A new interpretation of thalamocortical circuitry

The discovery of the sub-threshold currents M and Q/H in central neurons

The history, content and consequences of the highly-cited 1982 Brain Research paper by Halliwell and Adams are summarized. The paper pioneered the use of the single-electrode voltage clamp in mammalian brain slices, described 2 novel sub-threshold voltage-dependent ionic currents, IM and IQ/H, and suggested that cholinergic inputs "enabled" pyramidal cell firing in response to conventional synaptic input, the first example of central neuromodulation. The paper, published in Brain Research to give the first author appropriate importance, heralded an ongoing tidal wave of quantitative electrophysiology in mammalian central neurons.

ORIGINAL ARTICLE ABSTRACT

Voltage-clamp analysis of muscarinic excitation in hippocampal neurons Pyramidal cells in the CA1 field of guinea pig hippocampal slices were voltage-clamped using a single microelectrode, at 23-30°C. Small inwardly relaxing currents triggered by step hyperpolarizations from holding potentials of -80 to -40mV were investigated. Inward relaxations occurring for negative steps between -40mV and -70mV resembled M-currents of sympathetic ganglion cells: they were abolished by addition of carbachol, muscarine or bethanechol, as well as by 1mM barium; the relaxations appeared to invert at around -80mV; they became faster at more negative potentials; and the inversion potential was shifted positively by raising external K(+) concentration. Inward relaxations triggered by steps negative to -80mV, in contrast, appeared to reflect passage of another current species, which has been labeled IQ.Thus IQ did not invert negative to -80mV, it was insensitive to muscarinic agonizts or to barium, and it was blocked by 0.5-3mM cesium (which does not block IM). Turn-on of IQ causes the well known droop in the hyperpolarizing electrotonic potential in these cells. The combined effects of IQ and IM make the steady-state current-voltage relation of CA1 cells slightly sigmoidal around rest potential. It is suggested that activation of cholinergic septal inputs to the hippocampus facilitates repetitive firing off pyramidal cells by turning off the M-conductance, without much change in the resting potential of the cell. © 1982. This article is part of a Special Issue entitled SI:50th Anniversary Issue.

Hebbian learning from higher-order correlations requires crosstalk minimization

Activity-dependent synaptic plasticity should be extremely connection specific, though experiments have shown it is not, and biophysics suggests it cannot be. Extreme specificity (near-zero "crosstalk") might be essential for unsupervised learning from higher-order correlations, especially when a neuron has many inputs. It is well known that a normalized nonlinear Hebbian rule can learn "unmixing" weights from inputs generated by linearly combining independently fluctuating nonGaussian sources using an orthogonal mixing matrix. We previously reported that even if the matrix is only approximately orthogonal, a nonlinear-specific Hebbian rule can usually learn almost correct unmixing weights (Cox and Adams in Front Comput Neurosci 3: doi: 10.3389/neuro.10.011.2009 2009). We also reported simulations that showed that as crosstalk increases from zero, the learned weight vector first moves slightly away from the crosstalk-free direction and then, at a sharp threshold level of inspecificity, jumps to a completely incorrect direction. Here, we report further numerical experiments that show that above this threshold, residual learning is driven instead almost entirely by second-order input correlations, as occurs using purely Gaussian sources or a linear rule, and any amount of crosstalk. Thus, in this "ICA" model learning from higher-order correlations, required for unmixing, requires high specificity. We compare our results with a recent mathematical analysis of the effect of crosstalk for exactly orthogonal mixing, which revealed that a second, even lower, threshold, exists below which successful learning is impossible unless weights happen to start close to the correct direction. Our simulations show that this also holds when the mixing is not exactly orthogonal. These results suggest that if the brain uses simple Hebbian learning, it must operate with extraordinarily accurate synaptic plasticity to ensure powerful high-dimensional learning. Synaptic crowding would preclude this when inputs are numerous, and we propose that the neocortex might be distinguished by special circuitry that promotes extreme specificity for high-dimensional nonlinear learning.

Experience-Dependent, Layer-Specific Development of Divergent Thalamocortical Connectivity

The main input to primary sensory cortex is via thalamocortical (TC) axons that form the greatest number of synapses in layer 4, but also synapse onto neurons in layer 6. The development of the TC input to layer 4 has been widely studied, but less is known about the development of the layer 6 input. Here, we show that, in neonates, the input to layer 6 is as strong as that to layer 4. Throughout the first postnatal week, there is an experience-dependent strengthening specific to layer 4, which correlates with the ability of synapses in layer 4, but not in layer 6, to undergo long-term potentiation (LTP). This strengthening consists of an increase in axon branching and the divergence of connectivity in layer 4 without a change in the strength of individual connections. We propose that experience-driven LTP stabilizes transient TC synapses in layer 4 to increase strength and divergence specifically in layer 4 over layer 6.

