Models of thalamocortical system

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Richard H. Granger and Robert A. Hearn (2007), Scholarpedia, 2(11):1796. doi:10.4249/scholarpedia.1796 revision #89049 [link to/cite this article]
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The thalamocortical system constitutes the vast majority of the mammalian brain, and increases disproportionately (allometrically) with overall brain size. Commensurate with its size, the thalamocortical system has been the subject of extensive neurobiological and computational study.

The thalamus and the neocortex are reciprocally connected via pathways of varying levels of topography. In addition, many areas of cortex and thalamus participate in a cortico-striato-pallido-thalamocortical loop. This article considers possible computational roles for these two kinds of loops (direct, and integrally involving the basal ganglia). In this article "thalamus" without qualification refers to the mammalian dorsal thalamus.

Elucidation of computational function relies on accurate anatomical and physiological data, which this article will begin by surveying. It should be noted that much of the neuroanatomy of thalamocortical pathways is incompletely mapped, leaving many topics of ongoing research, some of which will be noted.

Contents

Thalamocortical Anatomy

The primary sensory signals of vision, audition and touch pass through thalamus en route to cortex. These sensory pathways include the optic tract (vision), the the brachium of the inferior colliculus (audition), and the medial lemniscus (somatosensation), which target, respectively, the dorsal lateral geniculate nucleus, the medial geniculate complex, and the ventral posterior complex of the thalamus. Those thalamic nuclei in turn project topographically to well-defined architectonic fields of cortex, which reciprocally project back topographically to their innervating thalamic nuclei. Both the thalamocortical fibers and the reciprocal corticothalamic fibers also send collaterals to the thalamic reticular nucleus (nucleus reticularis thalami) (Liu and Jones, 1999) which is a thin shell of GABAergic cells surrounding the thalamus. The thalamic reticular nucleus then reciprocally innervates the thalamus, topographically (DeFelipe and Jones, 1991; Jones, 2001). (There is not a one-to-one correspondence; there are fewer reticular cells than thalamic relay cells.) The entirety of the thalamus is also innervated by diffuse brainstem reticular inputs. For details of the internal circuitry of thalamic nuclei see Thalamus.

The three sensory pathways described above (especially vision) are perhaps the best studied, but other thalamic nuclei receive subcortical input, project to cortex, and are innervated by cortex. However, the specific projection patterns are in general not as straightforward as for the primary sensory pathways mentioned.

Perspectives on Thalamocortical and Corticothalamic Connectivity

Two current detailed perspectives on fitting the patterns of thalamocortical and corticothalamic connectivity into a coherent picture are noteworthy.

Core vs. Matrix

In the system of Jones (2001, 2007), thalamic relay cells fall into one of two classes: core and matrix. From a given thalamic nucleus, core cells project topographically to a sharply bounded, associated architectonic field of cortex. For example, the core cells in the dorsal lateral geniculate nucleus project topographically to V1 (Brodmann area 17). Projections from thalamic core cells synapse on neurons in all cortical layers to some extent (Keller and White, 1989) but predominantly in deep layer III and in layer IV in granular cortex, as well as on the apical dendrites of layer VI neurons (Molinari et al., 1995; Jones, 2001). These afferents, which preserve topographic organization, are often described as the primary input to sensory neocortical regions, though quantitative anatomical studies report that these afferents comprise perhaps 6% of the synapses onto layer IV target cells, with the majority of the remaining afferents reportedly arriving from lateral cortico-cortical connections (Freund et al., 1985; Freund et al., 1989; Peters and Payne, 1993; Peters et al., 1994; Ahmed et al., 1997). Projections from a given thalamic core region extend to a cortical area roughly 0.5-1.0 mm wide, somewhat larger than the size of physiologically-delineated functional columns (Jones, 1981). Example thalamic core nuclei include MGv, VPL, VPM, LGd.

In contrast, matrix cells project more broadly and less topographically, crossing architectonic borders, synapsing chiefly in layer I, on apical dendrites of neurons in multiple layers including layers II, III, and V. The earliest detailed reports of these projections emphasize that they occur as a prevalent feature of cortical anatomy, and describe them as “nonspecific”, i.e., projections from small thalamic regions innervate broad cortical areas, and projections to circumscribed cortical areas may originate from a broad expanse of thalamus (Lorente de No, 1938). These initial findings have been confirmed and extended repeatedly (Killackey and Ebner, 1972; Killackey and Ebner, 1973; Herkenham, 1986; Jones, 1998), and it has consistently been found that thalamic cells projecting to a given cortical area receive projections back from layer V of that cortical area without intervening NRt contacts (Conley and Diamond, 1990; Rouiller et al., 1991; Bourassa and Deschênes, 1995; Deschênes et al., 1998). Example thalamic nuclei include MGm, Pul, Pom, AD. Matrix cells are distributed throughout the entire thalamus. In certain nuclei only, a population of core cells is superimposed. The nuclei containing core cells receive subcortical inputs that are highly ordered topographically, whereas the nuclei without a core population receive less topographic subcortical inputs. In addition to the primary sensory pathways mentioned above, other core pathways include cerebellum -> ventral lateral posterior nucleus -> motor cortex, and mammilary nuclei -> anterior thalamic nuclei -> limbic areas.

