Cortical Neural Computation by Discrete Results Hypothesis

Procedure for extracting temporal structure embedded within psychophysical data

The idea that mental events unfold over time with an intrinsically paced regularity has a long history within experimental psychology, and it has gained traction from the actual measurement of brain rhythms evident in EEG signals recorded from the human brain and from direct recordings of action potentials and local field potentials within the nervous systems of nonhumans. The weak link in this idea, however, is the challenge of extracting signatures of this temporal structure from behavioral measures. Because there is nothing in the seamless stream of conscious awareness that belies rhythmic modulations in sensitivity or mental acuity, one must deploy inferential strategies for extracting evidence for the existence of temporal regularities in neural activity. We have devised a parametric procedure for analysis of temporal structure embedded in behaviorally measured data comprising durations. We confirm that this procedure, dubbed PATS, achieves comparable results to those obtained using spectral analysis, and that it outperforms conventional spectral analysis when analyzing human response time data containing just a few hundred data points per condition. PATS offers an efficient, sensitive means for bridging the gap between oscillations identified neurophysiologically and estimates of rhythmicity embedded within durations measured behaviorally.

Cellular and Network Mechanisms for Temporal Signal Propagation in a Cortical Network Model

The mechanisms underlying an effective propagation of high intensity information over a background of irregular firing and response latency in cognitive processes remain unclear. Here we propose a SSCCPI circuit to address this issue. We hypothesize that when a high-intensity thalamic input triggers synchronous spike events (SSEs), dense spikes are scattered to many receiving neurons within a cortical column in layer IV, many sparse spike trains are propagated in parallel along minicolumns at a substantially high speed and finally integrated into an output spike train toward or in layer Va. We derive the sufficient conditions for an effective (fast, reliable, and precise) SSCCPI circuit: (i) SSEs are asynchronous (near synchronous); (ii) cortical columns prevent both repeatedly triggering SSEs and incorrectly synaptic connections between adjacent columns; and (iii) the propagator in interneurons is temporally complete fidelity and reliable. We encode the membrane potential responses to stimuli using the non-linear autoregressive integrated process derived by applying Newton's second law to stochastic resilience systems. We introduce a multithreshold decoder to correct encoding errors. Evidence supporting an effective SSCCPI circuit includes that for the condition, (i) time delay enhances SSEs, suggesting that response latency induces SSEs in high-intensity stimuli; irregular firing causes asynchronous SSEs; asynchronous SSEs relate to healthy neurons; and rigorous SSEs relate to brain disorders. For the condition (ii) neurons within a given minicolumn are stereotypically interconnected in the vertical dimension, which prevents repeated triggering SSEs and ensures signal parallel propagation; columnar segregation avoids incorrect synaptic connections between adjacent columns; and signal propagation across layers overwhelmingly prefers columnar direction. For the condition (iii), accumulating experimental evidence supports temporal transfer precision with millisecond fidelity and reliability in interneurons; homeostasis supports a stable fixed-point encoder by regulating changes to synaptic size, synaptic strength, and ion channel function in the membrane; together all-or-none modulation, active backpropagation, additive effects of graded potentials, and response variability functionally support the multithreshold decoder; our simulations demonstrate that the encoder-decoder is temporally complete fidelity and reliable in special intervals contained within the stable fixed-point range. Hence, the SSCCPI circuit provides a possible mechanism of effective signal propagation in cortical networks.

Selective effects of isoflurane on cortico-cortical feedback afferent responses in murine non-primary neocortex

BACKGROUND

General anaesthetics affect loss of consciousness by disrupting information-passing and integration within thalamo-cortical (TC) networks. Feedback cortical connections that carry internally generated signals such as expectation and attention appear more sensitive to anaesthesia than feedforward signals. However, direct evidence for this effect in non-primary cortex is lacking. In addition, direct comparisons between TC core and matrix, and between cortico-cortical (CC) feedforward and feedback responses have not been reported.

METHODS

We investigated the disruption of synaptic responses by isoflurane of four distinct afferent pathways to non-primary neocortex. We independently activated TC core and matrix and reciprocal CC (feedforward and feedback) pathways using optogenetic techniques, and compared the relative sensitivity of synaptic responses to isoflurane.

RESULTS

Under control conditions, activation of axon terminals of all pathways evoked postsynaptic currents (recorded extracellularly) and postsynaptic potentials in pyramidal neurones. CC feedback responses were substantially more sensitive to isoflurane (0 to 0.53 mM) compared with TC core, TC matrix, or CC feedforward pathways.

CONCLUSION

Differential sensitivity of CC feedback synaptic responses to isoflurane in a clinically relevant range suggests a role for disruption of these afferents in the hypnotic effects of anaesthetic agents.

Understanding the Effects of General Anesthetics on Cortical Network Activity Using Ex Vivo Preparations

General anesthetics have been used to ablate consciousness during surgery for more than 150 yr. Despite significant advances in our understanding of their molecular-level pharmacologic effects, comparatively little is known about how anesthetics alter brain dynamics to cause unconsciousness. Consequently, while anesthesia practice is now routine and safe, there are many vagaries that remain unexplained. In this paper, the authors review the evidence that cortical network activity is particularly sensitive to general anesthetics, and suggest that disruption to communication in, and/or among, cortical brain regions is a common mechanism of anesthesia that ultimately produces loss of consciousness. The authors review data from acute brain slices and organotypic cultures showing that anesthetics with differing molecular mechanisms of action share in common the ability to impair neurophysiologic communication. While many questions remain, together, ex vivo and in vivo investigations suggest that a unified understanding of both clinical anesthesia and the neural basis of consciousness is attainable.

PV+ Cells Enhance Temporal Population Codes but not Stimulus-Related Timing in Auditory Cortex

Spatio-temporal cortical activity patterns relative to both peripheral input and local network activity carry information about stimulus identity and context. GABAergic interneurons are reported to regulate spiking at millisecond precision in response to sensory stimulation and during gamma oscillations; their role in regulating spike timing during induced network bursts is unclear. We investigated this issue in murine auditory thalamo-cortical (TC) brain slices, in which TC afferents induced network bursts similar to previous reports in vivo. Spike timing relative to TC afferent stimulation during bursts was poor in pyramidal cells and SOM+ interneurons. It was more precise in PV+ interneurons, consistent with their reported contribution to spiking precision in pyramidal cells. Optogenetic suppression of PV+ cells unexpectedly improved afferent-locked spike timing in pyramidal cells. In contrast, our evidence suggests that PV+ cells do regulate the spatio-temporal spike pattern of pyramidal cells during network bursts, whose organization is suited to ensemble coding of stimulus information. Simulations showed that suppressing PV+ cells reduces the capacity of pyramidal cell networks to produce discriminable spike patterns. By dissociating temporal precision with respect to a stimulus versus internal cortical activity, we identified a novel role for GABAergic cells in regulating information processing in cortical networks.