Experience-Dependent Response Plasticity in the Auditory Cortex: Issues, Characteristics, Mechanisms, and Functions

Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks

Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target feature extraction and recognition using ship-radiated noise. In this paper, firstly, taking the one-dimensional time-domain raw signal of the ship as the input of the model, a new deep neural network model for underwater target recognition is proposed. Depthwise separable convolution and time-dilated convolution are used for passive underwater acoustic target recognition for the first time. The proposed model realizes automatic feature extraction from the raw data of ship radiated noise and temporal attention in the process of underwater target recognition. Secondly, the measured data are used to evaluate the model, and cluster analysis and visualization analysis are performed based on the features extracted from the model. The results show that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Furthermore, the cross-folding model is used to verify that there is no overfitting in the model, which improves the generalization ability of the model. Finally, the model is compared with traditional underwater acoustic target recognition, and its accuracy is significantly improved by 6.8%.

A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition

Underwater acoustic target recognition (UATR) using ship-radiated noise faces big challenges due to the complex marine environment. In this paper, inspired by neural mechanisms of auditory perception, a new end-to-end deep neural network named auditory perception inspired Deep Convolutional Neural Network (ADCNN) is proposed for UATR. In the ADCNN model, inspired by the frequency component perception neural mechanism, a bank of multi-scale deep convolution filters are designed to decompose raw time domain signal into signals with different frequency components. Inspired by the plasticity neural mechanism, the parameters of the deep convolution filters are initialized randomly, and the is n learned and optimized for UATR. The n, max-pooling layers and fully connected layers extract features from each decomposed signal. Finally, in fusion layers, features from each decomposed signal are merged and deep feature representations are extracted to classify underwater acoustic targets. The ADCNN model simulates the deep acoustic information processing structure of the auditory system. Experimental results show that the proposed model can decompose, model and classify ship-radiated noise signals efficiently. It achieves a classification accuracy of 81.96%, which is the highest in the contrast experiments. The experimental results show that auditory perception inspired deep learning method has encouraging potential to improve the classification performance of UATR.

Synthetic consciousness: the distributed adaptive control perspective

Understanding the nature of consciousness is one of the grand outstanding scientific challenges. The fundamental methodological problem is how phenomenal first person experience can be accounted for in a third person verifiable form, while the conceptual challenge is to both define its function and physical realization. The distributed adaptive control theory of consciousness (DACtoc) proposes answers to these three challenges. The methodological challenge is answered relative to the hard problem and DACtoc proposes that it can be addressed using a convergent synthetic methodology using the analysis of synthetic biologically grounded agents, or quale parsing. DACtoc hypothesizes that consciousness in both its primary and secondary forms serves the ability to deal with the hidden states of the world and emerged during the Cambrian period, affording stable multi-agent environments to emerge. The process of consciousness is an autonomous virtualization memory, which serializes and unifies the parallel and subconscious simulations of the hidden states of the world that are largely due to other agents and the self with the objective to extract norms. These norms are in turn projected as value onto the parallel simulation and control systems that are driving action. This functional hypothesis is mapped onto the brainstem, midbrain and the thalamo-cortical and cortico-cortical systems and analysed with respect to our understanding of deficits of consciousness. Subsequently, some of the implications and predictions of DACtoc are outlined, in particular, the prediction that normative bootstrapping of conscious agents is predicated on an intentionality prior. In the view advanced here, human consciousness constitutes the ultimate evolutionary transition by allowing agents to become autonomous with respect to their evolutionary priors leading to a post-biological Anthropocene.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

New perspectives on the auditory cortex: learning and memory

Primary ("early") sensory cortices have been viewed as stimulus analyzers devoid of function in learning, memory, and cognition. However, studies combining sensory neurophysiology and learning protocols have revealed that associative learning systematically modifies the encoding of stimulus dimensions in the primary auditory cortex (A1) to accentuate behaviorally important sounds. This "representational plasticity" (RP) is manifest at different levels. The sensitivity and selectivity of signal tones increase near threshold, tuning above threshold shifts toward the frequency of acoustic signals, and their area of representation can increase within the tonotopic map of A1. The magnitude of area gain encodes the level of behavioral stimulus importance and serves as a substrate of memory strength. RP has the same characteristics as behavioral memory: it is associative, specific, develops rapidly, consolidates, and can last indefinitely. Pairing tone with stimulation of the cholinergic nucleus basalis induces RP and implants specific behavioral memory, while directly increasing the representational area of a tone in A1 produces matching behavioral memory. Thus, RP satisfies key criteria for serving as a substrate of auditory memory. The findings suggest a basis for posttraumatic stress disorder in abnormally augmented cortical representations and emphasize the need for a new model of the cerebral cortex.

