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date: 27 May 2018

Thalamocortical Interactions for Sensory Processing

Summary and Keywords

The thalamocortical pathway is the main route of sensory information to the cerebral cortex. Vision, touch, hearing, taste, and balance all depend on the integrity of this pathway that connects the thalamic structures receiving sensory input with the cortical areas specialized in each sensory modality. Only the ancient sense of smell is independent of the thalamus, gaining access to cortex through more anterior routes. While the thalamocortical pathway targets different layers of the cerebral cortex, its main stream projects to the middle layers and has axon terminals that are dense, spatially restricted, and highly specific in their connections. The remarkable specificity of these thalamocortical connections allows for a precise reconstruction of the sensory dimensions that need to be most finely sampled, such as spatial acuity in vision and sound frequency in hearing. The thalamic axon terminals also segregate topographically according to their stimulus preferences, providing a simple principle to build cortical sensory maps: neighboring values in sensory space are represented by neighboring points within the cortex.

Thalamocortical processing is not static. It is continuously modulated by the brain stem and corticothalamic feedback based on the level of attention and alertness, and during sleep or general anesthesia. When alert, visual thalamic responses become stronger, more reliable, more sustained, more effective at sampling fast changes in the scene, and more linearly related to the stimulus. The high firing rates of the alert state make thalamocortical synapses chronically depressed and excitatory synaptic potentials less dependent on temporal history, improving even further the linear relation between stimulus and response. In turn, when alertness wanes, the thalamus reduces its firing rate, and starts generating spike bursts that drive large postsynaptic responses and keep the cortex responsive to sudden stimulus changes.

Keywords: thalamus, cortex, primary visual cortex, lateral geniculate nucleus, somatosensory cortex, auditory cortex

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Figure 1. First-order and higher-order thalamic relays. (Left) Drawing of human thalamus illustrating first-order (orange) and higher-order (purple) thalamic nuclei. A, Anterior nuclei; MD, mediodorsal nucleus; IML, internal medullary lamina; P, pulvinar; MGNv, medial geniculate nucleus (ventral portion); LGNd, lateral geniculate nucleus (dorsal portion); LP, lateral posterior nucleus; VP, ventral posterior nucleus; LD, lateral dorsal nucleus; VL, ventral lateral nucleus; VA, ventral anterior nucleus. (Right) Main inputs and outputs of first-order and higher-order thalamic nuclei.

Thalamocortical projections originate in two different types of thalamic structures that Ray Guillery called first-order and higher-order relays (Guillery, 1995). First-order relays receive input from peripheral senses, project densely to the middle layers of primary sensory cortex, and receive feedback from cortical layer 6, making a loop of thalamo-cortical-thalamic communication. Higher-order relays receive inputs from layer 5 of primary sensory cortex and project to multiple cortical areas making a loop of cortico-thalamo-cortical communication (Sherman & Guillery, 2002) (Figure 1). All sensory information passes through first-order thalamic relays with the exception of olfaction (Courtiol & Wilson, 2015). The thalamic afferents originating in first-order relays target very specific regions of primary sensory cortex and were called by Rafael Lorente de No specific afferents (Lorente de No, 1938). In contrast, the afferents originating in higher-order relays target broader cortical regions and are called non-specific. Here, we will focus on thalamocortical interactions from specific afferents that originate in first-order relays, with a particular emphasis on visual thalamus. We start by describing the specificity and strength of thalamocortical connections and then review the contribution of corticothalamic pathways and modulation of thalamocortical processing by brain state.

Specificity of Thalamocortical Connections

The “specific” afferents of Lorente the No included thalamocortical afferents connecting the lateral geniculate nucleus (LGN) to visual cortex, the medial geniculate nucleus (MGN) to auditory cortex and the ventrobasal nucleus (VB) to somatosensory cortex. Lorente the No noticed that these afferents “divide repeatedly into numerous branches, forming a plexus located chiefly in layer IV” (Lorente de No, 1938). Later studies using intracellular fillings were able to reconstruct the axon terminals of thalamocortical afferents more completely and study their response properties. Consistent with the original observations of Lorente de No, thalamocortical afferents were found to project densely to layer 4 and send weaker projections to other layers such as layer 6 and the bottom of layer 3 (Figure 2). The spread of thalamocortical axon terminals can range from a single, dense clump in layer 4 of ~300 μ‎m diameter and covering just 0.07 mm2 of cortical surface (e.g., parvocellular cells from macaque LGN projecting to layer 4Cβ‎ of cortical area 17; Blasdel & Lund, 1983) to a broad axon-terminal of ~1.8 mm diameter projecting to both layers 4 and 6 and covering 2.8 mm2 of cortical surface (e.g., large Y cells from cat LGN projecting to cortical area 18; Humphrey, Sur, Uhlrich, & Sherman, 1985a).

