A. A. Kozhevnikov

An ultra-sparse code underlies the generation of neural sequences in a songbird

Sequences of motor activity are encoded in many vertebrate brains by complex spatio-temporal patterns of neural activity; however, the neural circuit mechanisms underlying the generation of these pre-motor patterns are poorly understood. In songbirds, one prominent site of pre-motor activity is the forebrain robust nucleus of the archistriatum (RA), which generates stereotyped sequences of spike bursts during song and recapitulates these sequences during sleep. We show that the stereotyped sequences in RA are driven from nucleus HVC (high vocal centre), the principal pre-motor input to RA. Recordings of identified HVC neurons in sleeping and singing birds show that individual HVC neurons projecting onto RA neurons produce bursts sparsely, at a single, precise time during the RA sequence. These HVC neurons burst sequentially with respect to one another. We suggest that at each time in the RA sequence, the ensemble of active RA neurons is driven by a subpopulation of RA-projecting HVC neurons that is active only at that time. As a population, these HVC neurons may form an explicit representation of time in the sequence. Such a sparse representation, a temporal analogue of the ‘grandmother cell’ concept for object recognition, eliminates the problem of temporal interference during sequence generation and learning attributed to more distributed representations.

A Compact Statistical Model of the Song Syntax in Bengalese Finch

Songs of many songbird species consist of variable sequences of a finite number of syllables. A common approach for characterizing the syntax of these complex syllable sequences is to use transition probabilities between the syllables. This is equivalent to the Markov model, in which each syllable is associated with one state, and the transition probabilities between the states do not depend on the state transition history. Here we analyze the song syntax in Bengalese finch. We show that the Markov model fails to capture the statistical properties of the syllable sequences. Instead, a state transition model that accurately describes the statistics of the syllable sequences includes adaptation of the self-transition probabilities when states are revisited consecutively, and allows associations of more than one state to a given syllable. Such a model does not increase the model complexity significantly. Mathematically, the model is a partially observable Markov model with adaptation (POMMA). The success of the POMMA supports the branching chain network model of how syntax is controlled within the premotor song nucleus HVC, but also suggests that adaptation and many-to-one mapping from the syllable-encoding chain networks in HVC to syllables should be included in the network model.

Phase Sensitive Shot Noise in an Andreev Interferometer

We investigate nonequilibrium noise in a diffusive Andreev interferometer, in which currents emerging from two normal metal/superconductor (N-S) interfaces can interfere. We observe a modulation of the shot noise when the phase difference between the two N-S interfaces is varied by a magnetic flux. This is the signature of phase-sensitive fluctuations in the normal metal. The effective charge inferred from the shot noise measurement is close to q(eff) = 2e but shows phase-dependent deviations from 2e at finite energy, which we interpret as being due to pair correlations. Experimental data are in good agreement with predictions based on an extended Keldysh Greens function approach.

Temperature Manipulation in Songbird Brain Implicates the Premotor Nucleus HVC in Birdsong Syntax

Variable motor sequences of animals are often structured and can be described by probabilistic transition rules between action elements. Examples include the songs of many songbird species such as the Bengalese finch, which consist of stereotypical syllables sequenced according to probabilistic rules (song syntax). The neural mechanisms behind such rules are poorly understood. Here, we investigate where the song syntax is encoded in the brain of the Bengalese finch by rapidly and reversibly manipulating the temperature in the song production pathway. Cooling the premotor nucleus HVC (proper name) slows down the song tempo, consistent with the idea that HVC controls moment-to-moment timings of acoustic features in the syllables. More importantly, cooling HVC alters the transition probabilities between syllables. Cooling HVC reduces the number of repetitions of long-repeated syllables and increases the randomness of syllable sequences. In contrast, cooling the downstream motor area RA (robust nucleus of the acropallium), which is critical for singing, does not affect the song syntax. Unilateral cooling of HVC shows that control of syllables is mostly lateralized to the left HVC, whereas transition probabilities between the syllables can be affected by cooling HVC in either hemisphere to varying degrees. These results show that HVC is a key site for encoding song syntax in the Bengalese finch. HVC is thus involved both in encoding timings within syllables and in sequencing probabilistic transitions between syllables. Our finding suggests that probabilistic selections and fine-grained timings of action elements can be integrated within the same neural circuits. SIGNIFICANCE STATEMENT Many animal behaviors such as birdsong consist of variable sequences of discrete actions. Where and how the probabilistic rules of such sequences are encoded in the brain is poorly understood. We locally and reversibly cooled brain areas in songbirds during singing. Mild cooling of area HVC in the Bengalese finch brain—a premotor area homologous to the mammalian premotor cortex—alters the statistics of the syllable sequences, suggesting that HVC is critical for birdsong sequences. HVC is also known for controlling moment-to-moment timings within syllables. Our results show that timing and probabilistic sequencing of actions can share the same neural circuits in local brain areas.

Wideband radio-frequency device for measurements of dielectric properties of small volumes of liquids

A wideband differential detector for high-sensitivity rf and microwave dielectric spectroscopy of liquids is described. The device is unique in that it has very high wideband sensitivity and requires extremely small (nanoliter range) volumes of the liquid. In addition, the device has good linearity and can be used for quantitative extraction of the frequency-dependent dielectric function e(ω) of extremely small volumes of liquids. Using this device, quantitative measurements of the dielectric function at frequencies from 50 MHz to 1.6 GHz are demonstrated for 200 nL volume samples of liquids. The detector may be used for wideband rf/microwave spectroscopic analysis of chemical and biological samples.

A simple miniature device for wireless stimulation of neural circuits in small behaving animals.

The use of wireless neural stimulation devices offers significant advantages for neural stimulation experiments in behaving animals. We demonstrate a simple, low-cost and extremely lightweight wireless neural stimulation device which is made from off-the-shelf components. The device has low power consumption and does not require a high-power RF preamplifier. Neural stimulation can be carried out in either a voltage source mode or a current source mode. Using the device, we carry out wireless stimulation in the premotor brain area HVC of a songbird and demonstrate that such stimulation causes rapid perturbations of the acoustic structure of the song.

Shot Noise Measurements in Diffusive Normal Metal-Superconductor (N-S) Junctions

We report on the measurements of non-equilibrium noise indiffusive normal metal-superconductor (N-S) junctions. Weobserve that at bias voltages less than the gap voltage theshot noise is doubled compared to the normal diffusiveconductor, in agreement with theoretical predictions. We alsoobserve that the crossover from the thermal to shot noiseoccurs at bias voltages smaller than for the normal conductor,in qualitative agreement with theory.

Radio Frequency-Single Electron Transistor: A Fast and Sensitive Electrometer Analogous to the Radio Frequency-Superconducting Quantum Interference Device

We describe a new mode of operation of the single electron transistor (SET). The so-called RF-SET (radio frequency-SET) is a dual of the RF-SQUID (radio frequency-superconducting quantum interference device). It has been operated at frequencies above 100 MHz with a very high charge sensitivity (1.2 × 10−5e/√Hz. The large bandwidth, combined with a high sensitivity, will enable studies of the dynamics of mesoscopic systems on very short time scales.