Hebbian crosstalk and input segregation

Hebbian synapses respond to input/output correlations, and thus to input statistical structure. However, recent evidence suggests that strength adjustments are not completely connection-specific, and this "crosstalk" could distort, or even prevent, learning processes. Crosstalk would then be a form of adjustment mistake, analogous to mistakes in polynucleotide copying. The mutation rate must be extremely low for successful evolution (which is a type of learning process), and similarly neural learning might require minimal crosstalk. We analyze aspects of the effect of crosstalk in Hebbian learning from pairwise input correlations, using the classical Oja model. In previous work we showed that crosstalk leads to learning of the principal eigenvector of EC (the input covariance matrix pre-multiplied by an error matrix that describes the crosstalk pattern), and found that, with positive input correlations, increasing crosstalk smoothly degrades performance. However, the Oja model requires negative input correlations to account for biological ocular segregation. Although this assumption is biologically somewhat implausible, it captures features that are seen in more complex models. Here, we analyze how crosstalk would affect such segregation. We show that, for statistically unbiased inputs, crosstalk induces a bifurcation from segregating to non-segregating outcomes at a critical value which depends on correlations. We also investigate the behavior in the vicinity of this critical state and for weakly biased inputs. Our results show that crosstalk can induce a bifurcation under special conditions even in the simplest Hebbian models, and that even the low levels of crosstalk observed in the brain could prevent normal development. However, during learning pairwise input statistics are more complex, and crosstalk-induced bifurcations may not occur in the Oja model. Such bifurcations would be analogous to "error catastrophes" in genetic models, and we argue that they are usually absent for simple linear Hebbian learning because such learning is only driven by pairwise correlations.

Reciprocal inhibition and slow calcium decay in perigeniculate interneurons explain changes of spontaneous firing of thalamic cells caused by cortical inactivation

The role of cortical feedback in the thalamocortical processing loop has been extensively investigated over the last decades. With an exception of several cases, these searches focused on the cortical feedback exerted onto thalamo-cortical relay (TC) cells of the dorsal lateral geniculate nucleus (LGN). In a previous, physiological study, we showed in the cat visual system that cessation of cortical input, despite decrease of spontaneous activity of TC cells, increased spontaneous firing of their recurrent inhibitory interneurons located in the perigeniculate nucleus (PGN). To identify mechanisms underlying such functional changes we conducted a modeling study in NEURON on several networks of point neurons with varied model parameters, such as membrane properties, synaptic weights and axonal delays. We considered six network topologies of the retino-geniculo-cortical system. All models were robust against changes of axonal delays except for the delay between the LGN feed-forward interneuron and the TC cell. The best representation of physiological results was obtained with models containing reciprocally connected PGN cells driven by the cortex and with relatively slow decay of intracellular calcium. This strongly indicates that the thalamic reticular nucleus plays an essential role in the cortical influence over thalamo-cortical relay cells while the thalamic feed-forward interneurons are not essential in this process. Further, we suggest that the dependence of the activity of PGN cells on the rate of calcium removal can be one of the key factors determining individual cell response to elimination of cortical input.

Cross-Talk Induces Bifurcations in Nonlinear Models of Synaptic Plasticity

Linear models of synaptic plasticity provide a useful starting-point for examining the dynamics of neuronal development and learning, but their inherent problems are well known. Models of synaptic plasticity that embrace the demands of biological realism are therefore typically nonlinear. Viewed from a more abstract perspective, nonlinear models of synaptic plasticity are a subset of nonlinear dynamical systems. As such, they may therefore exhibit bifurcations under the variation of control parameters, including noise and errors in synaptic updates. One source of noise or error is the cross-talk that occurs during otherwise Hebbian plasticity. Under cross-talk, stimulation of a set of synapses can induce or modify plasticity in adjacent, unstimulated synapses. Here, we analyze two nonlinear models of developmental synaptic plasticity and a model of independent component analysis in the presence of a simple model of cross-talk. We show that cross-talk does indeed induce bifurcations in these models, entirely destroying their ability to acquire either developmentally or learning-related patterns of fixed points. Importantly, the critical level of cross-talk required to induce bifurcations in these models is very sensitive to the statistics of the afferents’ activities and the number of afferents synapsing on a postsynaptic cell. In particular, the critical level can be made arbitrarily small. Because bifurcations are inevitable in nonlinear models, our results likely apply to many nonlinear models of synaptic plasticity, although the precise details vary by model. Hence, many nonlinear models of synaptic plasticity are potentially fatally compromised by the toxic influence of cross-talk and other sources of noise and errors more generally. We conclude by arguing that biologically realistic models of synaptic plasticity must be robust against noise-induced bifurcations and that biological systems may have evolved strategies to circumvent their possible dangers.

Hebbian crosstalk prevents nonlinear unsupervised learning

Learning is thought to occur by localized, activity-induced changes in the strength of synaptic connections between neurons. Recent work has shown that induction of change at one connection can affect changes at others (“crosstalk”). We studied the role of such crosstalk in nonlinear Hebbian learning using a neural network implementation of independent components analysis. We find that there is a sudden qualitative change in the performance of the network at a threshold crosstalk level, and discuss the implications of this for nonlinear learning from higher-order correlations in the neocortex.