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Figure 2. Morphological specificity of thalamocortical axons. (Left) Receptive field (top) and axon terminal (bottom) of an ON-center X LGN afferent with a center diameter of 0.6 degrees projecting to cat area 17 (adapted with permission from Humphrey et al., 1985b). (Right) Receptive field (top) and axon terminal (bottom) of a hair VPL afferent with a receptive field covering the entire dorsal part of the forearm and projecting to cat primary somatosensory cortex.

Adapted with permission from Landry and Deschenes (1981). VPL, ventroposterolateral.

The afferents with the largest axon terminals do not cover cortical space homogeneously. Instead, they form clumps separated by gaps (Humphrey, Sur, Uhlrich, & Sherman, 1985b) that reflect discontinuities in the representation of sensory space within the cortex. For example, the map of visual space in cortical layer 4 of cats and macaques is fragmented by alternating stripes that contain separate sensory representations for each eye. The width of these cortical stripes is approximately 0.5 mm in cats. Because LGN afferents are monocular, those with the largest axon terminals form at least two clumps of 0.5 mm aligned with the cortical representation of one eye and separated by a 0.5 mm gap aligned with the cortical representation of the other eye. Therefore, a large axonal arbor can cover a total horizontal distance of 1.5 mm or more. The size of the axon terminal from a thalamocortical afferent is determined by the size of the cortical region representing its receptive field. Because the region of visual cortex representing central vision is larger than the region of somatosensory cortex representing a leg, an LGN afferent with a small receptive field of just 0.6 degrees can occupy the same or larger cortical area than an afferent from the ventroposterolateral (VPL) nucleus with a receptive field covering the entire forearm (Figure 2).

The specificity of thalamocortical afferents is determined not only by receptive field size but also by other properties. For example, LGN afferents from the left eye make connections with cortical regions dominated by the left eye, and LGN afferents from the OFF pathway concentrate in cortical regions dominated by OFF responses (Jin, Wang, Swadlow, & Alonso, 2011; Jin et al., 2008; McConnell & LeVay, 1984; Norton, Rager, & Kretz, 1985; Smith, Whitney, & Fitzpatrick, 2015; Wang et al., 2015; Zahs & Stryker, 1988). The segregation of thalamic afferents by sensory preference provides the layout of cortical maps, as originally demonstrated for LGN afferents carrying information from different eyes, which form ocular dominance maps (Wiesel, Hubel, & Lam, 1974) (Figure 3), and more recently for LGN afferents with different contrast polarity, which form orientation maps (Jin et al., 2011; Kremkow, Jin, Wang, & Alonso, 2016; Lee, Huang, & Fitzpatrick, 2016). Cortical maps are organized according to multiple sensory parameters (Figure 3). In the visual cortex of carnivores and primates, multiple sensory maps can coexist (Hubener, Shoham, Grinvald, & Bonhoeffer, 1997; Yu, Farley, Jin, & Sur, 2005; Nauhaus, Nielsen, & Callaway, 2016) for spatial position (Blasdel & Campbell, 2001; Daniel & Whitteridge, 1961; Hubel & Wiesel, 1962), ocular dominance (Wiesel et al., 1974), orientation (Blasdel, 1992; Bonhoeffer & Grinvald, 1991; Hubel & Wiesel, 1962), direction (Shmuel & Grinvald, 1996; Weliky, Bosking, & Fitzpatrick, 1996), dark/light contrast polarity (Smith et al., 2015; Wang et al., 2015), and spatial frequency (Issa, Trepel, & Stryker, 2000; Nauhaus, Nielsen, & Callaway, 2016). The cortical segregation for each dimension varies across animals and sensory modalities. For example, cortical segregation in ocular dominance columns is very pronounced in humans and macaques (Adams, Sincich, & Horton, 2007), variable in squirrel monkeys (Adams & Horton, 2003), and non-existent in lagomorphs (Van Sluyters & Stewart, 1974) or rodents (Drager, 1975). Moreover, within cats, cortical segregation for dark/light polarity is less pronounced than for ocular dominance (Kremkow et al., 2016).

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Figure 3. The topographic arrangement of thalamocortical axons in the cerebral cortex provides the layout for cortical maps that are organized according to multiple sensory parameters. (a) Cortical map for retinotopy in the macaque visual cortex (adapted with permission from Tootell et al., 1988). (b) Cortical map for ocular dominance in the macaque visual cortex (adapted with permission from Hubel & Wiesel, 1977). (c) Cortical map for orientation preference in the tree shrew.

Adapted with permission from Bosking et al. (1997).

The receptive fields of the thalamic afferents making monosynaptic connection within the same cortical point are overlapped in sensory space (Figure 4a). For example, in cat visual cortex, afferents making monosynaptic connections within the same orientation column have receptive fields that are spatially overlapped (Jin et al., 2011). Most receptive fields are centered at the same spatial position, while adjacent positions in visual spaced are covered by a smaller number of afferents (Figure 4a). In fact, thalamic afferents with receptive fields separated by more than two receptive-field center-diameters rarely make connection within the same orientation column in cat visual cortex (Figure 4b) (Jin et al., 2011).

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Figure 4. Topographic segregation of thalamic afferents by spatial position and ON/OFF polarity provides the layout for retinotopic and orientation maps in visual cortex. (a) Receptive fields of thalamic afferents making monosynaptic connection within the same orientation column in cat visual cortex. In this example, 22 afferents have receptive fields covering the same point in visual space. (b) ON (red) and OFF (blue) receptive fields overlap extensively to maximize the accurate representation of spatial position. (c) However, ON and OFF receptive fields do not cover visual space homogeneously. Instead, the population receptive field (ON-OFF subtraction) has parallel subregions arranged along the preferred orientation of the cortical column. (d) Horizontal track through cat visual cortex showing receptive fields for dark (D in blue, top row), light (L in red, middle row), and light-dark stimuli (L-D, bottom row). Each row shows a receptive field series across a horizontal track of 1.2 mm (0.1 mm separation between adjacent receptive fields). Notice that the receptive field position measured with darks moves slightly upward in the visual field while the receptive fields measured with lights rotate around the darks. The polar plots at the bottom show changes in orientation/direction preference with cortical distance (the small circles illustrate the orientation of the L-D receptive fields).

Adapted with permission from Jin et al. (2011), for a–c and Kremkow et al. (2016) for d.

The receptive fields of thalamocortical afferents making connection at the same cortical point overlap even if they have different preferences for light (ON) and dark polarity (OFF). This remarkable specificity of thalamocortical connections by spatial position is important to build accurate maps of visual space in the cerebral cortex (Figure 3a). At the same time, the extensive receptive field overlap does not prevent a cortical segregation for ON and OFF responses. Most cortical points are dominated by either ON or OFF afferents (Jin et al., 2008, 2011; McConnell & LeVay, 1984; Norton et al., 1985; Zahs & Stryker, 1988) and the ON and OFF afferents at each cortical point do not cover visual space homogeneously. Instead, the population receptive field (calculated as ON-OFF subtraction) has ON and OFF parallel subregions that match the preferred orientation of the cortical column. Changes in the arrangement of the ON and OFF subregions within the population receptive field are associated with changes in orientation preference within the cortex (Figure 4d) (Kremkow, Jin, Wang, & Alonso, 2016; Lee et al., 2016). Therefore, the extensive receptive field overlap of the thalamic afferents at each cortical point allows building an accurate map for spatial position. In turn, the rotation of ON and OFF receptive fields within the cortical representation of each spatial position allows representing all orientations for each spatial position in an orientation map.

Thalamocortical afferents not only target specific cortical regions, they also make connections with specific neurons within each region. This specificity is based on a precise match of the sensory properties represented by both afferents and cortical targets (Alonso & Swadlow, 2005; Reid & Alonso, 1996). Connections between LGN and primary visual cortex involve neurons with receptive fields matched in position and preference for dark/light polarity (Tanaka, 1983; Alonso, Usrey, & Reid, 1996, 2001; Reid & Alonso, 1995) (Figure 5a). Connections between MGN and auditory cortex involve neurons matched in their stimulus preferences for sound frequency (Miller, Escabi, Read, & Schreiner, 2001a; Miller et al., 2001b) (Figure 5b). Connections between the anterodorsal thalamic nucleus (ADn) and post-subicular cortex involve neurons precisely matched in their preferences for head direction (Peyrache, Lacrois, Petersen, & Buzsaki, 2015) (Figure 5c). The specificity of the connections is more precise for cortical excitatory neurons than broadly tuned inhibitory neurons (Bruno & Simons, 2002; Peyrache et al., 2015; Swadlow & Gusev, 2002; Zhuang et al., 2013), most likely because the two types of neurons differ in their thalamocortical convergence (Alonso & Swadlow, 2005). However, even broadly tuned neurons have sensory properties that are well matched with their thalamic afferents (Figure 5d–f). For example, putative inhibitory neurons in the visual cortex of rabbits (Figure 5d) respond similarly to light and dark stimuli at each position of their large receptive fields, and the lack of dark/light selectivity within their receptive fields matches their lack of specificity for connections with ON and OFF thalamic inputs. