Dual Coding of Frequency Modulation in the Ventral Cochlear Nucleus.

Frequency modulation (FM) is a common acoustic feature of natural sounds and is known to play a role in robust sound source recognition. Auditory neurons show precise stimulus-synchronized discharge patterns that may be used for the representation of low-rate FM. However, it remains unclear whether this representation is based on synchronization to slow temporal envelope (ENV) cues resulting from cochlear filtering or phase locking to faster temporal fine structure (TFS) cues. To investigate the plausibility of those encoding schemes, single units of the ventral cochlear nucleus of guinea pigs of either sex were recorded in response to sine FM tones centered at the unit's best frequency (BF). The results show that, in contrast to high-BF units, for modulation depths within the receptive field, low-BF units (<4 kHz) demonstrate good phase locking to TFS. For modulation depths extending beyond the receptive field, the discharge patterns follow the ENV and fluctuate at the modulation rate. The receptive field proved to be a good predictor of the ENV responses for most primary-like and chopper units. The current in vivo data also reveal a high level of diversity in responses across unit types. TFS cues are mainly conveyed by low-frequency and primary-like units and ENV cues by chopper and onset units. The diversity of responses exhibited by cochlear nucleus neurons provides a neural basis for a dual-coding scheme of FM in the brainstem based on both ENV and TFS cues.SIGNIFICANCE STATEMENT Natural sounds, including speech, convey informative temporal modulations in frequency. Understanding how the auditory system represents those frequency modulations (FM) has important implications as robust sound source recognition depends crucially on the reception of low-rate FM cues. Here, we recorded 115 single-unit responses from the ventral cochlear nucleus in response to FM and provide the first physiological evidence of a dual-coding mechanism of FM via synchronization to temporal envelope cues and phase locking to temporal fine structure cues. We also demonstrate a diversity of neural responses with different coding specializations. These results support the dual-coding scheme proposed by psychophysicists to account for FM sensitivity in humans and provide new insights on how this might be implemented in the early stages of the auditory pathway

Connectivity motifs of inhibitory neurons in the mouse Auditory Cortex

Connectivity determines the function of neural circuits and it is the gateway to behavioral output. The emergent properties of the Auditory Cortex (ACx) have been difficult to unravel partly due to our assumption that it is organized similarly to other sensory areas. But detailed investigations of its functional connectivity have begun to reveal significant differences from other cortical areas that perform different functions. Using Laser Scanning Photostimulation we previously discovered unique circuit features in the ACx. Specifically, we found that the functional asymmetry of the ACx (tonotopy and isofrequency axes) is reflected in the local circuitry of excitatory inputs to Layer 3 pyramidal neurons. In the present study we extend the functional wiring diagram of the ACx with an investigation of the connectivity patterns of inhibitory subclasses. We compared excitatory input to parvalbumin (PV) and somatostatin (SOM)-expressing interneurons and found distinct circuit-motifs between and within these subpopulations. Moreover, these connectivity motifs emerged as intrinsic differences between the left and right ACx. Our results support a functional circuit based approach to understand the role of inhibitory neurons in auditory processing

An Efficient Coding Hypothesis Links Sparsity and Selectivity of Neural Responses

To what extent are sensory responses in the brain compatible with first-order principles? The efficient coding hypothesis projects that neurons use as few spikes as possible to faithfully represent natural stimuli. However, many sparsely firing neurons in higher brain areas seem to violate this hypothesis in that they respond more to familiar stimuli than to nonfamiliar stimuli. We reconcile this discrepancy by showing that efficient sensory responses give rise to stimulus selectivity that depends on the stimulus-independent firing threshold and the balance between excitatory and inhibitory inputs. We construct a cost function that enforces minimal firing rates in model neurons by linearly punishing suprathreshold synaptic currents. By contrast, subthreshold currents are punished quadratically, which allows us to optimally reconstruct sensory inputs from elicited responses. We train synaptic currents on many renditions of a particular bird's own song (BOS) and few renditions of conspecific birds' songs (CONs). During training, model neurons develop a response selectivity with complex dependence on the firing threshold. At low thresholds, they fire densely and prefer CON and the reverse BOS (REV) over BOS. However, at high thresholds or when hyperpolarized, they fire sparsely and prefer BOS over REV and over CON. Based on this selectivity reversal, our model suggests that preference for a highly familiar stimulus corresponds to a high-threshold or strong-inhibition regime of an efficient coding strategy. Our findings apply to songbird mirror neurons, and in general, they suggest that the brain may be endowed with simple mechanisms to rapidly change selectivity of neural responses to focus sensory processing on either familiar or nonfamiliar stimuli. In summary, we find support for the efficient coding hypothesis and provide new insights into the interplay between the sparsity and selectivity of neural responses

