our neuroprosthetic system, can be classically conditioned to the acquisition of an eye-blink response. However, non-stationarities in the recorded biological signals limit the performance of the cerebellar model. Thus, we introduce an updated cerebellar model and validate it with physiological recordings showing that learning becomes stable and reliable. The resulting system represents an important step toward replacing lost functions of the central nervous system via neuroprosthetics, obtained by integrating a synthetic circuit with the afferent and efferent pathways of a damaged brain region. These results also embody an early example of science-based medicine, where on the one hand the neuroprosthetic system directly validates a theory of cerebellar learning that informed the design of the system, and on the other one it takes a step toward the development of neuro-prostheses that could recover lost learning functions in animals and, in the longer term, humans.

Oscillations Synchronize Amygdala-to-Prefrontal Primate Circuits during Aversive Learning

The contribution of oscillatory synchrony in the primate amygdala-prefrontal pathway to aversive learning remains largely unknown. We found increased power and phase synchrony in the theta range during aversive conditioning. The synchrony was linked to single-unit spiking and exhibited specific directionality between input and output measures in each region. Although it was correlated with the magnitude of conditioned responses, it declined once the association stabilized. The results suggest that amygdala spikes help to synchronize ACC activity and transfer error signal information to support memory formation.

Acceleration of cerebellar conditioning through improved detection of its sensory input

We studied whether slow cerebellar motor-learning is related to low signal-to-noise ratio (SNR) of the sensory signals at the pontine nucleus (PN). Rats’ PN activity was recorded during baseline and in response to auditory-stimuli. Response SNR was associated inversely with the level of baseline activity. To test the implications of this finding on cerebellar learning, a brain-computer interface was devised to analyze on-line the PN activity and deliver paired auditory conditioned stimulus (CS) and periorbital unconditioned stimulus trials, on low level baseline activity. This maneuver resulted in PN responses of high SNR and accelerated eyeblink-conditioning. These findings suggest that the poly-sensory noisy activity in the PN reduces the SNR of the CS, compromising its detection in the cerebellum which in turn slows-down the learning rate. Present results suggest a unique way in which brain-computer interfaces can be used to potentiate and expedite motor learning in healthy and brain degenerated subjects.

Implicit aversive memory under anaesthesia in animal models: a narrative review

&NA; Explicit memory after anaesthesia has gained considerable attention because of its negative implications, while implicit memory, which is more elusive and lacks patients’ explicit recall, has received less attention and dedicated research. This is despite the likely impact of implicit memory on postoperative long‐term well‐being and behaviour. Given the scarcity of human data, fear conditioning in animals offers a reliable model of implicit learning, and importantly, one where we already have a good understanding of the underlying neural circuitry in awake conditions. Animal studies provide evidence that fear conditioning occurs under anaesthesia. The effects of different anaesthetics on memory are complex, with different drugs interacting at different stages of learning. Modulatory suppressive effects can be because of context, specific drugs, and dose dependency. In some cases, low doses of general anaesthetics can actually lead to a paradoxical opposite effect. The underlying mechanisms involve several neurotransmitter systems, acting mainly in the amygdala, hippocampus, and neocortex. Here, we review animal studies of aversive conditioning under anaesthesia, discuss the complex picture that arises, identify the gaps in knowledge that require further investigation, and highlight the potential translational relevance of the models.

Detection of auditory stimulus onset in the Pontine Nucleus using a multichannel multi-unit activity electrode

This paper discusses a real time stimulus timing detection for a Brain-Machine-Interface (BMI). We present a low complexity detector for detecting the stimulus onset time from real multichannel, multi-unit electro-physiological data, recorded from a brainstem area called Pontine Nucleus (PN). The detector contains a novel pre-processing block, which takes advantage of the high coherence between different channels during response, in order to enhance the Signal-to- Noise Ratio (SNR), as well as to achieve higher detection rates. An intuitive effective method for fusion and combination of different channels based on spike counts is used. A full detailed description of the algorithm blocks is presented, along with its optimized parameters according to real data performance evaluation.

The application of a real-time rapid-prototyping environment for the behavioral rehabilitation of a lost brain function in rats

In this paper we propose a Rapid Prototyping Environment (RPE) for real-time biosignal analysis including ECG, EEG, ECoG and EMG of humans and animals requiring a very precise time resolution. Based on the previous RPE which was mainly designed for developing Brain Computer Interfaces (BCI), the present solution offers tools for data preprocessing, analysis and visualization even in the case of high sampling rates and furthermore tools for precise cognitive stimulation. One application of the system, the analysis of multi-unit activity measured from the brain of a rat is presented to prove the efficiency of the proposed environment. The experimental setup was used to design and implement a biomimetic, biohybrid model for demonstrating the recovery of a learning function lost with age. Throughout the paper we discuss the components of the setup, the software structure and the online visualization. At the end we present results of a real-time experiment in which the model of the brain learned to react to the acquired signals.