Learning strategy determines auditory cortical plasticity

Learning modifies the primary auditory cortex (A1) to emphasize the processing and representation of behaviorally relevant sounds. However, the factors that determine cortical plasticity are poorly understood. While the type and amount of learning are assumed to be important, the actual strategies used to solve learning problems might be critical. To investigate this possibility, we trained two groups of adult male Sprague-Dawley rats to bar-press (BP) for water contingent on the presence of a 5.0 kHz tone using two different strategies: BP during tone presence or BP from tone-onset until receiving an error signal after tone cessation. Both groups achieved the same high levels of correct performance and both groups revealed equivalent learning of absolute frequency during training. Post-training terminal "mapping" of A1 showed no change in representational area of the tone signal frequency but revealed other substantial cue-specific plasticity that developed only in the tone-onset-to-error strategy group. Threshold was decreased approximately 10 dB and tuning bandwidth was narrowed by approximately 0.7 octaves. As sound onsets have greater perceptual weighting and cortical discharge efficacy than continual sound presence, the induction of specific learning-induced cortical plasticity may depend on the use of learning strategies that best exploit cortical proclivities. The present results also suggest a general principle for the induction and storage of plasticity in learning, viz., that the representation of specific acquired information may be selected by neurons according to a match between behaviorally selected stimulus features and circuit/network response properties.

Associative representational plasticity in the auditory cortex: a synthesis of two disciplines

Historically, sensory systems have been largely ignored as potential loci of information storage in the neurobiology of learning and memory. They continued to be relegated to the role of "sensory analyzers" despite consistent findings of associatively induced enhancement of responses in primary sensory cortices to behaviorally important signal stimuli, such as conditioned stimuli (CS), during classical conditioning. This disregard may have been promoted by the fact that the brain was interrogated using only one or two stimuli, e.g., a CS(+) sometimes with a CS(-), providing little insight into the specificity of neural plasticity. This review describes a novel approach that synthesizes the basic experimental designs of the experimental psychology of learning with that of sensory neurophysiology. By probing the brain with a large stimulus set before and after learning, this unified method has revealed that associative processes produce highly specific changes in the receptive fields of cells in the primary auditory cortex (A1). This associative representational plasticity (ARP) selectively facilitates responses to tonal CSs at the expense of other frequencies, producing tuning shifts toward and to the CS and expanded representation of CS frequencies in the tonotopic map of A1. ARPs have the major characteristics of associative memory: They are highly specific, discriminative, rapidly acquired, exhibit consolidation over hours and days, and can be retained indefinitely. Evidence to date suggests that ARPs encode the level of acquired behavioral importance of stimuli. The nucleus basalis cholinergic system is sufficient both for the induction of ARPs and the induction of specific auditory memory. Investigation of ARPs has attracted workers with diverse backgrounds, often resulting in behavioral approaches that yield data that are difficult to interpret. The advantages of studying associative representational plasticity are emphasized, as is the need for greater behavioral sophistication.

Listening experience with iterated rippled noise alters the perception of 'pitch' strength of complex sounds in the chinchilla

Behavioral responses obtained from chinchillas trained to discriminate a cosine-phase harmonic tone complex from wideband noise indicate that the perception of 'pitch' strength in chinchillas is largely influenced by periodicity information in the stimulus envelope. The perception of 'pitch' strength was examined in chinchillas in a stimulus generalization paradigm after animals had been retrained to discriminate infinitely iterated rippled noise from wideband noise. Retrained chinchillas gave larger behavioral responses to test stimuli having strong fine structure periodicity, but weak envelope periodicity. That is, chinchillas learn to use the information in the fine structure and consequently, their perception of 'pitch' strength is altered. Behavioral responses to rippled noises having similar periodicity strengths, but large spectral differences were also tested. Responses to these rippled noises were similar, suggesting a temporal analysis can be used to account for the behavior. Animals were then retested using the cosine-phase harmonic tone complex as the expected signal stimulus. Generalization gradients returned to those obtained originally in the naïve condition, suggesting that chinchillas do not remain "fine structure listeners," but rather revert back to being "envelope listeners" when the periodicity strength in the envelope of the expected stimulus is high.

CS-specific gamma, theta, and alpha EEG activity detected in stimulus generalization following induction of behavioral memory by stimulation of the nucleus basalis

Tone paired with stimulation of the nucleus basalis (NB) induces behavioral memory that is specific to the frequency of the conditioned stimulus (CS), assessed by cardiac and respiration behavior during post-training stimulus generalization testing. This paper focuses on CS-specific spectral and temporal features of conditioned EEG activation. Adult male Sprague-Dawley rats, chronically implanted with a stimulating electrode in the NB and a recording electrode in the ipsilateral auditory cortex, received either tone (6kHz, 70dB, 2s) paired with co-terminating stimulation of the nucleus basalis (0.2s, 100Hz, 80-105 microA, ITI approximately 45s) or unpaired presentation of the stimuli (approximately 200 trials/day for approximately 14 days). CS-specificity was tested 24h post-training by presenting test tones to obtain generalization gradients for the EEG, heart rate, and respiration. Behavioral memory was evident in cardiac and respiratory responses that were maximal to the CS frequency of 6kHz. FFT analyses of tone-elicited changes of power in the delta, theta, alpha, beta1, beta2, and gamma bands in the paired group revealed that conditioned EEG activation (shift from lower to higher frequencies) was differentially spectrally and temporally specific: theta, and alpha to a lesser extent, decreased selectively to 6kHz during and for several seconds following tone presentation while gamma power increased transiently during and after 6kHz. Delta exhibited no CS-specificity and the beta bands showed transient specificity only after several seconds. The unpaired group exhibited neither CS-specific behavioral nor EEG effects. Thus, stimulus generalization tests reveal that conditioned EEG activation is not unitary but rather reflects CS-specificity, with band-selective markers for specific, associative neural processes in learning and memory.