Layer specific differences in the mouse auditory corticocollicular system

Recent data suggest that there may be distinct processing streams emanating from auditory cortical layer 5 and layer 6 that influence the auditory midbrain. To examine the functional properties of neurons in these two layers that project to the midbrain, we performed three sets of experiments. First, to determine whether these projections have different physiological properties, we injected rhodamine-tagged latex tracer beads into the inferior colliculus (IC) of >30 day old mice to label these corticofugal cells. Whole-cell recordings were performed on 62 labeled cells to determine their basic electrophysiological properties and cells were filled with biocytin to determine their morphological characteristics. We observed that layer 5 auditory corticocollicular cells have prominent Ih-mediated sag and rebound currents, generate calcium-dependent rhythmic bursts, and have relatively sluggish time constants. In contrast, layer 6 auditory corticocollicular cells are non-bursting, do not demonstrate sag or rebound currents, and have short time constants. Quantitative (Sholl) analysis of morphology showed that layer 6 cells are smaller, have a horizontal orientation, and have very long dendrites (> 500μm) that branch profusely both near the soma and distally near the pia. Layer 5 corticocollicular cells are large pyramidal cells with a long apical dendrite with most of the branching near the pial surface. The marked differences in physiological properties and dendritic arborization between neurons in layer 5 and layer 6 make it likely that each type plays a distinct role in controlling auditory information processing in the midbrain. Very little is currently known about the nature of the inputs from the rest of the auditory cortex onto these cells. Therefore, our second set of experiments was designed to investigate these local inputs. To do this, we utilized laser photo-uncaging of glutamate to stimulate the cells that synapse onto the layer 5 and layer 6 corticocollicular cells in brain slices taken from adult mice. Pre-identified cells were recorded in a whole cell patch configuration then stimulated with a larger grid covering the area from the white matter to the pia. To isolate synaptic responses, in this preparation, we used a low calcium artificial cerebral spinal fluid method which uses a physiological method of parsing out synaptic versus direct stimulation. We contrast this method with the more commonly used time window method. In identified layer 5 and layer 6 corticocollicular recordings, cells show spatial differences in their respective input maps. Layer 5 corticocollicular neurons were shown to receive inputs coming from various layers compared to layer 6 corticocollicular neurons which almost exclusively receives input from layer 6. Combined with our first set of studies, which showed that layer 5 and layer 6 corticocollicular neurons have different electrophysiological properties, the current data suggest that neurons in these two layers play different roles in modifying ascending information at the IC. These differences may explain the varied results seen in the inferior colliculus during in vivo stimulation of the auditory cortex. In sensory cortices, layer 4 is generally considered to be the primary thalamorecipient layer. Recent data, however, have shown that other layers receive thalamic input. With many of the direct inputs of these thalamocortical collaterals onto inhibitory interneurons. Therefore, in our third set of studies, to investigate these inputs, we used a laser to stimulate thalamocortical axons which have been labeled with channelrhodopsin. Pre-identified corticocollicular cells were recorded in a whole cell patch configuration then stimulated with a larger grid covering the area from the white matter to the pia. We compared the excitatory and inhibitory input from the thalamus in both layer 5 and layer 6 corticocollicular neurons. We found that similar to previous results, layer 5 corticocollicular neurons received more input in general than layer 6. However, interestingly, there are both layer 5 and layer 6 corticocollicular neurons that receive considerable thalamic inputs compared to those assessed with glutamate uncaging. It is likely that the coordinated stimulation of these afferents cause spatial summation of input compared to the very focal nature inherent to the methods in glutamate uncaging

Seeing sound: a new way to illustrate auditory objects and their neural correlates

This thesis develops a new method for time-frequency signal processing and examines the relevance of the new representation in studies of neural coding in songbirds. The method groups together associated regions of the time-frequency plane into objects defined by time-frequency contours. By combining information about structurally stable contour shapes over multiple time-scales and angles, a signal decomposition is produced that distributes resolution adaptively. As a result, distinct signal components are represented in their own most parsimonious forms.  Next, through neural recordings in singing birds, it was found that activity in song premotor cortex is significantly correlated with the objects defined by this new representation of sound. In this process, an automated way of finding sub-syllable acoustic transitions in birdsongs was first developed, and then increased spiking probability was found at the boundaries of these acoustic transitions. Finally, a new approach to study auditory cortical sequence processing more generally is proposed. In this approach, songbirds were trained to discriminate Morse-code-like sequences of clicks, and the neural correlates of this behavior were examined in primary and secondary auditory cortex. It was found that a distinct transformation of auditory responses to the sequences of clicks exists as information transferred from primary to secondary auditory areas. Neurons in secondary auditory areas respond asynchronously and selectively -- in a manner that depends on the temporal context of the click. This transformation from a temporal to a spatial representation of sound provides a possible basis for the songbird's natural ability to discriminate complex temporal sequences

Neuronal Auditory Machine Intelligence (NEURO-AMI) In Perspective

The recent developments in soft computing cannot be complete without noting the contributions of artificial neural machine learning systems that draw inspiration from real cortical tissue or processes that occur in human brain. The universal approximability of such neural systems has led to its wide spread use, and novel developments in this evolving technology has shown that there is a bright future for such Artificial Intelligent (AI) techniques in the soft computing field. Indeed, the proliferation of large and very deep networks of artificial neural systems and the corresponding enhancement and development of neural machine learning algorithms have contributed immensely to the development of the modern field of Deep Learning as may be found in the well documented research works of Lecun, Bengio and Hinton. However, the key requirements of end user affordability in addition to reduced complexity and reduced data learning size requirement means there still remains a need for the synthesis of more cost-efficient and less data-hungry artificial neural systems. In this report, we present an overview of a new competing bio-inspired continual learning neural tool Neuronal Auditory Machine Intelligence (Neuro-AMI) as a predictor detailing its functional and structural details, important aspects on right applicability, some recent application use cases and future research directions for current and prospective machine learning experts and data scientists.Comment: Journal submission in progres