Localist versus distributed representation of sounds in the auditory cortex controlled by distinct inhibitory neuronal subtypes.

Cortical neuronal populations can use a multitude of codes to represent information, each with different advantages and trade-offs. The auditory cortex represents sounds via a sparse code, which lies on the continuum between a localist representation with different cells responding to different sounds, and a distributed representation, in which each sound is encoded in the relative response of each cell in the population. Being able to dynamically shift the neuronal code along this axis may help with a variety of tasks that require categorical or invariant representations. Cortical circuits contain multiple types of inhibitory neurons which shape how information is processed within neuronal networks. Here, we asked whether somatostatin-expressing (SST) and vasoactive intestinal peptide-expressing (VIP) inhibitory neurons may have distinct effects on population neuronal codes, differentially shifting the encoding of sounds between distributed and localist representations. We stimulated optogenetically SST or VIP neurons while simultaneously measuring the response of populations of hundreds of neurons to sounds presented at different sound pressure levels. SST activation shifted the neuronal population responses toward a more localist code, whereas VIP activation shifted them towards a more distributed code. Upon SST activation, sound representations became more discrete, relying on cell identity rather than strength. In contrast, upon VIP activation, distinct sounds activated overlapping populations at different rates. These shifts were implemented at the single-cell level by modulating the response-level curve of monotonic and nonmonotonic neurons. These results suggest a novel function for distinct inhibitory neurons in the auditory cortex in dynamically controlling cortical population codes.

Generalizing neural signal-to-text brain-computer interfaces.

Objective:Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces. The goal of this study is to evaluate the BCI performance of a robust speech decoding system that translates neural signals evoked by speech to a textual output. While previous studies have approached this problem by using neural signals to choose from a limited set of possible words, we employ a more general model that can type any word from a large corpus of English text.Approach:In this study, we create an end-to-end BCI that translates neural signals associated with overt speech into text output. Our decoding system first isolates frequency bands in the input depth-electrode signal encapsulating differential information regarding production of various phonemic classes. These bands form a feature set that then feeds into a Long Short-Term Memory (LSTM) model which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, a particle filtering algorithm temporally smooths these probabilities by incorporating prior knowledge of the English language to output text corresponding to the decoded word. The generalizability of our decoder is driven by the lack of a vocabulary constraint on this output word.Main result:This method was evaluated using a dataset of 6 neurosurgical patients implanted with intra-cranial depth electrodes to identify seizure foci for potential surgical treatment of epilepsy. We averaged 32% word accuracy and on the phoneme-level obtained 46% precision, 51% recall and 73.32% average phoneme error rate while also achieving significant increases in speed when compared to several other BCI approaches.Significance:Our study employs a more general neural signal-to-text model which could facilitate communication by patients in everyday environments.

Noise-induced distortion of the mean limit cycle of nonlinear oscillators.

We study the change in the size and shape of the mean limit cycle of a stochastically driven nonlinear oscillator as a function of noise amplitude. Such dynamics occur in a variety of nonequilibrium systems, including the spontaneous oscillations of hair cells of the inner ear. The noise-induced distortion of the limit cycle generically leads to its rounding through the elimination of sharp (high-curvature) features through a process we call corner cutting. We provide a criterion that may be used to identify limit cycle regions most susceptible to such noise-induced distortions. By using this criterion, one may obtain more meaningful parametric fits of nonlinear dynamical models from noisy experimental data, such as those coming from spontaneously oscillating hair cells.

Nonequilibrium limit-cycle oscillators: Fluctuations in hair bundle dynamics.

We develop a framework for the general interpretation of the stochastic dynamical system near a limit cycle. Such quasiperiodic dynamics are commonly found in a variety of nonequilibrium systems, including the spontaneous oscillations of hair cells of the inner ear. We demonstrate quite generally that in the presence of noise, the phase of the limit cycle oscillator will diffuse, while deviations in the directions locally orthogonal to that limit cycle will display the Lorentzian power spectrum of a damped oscillator. We identify two mechanisms by which these stochastic dynamics can acquire a complex frequency dependence and discuss the deformation of the mean limit cycle as a function of temperature. The theoretical ideas are applied to data obtained from spontaneously oscillating hair cells of the amphibian sacculus.