Core projections to cortex are reciprocated by topographic projections from layer VI of cortex. Although there is a commonly accepted principle of reciprocity (Diamond et al., 1969) stating that every cortical area returns fibers to the thalamic nucleus that provides its dominant thalamic input, most studies have focused on primary sensory relays, which contain core cells. It is unclear what the pattern is for nuclei with no significant core population. Also, Deschênes et al. (1998) note several nonreciprocal corticothalamic layer VI projections, even for primary sensory areas, and propose instead a rule of parity, which states that the distribution of layer VI corticothalamic projections across and within the thalamic nuclei is determined by the branching patterns of the different classes of prethalamic afferent. This rule can explain some of the observed nonreciprocal corticothalamic projections, and can further be seen as congruent with Jones's association of the degree of topography of thalamic input with the degree of topography of corresponding thalamocortical output.

Layer V cells also project to thalamus, as mentioned. However, these projections tend not to return to the primary innervating thalamic nucleus. Instead, they project to different, but usually functionally related, nuclei. These fibers lack collaterals to the thalamic reticular nucleus. Also, the thalamic innervation is itself via a collateral, the layer V fibers continuing on to other subcortical targets. The thalamic nuclei targeted by the layer V projections tend to be core-poor and matrix-rich, suggesting that the matrix relay cells are the targets (Jones, 2001). Jones proposes that one function of this pattern is to synchronize fast cortical oscillations via the matrix projections back to cortex.

The terminology is intended to extend the long-studied distinctions in the literature between “specific” and “nonspecific” nuclei (Killackey and Ebner, 1972; 1973; Berendse and Groenewegen, 1991; Wyss and VanGroen, 1995; Castro-Alamancos and Connors, 1997). There is evidence for some correspondence between matrix cells and calbindin-immunoreactivity, and between core cells and parvalbumin-immunoreactivity in primates (Jones et al., 1989) though specific exceptions to this idea have been found in primates, and it is important to note that the correspondence does not hold in other mammals (Jones, 2007, p. 113; Sherman and Guillery 2006).

First-Order vs. Higher-Order Relays

An important distinction is often made between first-order relays, predominantly denoting primary sensory relay nuclei, as distinct from higher-order thalamic relays. The layer V corticothalamic pathway is also reported to provide higher-order thalamic relays with a driving input that, unlike first-order relays, they otherwise lack (Sherman and Guillery 2002; 2006; see also Thalamus). Since the layer V output represents cortical motor output broadly defined, the thalamic input it provides is an efferent copy of the motor commands issued by a region of cortex. This cortico-thalamo-cortical pathway has been proposed as a largely-unexplored means of cortico-cortical communication, subject to modulatory control; it has been suggested that direct cortico-cortical pathways, rather than driving inputs (e.g., Felleman et al., 1991; Hilgetag et al., 1996), may be primarily modulatory in nature over these cortico-thalamo-cortical paths (Sherman and Guillery 2006).

In comparison to the core vs. matrix perspective, intriguing questions arise regarding the laminar targets of relay cells. Can the cells in higher-order nuclei, which from a core vs. matrix perspective would be matrix cells, be construed as having a parallel function to those in the first-order nuclei, if they target superficial rather than middle cortical layers? Traditionally, the middle layers are viewed at the recipient layers of driving thalamic input. However, there is evidence that the cat pulvinar nucleus, which in this scheme is a higher-order visual relay (with driving input from layer V of primary visual cortical areas), may indeed target layer IV neurons in multiple cortical regions (excluding V1) (Symonds et al., 1981, Abramson et al., 1985).

Figure 1: The primate thalamus.

Number of Cortical Cells

There are (with a few notable exceptions, such as the primary sensory areas) approximately 80,000 neurons beneath each square millimeter of cortical surface, distributed in a stereotypic manner across the cortical layers. Despite dendritic elongation in larger brains, and corresponding increases in numbers of synaptic contacts among pyramidal cells, the number of cells stays constant per region in animals weighing from grams to kilograms (Rockel et al., 1980). These cortical cells are innervated by a much smaller number of thalamic afferents; ratios of roughly 160 cortical cells per corresponding thalamic relay cell are typical (O'Kusky and Colonnier, 1982).

Cortical Cell Types

Excitatory (pyramidal) cells outnumber inhibitory cells by roughly four or five to one throughout most of cortex, again excepting the primary sensory areas (Fitzpatrick et al., 1987; Hendry et al., 1987). Excitatory neurons have axons that can extend millimeters whereas inhibitory cells project only locally (rarely more than 100 μm). Inhibitory axons synapse densely on or near pyramidal cell bodies (Keller and White, 1989). In contrast, excitatory cells receive only sparse afferents from other excitatory cells; it has been estimated that the probability of contact between two neocortical excitatory cells that are 0.2-0.3mm apart is less than 0.1, and between two such cells that are more than 1mm from each other, p < 0.01 (Braitenberg and Schüz, 1998).

