Neural representations of the content and production of human vocalization

Speech, as the spoken form of language, is fundamental for human communication. The phenomenon of covert inner speech implies functional independence of speech content and motor production. However, it remains unclear how a flexible mapping between speech content and production is achieved on the neural level. To address this, we recorded magnetoencephalography in humans performing a rule-based vocalization task. On each trial, vocalization content (one of two vowels) and production form (overt or covert) were instructed independently. Using multivariate pattern analysis, we found robust neural information about vocalization content and production, mostly originating from speech areas of the left hemisphere. Production signals dynamically transformed upon presentation of the content cue, whereas content signals remained largely stable throughout the trial. In sum, our results show dissociable neural representations of vocalization content and production in the human brain and provide insights into the neural dynamics underlying human vocalization.

Prenatal cyanuric acid exposure induced spatial learning impairments associated with alteration of acetylcholine-mediated neural information flow at the hippocampal CA3-CA1 synapses of male rats

Cyanuric acid (CA) is reported to induce nephrotoxicity but its toxic effect is not fully known. Prenatal CA exposure causes neurodevelopmental deficits and abnormal behavior in spatial learning ability. Dysfunction of the acetyl-cholinergic system in neural information processing is correlated with spatial learning impairment and was found in the previous reports of CA structural analogue melamine. To further investigate the neurotoxic effects and the potential mechanism, the acetylcholine (ACh) level was detected in the rats which were exposed to CA during the whole of gestation. Local field potentials (LFPs) were recorded when rats infused with ACh or cholinergic receptor agonist into hippocampal CA3 or CA1 region were trained in the Y-maze task. We found the expression of ACh in the hippocampus was significantly reduced in dose-dependent manners. Intra-hippocampal infusion of ACh into the CA1 but not the CA3 region could effectively mitigate learning deficits induced by CA exposure. However, activation of cholinergic receptors did not rescue the learning impairments. In the LFP recording, we found that the hippocampal ACh infusions could enhance the values of phase synchronization between CA3 and CA1 regions in theta and alpha oscillations. Meanwhile, the reduction in the coupling directional index and the strength of CA3 driving CA1 in the CA-treated groups was also reversed by the ACh infusions. Our findings are consistent with the hypothesis and provide the first evidence that prenatal CA exposure induced spatial learning defect is attributed to the weakened ACh-mediated neuronal coupling and NIF in the CA3-CA1 pathway.

Towards Generalizing the Information Theory for Neural Communication

Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes' timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon's idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory.

Cerebellar physiology: links between microcircuitry properties and sensorimotor functions

Existing knowledge of the cerebellar microcircuitry structure and physiology allows a rather detailed description of what it in itself can and cannot do. Combined with a known mapping of different cerebellar regions to afferent systems and motor output target structures, there are several constraints that can be used to describe how specific components of the cerebellar microcircuitry may work during sensorimotor control. In fact, as described in this review, the major factor that hampers further progress in understanding cerebellar function is the limited insights into the circuitry-level function of the targeted motor output systems and the nature of the information in the mossy fiber afferents. The cerebellar circuitry in itself is here summarized as a gigantic associative memory element, primarily consisting of the parallel fiber synapses, whereas most other circuitry components, including the climbing fiber system, primarily has the role of maintaining activity balance in the intracerebellar and extracerebellar circuitry. The review explores the consistency of this novel interpretational framework with multiple diverse observations at the synaptic and microcircuitry level within the cerebellum.

Subthalamic deep brain stimulation reduces pathological information transmission to the thalamus in a rat model of parkinsonism

The degeneration of dopaminergic neurons in the substantia nigra pars compacta leads to parkinsonian motor symptoms via changes in electrophysiological activity throughout the basal ganglia. High-frequency deep brain stimulation (DBS) partially treats these symptoms, but the mechanisms are unclear. We hypothesize that motor symptoms of Parkinson’s disease (PD) are associated with increased information transmission from basal ganglia output neurons to motor thalamus input neurons and that therapeutic DBS of the subthalamic nucleus (STN) treats these symptoms by reducing this extraneous information transmission. We tested these hypotheses in a unilateral, 6-hydroxydopamine-lesioned rodent model of hemiparkinsonism. Information transfer between basal ganglia output neurons and motor thalamus input neurons increased in both the orthodromic and antidromic directions with hemiparkinsonian (hPD) onset, and these changes were reversed by behaviorally therapeutic STN-DBS. Omnidirectional information increases in the parkinsonian state underscore the detrimental nature of that pathological information and suggest a loss of information channel independence. Therapeutic STN-DBS reduced that pathological information, suggesting an effective increase in the number of independent information channels. We interpret these data with a model in which pathological information and fewer information channels diminishes the scope of possible motor activities, driving parkinsonian symptoms. In this model, STN-DBS restores information-channel independence by eliminating or masking the parkinsonism-associated information, and thus enlarges the scope of possible motor activities, alleviating parkinsonian symptoms.

Estimating Neuronal Information: Logarithmic Binning of Neuronal Inter-Spike Intervals

Neurons communicate via the relative timing of all-or-none biophysical signals called spikes. For statistical analysis, the time between spikes can be accumulated into inter-spike interval histograms. Information theoretic measures have been estimated from these histograms to assess how information varies across organisms, neural systems, and disease conditions. Because neurons are computational units that, to the extent they process time, work not by discrete clock ticks but by the exponential decays of numerous intrinsic variables, we propose that neuronal information measures scale more naturally with the logarithm of time. For the types of inter-spike interval distributions that best describe neuronal activity, the logarithm of time enables fewer bins to capture the salient features of the distributions. Thus, discretizing the logarithm of inter-spike intervals, as compared to the inter-spike intervals themselves, yields histograms that enable more accurate entropy and information estimates for fewer bins and less data. Additionally, as distribution parameters vary, the entropy and information calculated from the logarithm of the inter-spike intervals are substantially better behaved, e.g., entropy is independent of mean rate, and information is equally affected by rate gains and divisions. Thus, when compiling neuronal data for subsequent information analysis, the logarithm of the inter-spike intervals is preferred, over the untransformed inter-spike intervals, because it yields better information estimates and is likely more similar to the construction used by nature herself.