New Perspectives on the Dialogue between Brains and Machines

Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies

Shaping the Dynamics of a Bidirectional Neural Interface

Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations

Carbon nanotube composite coating of neural microelectrodes preferentially improves the multiunit signal-to-noise ratio

Extracellular metal microelectrodes are widely used to record single neuron activity in vivo. However, their signal-to-noise ratio (SNR) is often far from optimal due to their high impedance value. It has been recently reported that carbon nanotube (CNT) coatings may decrease microelectrode impedance, thus improving their performance. To tease out the different contributions to SNR of CNT-coated microelectrodes we carried out impedance and noise spectroscopy measurements of platinum/tungsten microelectrodes coated with a polypyrrole-CNT composite. Neuronal signals were recorded in vivo from rat cortex by employing tetrodes with two recording sites coated with polypyrrole-CNT and the remaining two left untreated. We found that polypyrrole-CNT coating significantly reduced the microelectrode impedance at all neuronal signal frequencies (from 1 to 10 000 Hz) and induced a significant improvement of the SNR, up to fourfold on average, in the 150-1500 Hz frequency range, largely corresponding to the multiunit frequency band. An equivalent circuit, previously proposed for porous conducting polymer coatings, reproduced the impedance spectra of our coated electrodes but could not explain the frequency dependence of SNR improvement following polypyrrole-CNT coating. This implies that neither the neural signal amplitude, as recorded by a CNT-coated metal microelectrode, nor noise can be fully described by the equivalent circuit model we used here and suggests that a more detailed approach may be needed to better understand the signal propagation at the electrode-solution interface. Finally, the presence of significant noise components that are neither thermal nor electronic makes it difficult to establish a direct relationship between the actual electrode noise and the impedance spectra

Experimental setup of the dynamic neural interface.

<p>We placed two 16-channel microwire arrays (<i>recording</i> and <i>stimulating</i> arrays) in the vibrissa motor (M1) and sensory areas (S1) of a rat brain cortex. (A) In this example 4 electrical stimulation patterns are set by specifying the pair of electrodes in the 16-channel microwire stimulating array placed in area S1. (B) The activity of a small population of single neurons (11 in this illustration) of area M1 is recorded in response to each electrical stimulation pattern. The activity of each neuron is plotted on a row over a rectangular frame, whose color indicates the correspondence with a stimulation pattern. (C) The motor interface generates a force vector from the first two principal components of the response of the M1 neurons. (D) The obtained force vector is applied to a simulated point-mass moving in a viscous medium. The interaction with such dynamical system aims to emulate a reaching movement creating a convergent force field similar to the force fields observed during microstimulation of the spinal gray matter. (E) The sensory interface maps each point in the field into the corresponding stimulation pattern.</p

Recorded neural activities in M1 evoked by electrical stimulation in S1.

<p>At the beginning of each experimental session a series of electrical stimulation patterns is delivered and a sorting procedure is performed on the raw neural signal to identify both the stimulation artifacts and the single unit activities. Panels (A) and (B) show a portion of a raw signal close to a stimulation event. The sorting procedure is able of identifying the stimulus artifacts (red lines) and the spike occurrences (green lines). Panel (C) shows the unit templates used by the sorting algorithm (left) and a representation of the sorted data onto the first two principal components plane (right). (D) Post Stimulus Time Histograms (PSTH) of neural evoked responses of a subset of three neurons selected from three experiments. The color code represents different stimulation patterns. (E) Scatter plot of variance vs. mean of spike counts (computed in sliding 20 ms long post-stimulus windows) of all pooled data points across units and sessions. This measure is a relatively standard measure of cortical response variability. The best-fit power law curve ( with α = 0.7 and β = 0.93) is plotted with the best fit parameters. These data are at the most reliable end of the range of response variability reported in the cortical literature. (F) CO stained section (AP = −3.3 mm from bregma) of the rat brain with microelectrode track. The black dotted line indicates the boundary of the barrel. The perpendicular length from the tip of the electrodes (the center of the hole) to the cortical surface measured 730 µm.</p