Cortical Modules

Within architectonic regions of cortex, neurons are vertically organized into anatomically defined "pyramidal cell modules" consisting of distinct groups of layer V and layer II-III pyramidal cells whose apical dendrites are commingled (White and Peters, 1993; Peters et al., 1994). Architectonically distinguishable areas differ in size and population of cell layers, and there is correspondence between these region boundaries and the site of origin of their thalamic afferents. (In contrast, functional "columns" are physiologically defined, in terms of receptive field properties, rather than anatomical boundaries (Mountcastle, 1957), and are typically described as 400-500 μm in extent, comprising perhaps 200 pyramidal cell modules apiece.

Quantitative data on the microcircuits within cortex are incomplete. However, by extrapolating from known laminar distributions of neuron types in cortex and using several reconstructed neurons to provide morphometrical data, Binzegger et al. (2004) have produced a quantitative map of the circuit of cat primary visual cortex. Questions about the detailed statistics of cortico-cortical connectivity have recently been addressed via computational modeling (Felch & Granger 2008).

Thalamocortical Physiology

The highly recurrent connectivity of the thalamocortical system, in addition to inputs from basal ganglia and thalamic modulation by brainstem reticular input, belie the seemingly simple notion that thalamus is a mere "relay" from the senses to cortex.

Sequential circuit activation

Peripheral inputs activate thalamic core cells which in turn participate in topographic activation of middle cortical layers; e.g., ear > cochlea > auditory brainstem nuclei > ventral subdivision of medial geniculate nucleus (MGv) > A1; in contrast, matrix nuclei are most strongly driven by corticothalamic feedback (Bender, 1983; Diamond et al., 1992b; Diamond et al., 1992a), supporting a system in which peripheral afferents first activate core nuclei, which in turn activate cortex (via a stereotypical vertical pattern: middle layers > superficial layers > deep layers), which then activate both core and matrix nuclei via corticothalamic projections (Mountcastle, 1957; Hubel and Wiesel, 1977; Di et al., 1990; Kenan-Vaknin and Teyler, 1994). Although the majority of excitatory synapses in all thalamic nuclei arise from corticothalamic fibers, the different dendritic locations of subcortical afferent synapses vs. corticothalamic synapses supports this essentially feedforward view in non-matrix nuclei (Jones, 2007, p. 289). (But see Llinás and Paré, 1991, and also Oscillation-Assisted Processing, below, for a different perspective.)

Excitatory and inhibitory interaction

Axons of inhibitory interneurons densely terminate preferentially on the bodies, initial axon segments, and proximal apical dendrites of excitatory pyramidal cells in cortex, and thus are well situated to exert powerful control over the activity of target excitatory neurons. Inhibitory cells receive direct thalamocortical innervation, resulting in feedforward inhibition. When a field of excitatory neurons receives afferent stimulation within a window permitted by this feedforward inhibition, those that are most responsive will activate the local inhibitory cells in their neighborhood, which will in turn inhibit local excitatory cells. The typical time course of an excitatory (depolarizing) postsynaptic potential (PSP) at normal resting potential, in vivo, is brief (15-20 msec), whereas corresponding GABAergic inhibitory PSPs last roughly an order of magnitude longer (~50 msec GABAa response, 200-300 msec GABAb response) (Castro-Alamancos and Connors, 1997). Thus excitation tends to be brief, sparse, and curtailed by longer and stronger feedback inhibition.

Activity rates

The rate of repetitive activation in thalamocortical circuits ranges from the "slow sleep" (.2-1 Hz) and "delta" (1-4 Hz) frequency bands through the "gamma" range (30-80 Hz) (Steriade, 1997; Chrobak and Buzsaki, 1998; Shimono et al., 2000; Sarter and Bruno, 2000; Fries et al., 2001; Rozov et al., 2001; Canales et al., 2002; Knoblauch and Palm, 2002; Pesaran et al., 2002). (Gamma is occasionally considered to range as high as 120 Hz.) There is strong evidence for ascending influences (e.g., basal forebrain) on inhibitory neurons (Freund and Meskenaite, 1992; Gulyas et al., 1996; Blasco-Ibanez et al., 1998; Gulyas et al., 1999) modulating their response properties, in turn affecting the probability of response of excitatory cells during the peaks and troughs of such "clocked" inhibitory cycles. Evidence of intrinsic rhythmic currents in thalamic and cortical cells (Kim et al., 1995; Bush and Sejnowski, 1996; Destexhe et al., 1999; Zhu and Connors, 1999) is compatible with extrinsic ascending influences, acting either independently or in concert with them. Three modes of activity have typically been reported for thalamic neurons: tonic, rhythmic, and arrhythmic bursting. The latter appears predominantly during non-REM sleep whereas the first two appear during waking behavior (McCarley et al., 1983; Steriade and Llinas, 1988; McCormick and Feeser, 1990; Steriade et al., 1990; McCormick and Bal, 1994; Steriade and Contreras, 1995). It has been variously argued that rhythmic burst mode may provide better signal to noise and thus facilitate detection of a stimulus, and that tonic mode contains more detailed information about a stimulus (Guido et al., 1992; Guido et al., 1995; Mukherjee and Kaplan, 1995; Sherman, 2001). Others have suggested that distinctions between modes based on differential information are not warranted (e.g., Reinagel et al., 1999).