Differences in intrinsic properties and local network connectivity of identified layer 5 and layer 6 adult mouse auditory corticothalamic neurons support a dual corticothalamic projection hypothesis

Intrinsic properties, morphology, and local network circuitry of identified layer 5 and layer 6 auditory corticothalamic neurons were compared. We injected fluorescent microspheres into the mouse auditory thalamus to prelabel corticothalamic neurons, then recorded and filled labeled layer 5 or layer 6 auditory cortical neurons in vitro. We observed low-threshold bursting in adult, but not juvenile, layer 5 corticothalamic neurons that was voltage and time dependent with nonlinear input-output properties, whereas adult layer 6 corticothalamic neurons demonstrated a regular spiking. Layer 5 corticothalamic neurons had larger somata, thicker apical dendrites and were more likely to have a layer 1 apical dendrite than layer 6 neurons. Using laser photostimulation, identified layer 5 corticothalamic neurons received excitatory input from a wide area of layers 2/3, 4, and 5 with widespread gamma-aminobutyric acidergic input from layer 2/3 and lower layer 5, whereas layer 6 corticothalamic neurons from the same cortical column received circumscribed excitatory input and discrete patches of inhibition derived from layer 6 of adjacent columns. These data demonstrate that layer 5 and layer 6 corticothalamic neurons receive unique sets of inputs and process them in different manners, supporting the hypothesis that layer-specific corticothalamic projections play distinct roles in information processing.

Task-dependent modulation of medial geniculate body is behaviorally relevant for speech recognition

Recent work has shown that responses in first-order sensory thalamic nuclei are modulated by cortical areas [1–5]. However, the functional role of such corticothalamic modulation and its relevance for human perception is still unclear. Here, we show in two functional magnetic resonance imaging (fMRI) studies that the neuronal response in the first-order auditory thalamus, the medial geniculate body (MGB), is increased when rapidly varying spectrotemporal features of speech sounds are processed, as compared to processing slowly varying spectrotemporal features of the same sounds. The strength of this task-dependent modulation is positively correlated with the speech recognition scores of individual subjects. These results show that task-dependent modulation of the MGB serves the processing of specific features of speech sounds and is behaviorally relevant for speech recognition. Our findings suggest that the first-order auditory thalamus is not simply a nonspecific gatekeeper controlled by attention [6]. Together with studies in nonhuman mammals [4, 5], our findings imply a mechanism in which the first-order auditory thalamus, possibly by corticothalamic modulation, reacts adaptively to features of sensory input.

Copying and evolution of neuronal topology

We propose a mechanism for copying of neuronal networks that is of considerable interest for neuroscience for it suggests a neuronal basis for causal inference, function copying, and natural selection within the human brain. To date, no model of neuronal topology copying exists. We present three increasingly sophisticated mechanisms to demonstrate how topographic map formation coupled with Spike-Time Dependent Plasticity (STDP) can copy neuronal topology motifs. Fidelity is improved by error correction and activity-reverberation limitation. The high-fidelity topology-copying operator is used to evolve neuronal topologies. Possible roles for neuronal natural selection are discussed.

Functional connectivity of dissociative responses in posttraumatic stress disorder: a functional magnetic resonance imaging investigation

BACKGROUND

The purpose of this study was to assess interregional brain activity covariations during traumatic script-driven imagery in subjects with posttraumatic stress disorder (PTSD).

METHODS

Functional magnetic resonance imaging and functional connectivity analyses were used to assess interregional brain activity covariations during script-driven imagery in PTSD subjects with a dissociative response, PTSD subjects with a flashback response, and healthy control subjects.

RESULTS

Significant between-group differences in functional connectivity were found. Comparing dissociated PTSD patients and control subjects' connectivity maps in the left ventrolateral thalamus (VLT) [-14, -16, 4] revealed that control subjects had higher covariations between activations in VLT and in the left superior frontal gyrus (Brodmann's area [BA] 10), right parahippocampal gyrus (BA 30), and right superior occipital gyrus (BA 19, 39), whereas greater covariation with VLT in dissociated PTSD subjects occurred in the right insula (BA 13, 34), left parietal lobe (BA 7), right middle frontal gyrus (BA 8), superior temporal gyrus (BA 38, 34), and right cuneus (BA 19). Comparing dissociated PTSD and flashback PTSD connectivity maps in the right cingulate gyrus [3, 16, 30] revealed that dissociated PTSD subjects had higher covariations between activations in this region and the left inferior frontal gyrus (BA 47).

CONCLUSIONS

Greater activation of neural networks involved in representing bodily states was seen in dissociated PTSD subjects than in non-PTSD control subjects. These findings might illuminate the mechanisms underlying distorted body perceptions often observed clinically during dissociative episodes.