Temporal activity

Notable patterns occur in the activity of thalamocortical circuits:

  • Synchronous activity of wide regions of cortex (modulated in part by ascending systems affecting the periodic responsivity of inhibitory cells) makes the probability of excitatory cell spiking lower during peak inhibition and higher during inhibitory troughs.
  • The average time course of excitatory postsynaptic potentials in cortical pyramidal cells (~ 10-15 msec) appears to set limits on the temporal precision of spike trains that such a neuron may emit.
  • Summation characteristics and integration (e.g., capacitance) time constants of dendrites map many distinct spike train input patterns into postsynaptic voltage transients that are difficult to distinguish, limiting the temporal precision with which a target neuron can "read" differences among slightly different spike trains. However, rapid inhibitory feedback from local GABAa-type cortical interneurons contributes to increased synchronization of neighboring excitatory cells, a mechanism by which carefully timed firing may be achieved, potentially enabling spikes with sufficiently precise timing to support temporal coding (Magee, 2000; Magee and Cook, 2000).

Glutamatergic synapses

The vast majority of excitatory synapses throughout telencephalon are glutamatergic (Rodriguez et al., 2004). An excitatory axon targeting the apical dendrite of an excitatory cell typically terminates at a spine, which contains ~500-1000 AMPA- and NMDA-type glutamate receptors (Bekkers and Stevens, 1989). An average neocortical pyramidal cell in humans reportedly receives 25-80 thousand such afferents (Cragg, 1967; Rockel et al., 1980; Braitenberg and Schüz, 1998 pp. 190-191) (with a few notable exceptions such as area 17, which has an unusually high density of neurons per square mm and a correspondingly low number of synapses per neuron (Cragg, 1967; O'Kusky and Colonnier, 1982)), and typical methods may lead to systematic undercounting of synapses (Guillery and Herrup, 1997; von Bartheld, 1999, 2001). In certain regions, notably thalamus and layer IV cortex, as well as glutamatergic synapses onto inhibitory neurons, the NMDA receptors contain the (rare in the adult) NR3A subunit (Wong et al., 2002), which has been shown to inhibit the expression of NMDA receptor ion channels (Das et al., 1998).

Neocortical synaptic potentiation

NMDA-dependent long-term potentiation of synaptic connections in neocortex has been shown in superficial and deep layers of multiple regions (Komatsu et al., 1988; Hirsch and Crepel, 1990; Iriki et al., 1991; Bear and Kirkwood, 1993; Kirkwood et al., 1993; Kimura et al., 1994; Castro-Alamancos et al., 1995; Hess et al., 1996; Kudoh and Shibuki, 1996; Buonomano and Merzenich, 1998; Rioult-Pedotti et al., 2000; Heynen and Bear, 2001; Seki et al., 2001). Memories that are rapidly induced (i.e., with little or no practice), long lasting (potentially for decades) and high-capacity (enough to hold the memories of a lifetime) presumably require a biological mechanism with corresponding characteristics. Biological phenomena that last only for limited duration (decrementing over time), or are slow to induce (e.g., minutes of constant stimulation), or are not synapse-specific (and thus not high capacity) may underlie some form of short-term memory (or other operation) but not rapidly-induced, high-capacity long-term memory. "LTP" here refers specifically to the endogenously occurring synaptic phenomenon that has the properties just listed, enabling it to serve as the substrate of lifelong memories.

Thalamocortical Computation

Feedforward Sensory Processing

Spatiotemporal Coding

From a feedforward perspective, the thalamus relays sensory information to cortex. Several computational models have addressed how the spatial and temporal characteristics of sensory signals are transformed by thalamus and cortex, and how signal transmission can be modulated.

One principle that emerges is that of sparse coding: that sensory information may be encoded using only a small number of potentially active neurons at a time (Olshausen and Field, 2004; Field, 1987; Bell and Sejnowski, 1997; Lewicki 2002). More generally, several researchers have investigated the hypothesis that the thalamocortical system is optimized to efficiently code the statistical properties of the signals to which it is exposed (Attneave, 1954; Barlow, 1961; Simoncelli and Olshausen, 2001).

A critical feature of thalamocortical transmission is feedforward inhibition in cortex. Thalamocortical afferents contact both excitatory projection neurons and fast-spiking, local inhibitory interneurons, which synapse on the same projection neurons (Miller et al., 2001b). This characteristic microcircuit can act as a precise coincidence detector (Hull and Scanziani, 2007), and there are indications that it can also explain such diverse aspects of cortical sensory representation as orientation tuning in visual cortex (Troyer et al., 1998) and temporal tuning (inhibited response to fast-moving visual stimuli, and related temporal low-pass filtering effects in other cortical areas) (Krukowski and Miller, 2001). More generally, spatiotemporal integration across the neural receptive field is a general feature of sensory coding (Boloori and Stanley, 2006; Webber and Stanley, 2004).

Olhausen and Field (2005) sound a cautionary note about the degree to which even the feedforward aspects of sensory processing can ever be understood in terms of combinations of reduced stimuli such as spots, white noise, or sine wave gratings, due to the highly nonlinear response properties of real, as opposed to idealized, cortical neurons.

Ignoring for the moment corticothalamic feedback, the most prominent modulatory input to thalamus and cortex is acetylcholine from the brainstem reticular formation (Hallanger et al., 1987). Models have investigated ways in which acetylcholine can control the transition between thalamic relay tonic and burst modes (Sherman, 2001), affect tuning curves of cortical neurons (Soto et al., Kopell, and Sen, 2006), and modulate the relative importance of thalamocortical and intracortical processing (Gil et al., 1997; Hasselmo, 1995; Kimura, 2000, Clarke 2004).

Regional Specialization

Neocortex consists of multiple modules that share substantial architectural properties. The regularity of thalamocortical circuitry has supported decades of suggestions that it may be composed of functionally similar or even identical circuits, differing only, or predominantly, in their afferent sources and efferent targets (Szentagothai, 1975; Hubel and Wiesel, 1977; Creutzfeldt and Nothdurft, 1978; Mountcastle, 1978; Keller and White, 1989; Galuske et al., 2000; Gazzaniga, 2000, Castro- Alamancos and Connors, 1997; Jones, 1998; Heynen and Bear, 2001; Silberberg et al., 2002; Valverde, 2002).

However, the known architectonic differences between regions, in addition to differing statistical properties arising from distinct afferent sources of input, might suggest regional specializations in sensory processing. Thus, characterizing regional differences in computations performed by the thalamocortical circuit is an important and active area of research.

One way in which such differences could be manifested is by differing receptive field properties in thalamus and corresponding cortex. Miller et al. (2001a) propose a model with three different types of functional convergence from thalamus to cortex: in inheritance, a cortical cell’s receptive field is determined by functionally identical thalamic inputs; in constructive convergence, a cortical cell’s receptive field is a composite of many smaller (in spatiotemporal extent) thalamic inputs; in ensemble convergence, the thalamic inputs have some receptive field properties that are not shared by the cortical target cell.

Miller et al. find that in cat auditory thalamus and cortex, all three types of transformation are present, whereas in the visual system constructive convergence seems to predominate (Alonso et al., 2001), and in the rat somatosensory whisker barrel system ensemble convergence may predominate (Simons and Carvell, 1989).

In auditory cortex, there are important differences in anatomy and synaptic physiology from the columnar organization of other sensory cortices (Linden and Schreiner, 2003). There are indications auditory cortex might be specialized for fast temporal information processing (Buonomano, 2000).

Thalamocortical Oscillations

The recurrent connections in the thalamocortical system participate in a prominent feature of thalamocortical circuitry: oscillations across a wide range of frequencies, from as slow as 0.2 Hz to as fast as 80 Hz (Timofeev and Bazhenov, 2005; Buzsáki and Draguhn, 2004). (Some slower, "infra-slow" and faster, "ultra-fast" oscillations seem to not integrally involve thalamocortical connections.) These oscillations can be generated via intrinsic currents in thalamus or cortex, via peripheral input, or via network mechanisms, and are typically synchronized via network mechanisms. Thalamocortical oscillations are thought to play a number of important functional roles, both in sleeping and in waking states, though much about these roles remains unknown.

Slow Oscillations
Figure 2: Cortical slow sleep oscillation in vivo (modified from Timofeev and Bazhenov 2005).

Oscillations in the range of .2-1 Hz are the dominant form of thalamocortical activity seen during slow-wave sleep (Steriade, 2003). Slow waves are generated cortically (Steriade et al., 1993), but thalamocortical neurons are synchronized (Contreras and Steriade, 1995), inhibiting transmission of incoming sensory messages to the cortex (Steriade, 2003).

Less is known about the mechanisms underlying slow waves than about delta waves and spindle waves. Proposed mechanisms for slow wave generation include spontaneous miniature synaptic activities, or "minis" (Fatt and Katz, 1952; Timofeev et al., 2000; Bazhenov et al., 2002), and spontaneous activity of layer V cortical neurons (Sanchez-Vives and McCormick, 2000; Compte et al., 2003). Both processes would induce a transition to the active or UP cortical state.

Two recent large-scale numerical simulations of the thalamocortical system have reproduced many experimentally observed properties of slow waves, including transitions to and from slow-wave sleep.

The simulation of Bazhenov et al. (2002) used a four-layer model (thalamus, thalamic reticular nucleus, inhibitory cortex, pyramidal cortex) with 225 Hodgkin-Huxley cells (single-compartment in thalamus, multiple-compartment in cortex). In this simulation the reexcitation of the cortical network on each cycle is driven by coincidences of minis.

Hill and Tononi (2005) simulated two multi-layer visual cortical areas and their associated thalamic and reticular sectors, with over 65,000 neurons. This model is the first to integrate intrinsic neuronal properties with detailed thalamocortical anatomy and reproduce neural activity patterns in both wakefulness and sleep. In this model an UP state can be initiated by a variety of means, including minis, synaptic input from other cortical and thalamic areas, or intrinsic hyperpolarization-activated \(I_h\) currents.

Evidence exists that Delta oscillations (1-4 Hz) are generated intrinsically by thalamic relay neurons as a result of the interplay between their low-threshold Ca++ current and hyperpolarization-activated cation current (Amzica and Steriade 1998; McCormick and Pape, 1990).

Both slow and delta oscillations are thought to participate in consolidation of memories acquired during wakefulness (Gais et al., 2000; Stickgold et al., 2000; Maquet 2001; Huber et al., 2004; Steriade and Timofeev, 2003).

Based on analyses of multiple extracellular recordings of slow oscillations during natural sleep, it has been suggested that fast oscillations during active states of slow-wave sleep could reflect recalled events experienced previously, directly "imprinting" these memories in the network via synchronized events that are observable as slow-wave components in the EEG (Destexhe et al., 1997).


Spindle oscillations (7-14 Hz) consist of waxing and waning field potentials at 7-14 Hz, typically lasting 1-3 seconds and recurring roughly every 5-15 seconds. In vivo, spindle oscillations are typically observed during early stages of sleep or during active phases of slow-wave sleep oscillations. They are generated thalamically (Morison and Bassett 1945), critically involving the thalamic reticular nucleus (Steriade et al., 1985; Steriade and Deschênes, 1984, von Krosigk et al., 1993); burst firing of thalamocortical neurons in turn excite reticular neurons, maintaining the cycle, with corticothalamic feedback involved in synchronizing these oscillations (Destexhe et al., 1998; Destexhe et al., 1999). Modeling studies (Bazhenov et al. 1998; Destexhe et al., 1996; Destexhe and Sejnowski, 1997) have reproduced these features.

As with slow and delta oscillations, spindle oscillations are implicated in memory consolidation and demonstrate short- and intermediate-term synaptic plasticity (Gais et al., 2000; Steriade and Timofeev, 2003).

Fast Oscillations

Waking states of the brain can be characterized by a predominance of relatively high-frequency oscillations, notably Beta (15-30 Hz) and Gamma (30-80 Hz) oscillations. Gamma activity is associated with attentiveness, focused arousal, sensory perception, movement, and prediction. (See Beta-gamma oscillation for references.) Gamma-range synchronous activity has been proposed to be related to cognitive processing, and may transiently synchronize cells with disparate receptive fields. That synchrony has been hypothesized to allow multiple features of a cue to be assembled into a coherent representation. (See Singer 1998 for review.)

It is worth noting that beta oscillations have been selectively induced in hippocampal slices (Boddeke et al., 1997; Shimono et al., 2000), and beta and gamma oscillations are reported to have different synchronization properties (Kopell et al., 2000).

A large-scale model by Traub et al. (2005), incorporating 3,560 multi-compartment thalamic, reticular, and cortical neurons, replicates persistent gamma oscillations. In this model electrical coupling between axons is necessary for persistent gamma.

Spike-and-Wave Oscillations

During epileptic seizures, another pattern of oscillation, spike-and-wave oscillation, is observed. These oscillations occur at a frequency of about 3 Hz in humans. The "spike" in the EEG pattern is known to be related to cortical cell firing, the "wave" to cortical cell silence. One key element in thalamocortical models of spike-and-wave oscillations is the switching of the thalamus to a slow 3 Hz oscillation by excessive corticothalamic feedback.

Recurrent Computation

In principle, corticothalamic feedback could serve to modulate response properties of thalamus, or to induce and synchronize large-scale oscillations, or to relay specific information back to thalamus for more complex computational processing. Evidence suggests that all three processes occur.

Corticothalamic Modulation

Corticothalamic feedback from layer VI provides modulatory input to thalamic relay cells (Jones, 2007, p. 289), as does brainstem cholinergic input (and, more locally, thalamic reticular nucleus GABAergic input), affecting the structure of their receptive fields. Sherman and Guillery have proposed that, in awake sensory processing, burst transmission can serve as a "wake up call" to cortex, activating a set of cortical columns which would then provide feedback switching the relay mode to tonic, which provides more linear signal transfer (Sherman, 2001; Sherman and Guillery, 2006).

The corticothalamic collateral input to the thalamic reticular nucleus is stronger than the direct input to relay cells (Golshani et al, 2001), emphasizing the modulatory aspect of the feedback. There is evidence that projections from some areas of prefrontal cortex terminate widely in the TRN, rather than only in directly associated reticular and thalamic territories (Zikopoulos and Barbas, 2006). This suggests an additional role for corticothalamic modulation: prefrontal areas may participate in attentional regulation of relevant sensory signals, by gating thalamic output back to cortex.

Oscillation-Assisted Processing

Closed-loop neuronal computations occur throughout the nervous system, including the thalamocortical system. Such loops may be viewed from either a homeostatic or a computational point of view. Feedback loops provide a mechanism for neural ensembles to maintain a set of variables within a given range; this can be seen as a homeostatic control process. From a computational perspective, the sequence of changes to the variable state values can be viewed as an encoding of the sensory input driving the homeostatic corrections (Ahissar and Kleinfeld, 2003).

Several studies have investigated correlations between whisking behavior and thalamocortical oscillations in the rat barrel cortex system. This mode of sensory processing is used during "active discrimination", as a function of overall behavioral state (Nicolelis, 2005). Thus, the entire feedback loop is under external modulatory control. Models suggest that a computational function of this loop is to transform temporally encoded vibrissal information into a rate code, by means of phase-locked loops (Ahissar et al., 2000; Ahissar et al., 1997). There is evidence that this fundamental pattern may be a more general property of sensory systems. In human speech perception, comprehension is enhanced when the temporal envelope frequency of the speech signal is similar to cortical activity frequency, and when there is phase locking between the two temporal envelopes (Ahissar et al., 2001).

A broader perspective of all sensory stimuli serving to modulate ongoing, self-generated brain activity has been put forward as an alternative to the traditional "feedforward" view (Llinás and Paré, 1991). This view is supported by several similarities in paradoxical or REM sleep and wakefulness, vs. other sleep states. On this view dreaming is what happens when the intrinsic functional realm of wakefulness is deprived of modulatory sensory input (and brainstem-mediated muscular atonia is present).

Computational Interpretations of Activity Patterns

Candidate computational roles have been proposed for the integrative action of thalalmocortical loops, with regard to the patterns of cortical activity that may occur over time in response to natural afferent stimulation. Earliest cortical activity occurs in middle and superficial layers in response to peripheral input via direct or core thalamic nuclei. Lateral inhibition in superficial layers generates IPSPs that are substantially longer than EPSPs; thus initial excitatory responses are rapidly inhibited, and only those excitatory cells that are most activated by an input pattern can respond at all before lateral inhibition quiets them. With synaptic potentiation of the kind described above (see Neocortical synaptic potentiation), superficial cells that initially respond to a particular thalamic input pattern become increasingly responsive not only to that input but also to a range of similar inputs, such that similar but distinguishable inputs will come to elicit identical patterns of output from layer II-III cells. Results of this kind have been obtained in a number of different models with related characteristics (von der Malsburg, 1973; Grossberg, 1976; Rumelhart, 1985; Coultrip et al., 1992).

Superficial layer responses activate deep layers, and output from layer VI initiates feedback activation of thalamic reticular nucleus (TRN), which in turn inhibits the portions of the core nucleus corresponding topographically to those portions of layer II-III that were active. On the next cycle of thalamocortical activity, the input will arrive at the core nucleus against the background of the inhibitory feedback from TRN, which has been shown to last for hundreds of milliseconds (Huguenard and Prince, 1994; Cox et al., 1997; Zhang et al., 1997).

Thus, in a series of modeling experiments, the predominant component of the next input to cortex is just the uninhibited remainder of the input, whereupon the same operations as before are performed. The result is that the second cortical response will consist of a quite distinct set of neurons from the initial response, since most of the input components giving rise to that first response are now inhibited. Analysis of the second (and ensuing) responses in computational models has shown successive sub-clustering of an input: the first cycle of response identifies the input’s membership in a general category of similar stimuli, the next response (a fraction of a second later) identifies its membership in a particular subcluster, then sub-sub-cluster, etc. (Rodriguez et al., 2004).

In contrast to the topography-preserving projections between core thalamic cells and cortex, the nontopographic projections from layer V to matrix cells bypass the thalamic reticular nucleus (see, e.g., Bourassa and Deschenes, 1995; Deschenes et al., 1998). These non-topography-preserving projections have been interpreted as orthogonalizing their inputs rather than clustering them: i.e., any structural relationships that may obtain among inputs are not retained in the resulting projections. Thus even cortical response patterns that are similar to each other may generate very different patterns in their projections to thalamic matrix cells.

If the thalamic input is changing over time, then the activation of layer V in rapid sequence via superficial layer inputs (in response to an element of a sequence) and via thalamic matrix inputs (corresponding to feedback from the previous element in a sequence) selects responding cells sparsely from the most activated cells in the layer (Coultrip et al., 1992) and selects synapses on those cells sparsely as a function of the sequential pattern of arriving inputs. Thus synapses potentiated at a given time in layer V correspond to the input occurring at that time together with orthogonalized feedback arising from input just prior to that time (Aleksandrovsky et al., 1996; Granger et al., 1994). In modeling experiments, the overall effect is "chaining" of elements in the input sequence, via the "links" created due to layer V activity from coincident inputs corresponding to current and prior input elements (Rodriguez et al., 2004; Granger 2006).

Large-Scale Simulation

Three very large-scale simulation efforts are noteworthy. The current state of the art in thalamocortical simulation is a model by Traub et al. (2005), comprising 3,650 multi-compartment (~100 compartments) neurons of several types from all cortical layers, thalamus, and thalamic reticular nucleus. This model exhibits persistent gamma oscillations, sleep spindles, synchronized population bursts resembling seizures, and ripples.

An even more ambitious project, the Blue Brain Project, is under way (Markram, 2006). This project will simulate an entire two-week-old rat neocortical somatosensory column, comprising some 10,000 neurons, with thousands of compartments each, and more than a dozen Hodgkin-Huxley ion channels per compartment. Blue Brain will use the IBM Blue Gene/L supercomputer architecture, and a large (currently private) database of columnar connectivity data gathered by the Markram laboratory over the past decade.

Izhikevich (2005) has simulated a network of \(10^{11}\) neurons and \(10^{15}\) synapses -- the size of the entire human brain. The simulation used microcircuitry based on quantitative anatomical studies of cat visual cortex (Binzegger et al., 2004) and of thalamic circuitry, employing a very efficient approximation to more detailed traditional spiking neuron models (Izhikevich, 2003). One second of simulated time required 50 days on a cluster of 27 3GHz processors.

Cortico-basal ganglia-thalamocortical Loops

A discussion of the function of the thalamocortical system would not be complete without consideration of those thalamic nuclei which integrally involve the basal ganglia in their thalamocortical loop (primarily ventral lateral anterior, ventral anterior, medial dorsal, and centré median). Often the basal ganglia inputs to thalamus are treated as just one more subcortical input, on a par with any other. But several features set them apart. First, the basal ganglia inputs (from globus pallidus, pars interna (GPi), and substantia nigra, pars reticulata (SNr)) are GABAergic, and thus inhibitory, unlike other inputs (apart from the thalamic reticular nucleus). Second, there are several closed cortico-striato-pallido-thalamocortical loops (Alexander et al. 1986, Middleton et al., 1996). These may coarsely be divided into sensorimotor, associational, and limbic loops (Parent et al., 1995). Thus, cortex is integrally involved in the basal ganglia inputs to thalamus, so these should not be viewed as directly analogous to primary sensory inputs.

Essentially all of cortex, both allocortex and isocortex (except, possibly, primary visual cortex), projects to striatum, via collaterals from the layer V projection to brainstem and motor targets (Swanson, 2000). (These same fibers are the source of the thalamic matrix innervation, in the core vs. matrix perspective, and the higher-order thalamic relay innervation, in the first-order vs. higher-order relays perspective, above.) The basal ganglia-thalamo-cortical pathway does not reciprocally innervate the entire cortex, however. Traditionally the motor output is emphasized, but more broadly basal ganglia is seen as affecting much of anterior cortex (e.g. Alexander et al. 1986). There do, however, seem to be some posterior cortical regions targeted as well (Middleton et al., 1996, Clower et al. 2005).

Reinforcement Learning

A popular view of basal ganglia function is that it performs reinforcement learning. The basal ganglia input to thalamus affects cortical states and subsequent behavior, which leads to increased or decreased reward signals via the dopaminergic nigrostriatal pathway. Striatal plasticity then adjusts the response of basal ganglia to cortical input, so that rewarding actions are reinforced, and nonrewarding actions are inhibited (Schultz 1997, 1998).

Comparisons have been drawn between the basal ganglia and computational models of reinforcement learning, specifically temporal-difference actor-critic models (Barto, 1995; Houk et al., 1995; Sutton and Barto, 1998; Suri and Schultz, 1998, 1999). Such models perhaps best represent the common current view on basal ganglia function. However, these models have been criticized on grounds that they either fail to reproduce the observed dopamine signal in some cases, that they have not been linked with brain structures supporting the required temporal difference signals, or both (Brown et al. 1999; Joel et al. 2002); see also Reinforcement Learning and Models of Basal Ganglia.

More fundamentally, it is unclear how exactly actions might be represented in the basal ganglia: a model in which individual behavioral actions are targeted topographically is highly problematic outside of primary motor cortex. If a notion of action in the formal reinforcement learning sense is to be applied to the basal ganglia, it seems that the picture that must be adopted is that the environment affected by the actions is not the organism's musculature, as is traditionally assumed in such models, but rather the thalamocortical system itself.

Basal Ganglia as Modulator of Cortical Functioning

Consistent with the above considerations, Sherman and Guillery (2006) propose on anatomic and physiological grounds that basal ganglia input is modulatory. Specifically, they propose that basal ganglia inputs modulate relay properties of higher-order relays. For example, the GPi inputs to the ventral anterior and ventral lateral nuclei would effectively serve to gate cortico-thalamo-cortical signals from layer V of motor cortex to input layers of premotor cortex. For an evolutionary point of view, see Granger, Behav & Brain Sci., (2006)

A related modulatory or gating view of basal ganglia output is that expressed by Houk (1995): basal ganglia inputs to thalamus serve to register or negate sensory contexts into working memory, by pushing a bistable layer VI feedback loop into one of two dynamical regimes.

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Internal references

See Also

Basal Ganglia, Brainstem, Cortex, Models of Visual Cortex, Neuroanatomy,Thalamocortical Circuit, Thalamocortical Oscillations, Thalamus, Visual Cortex

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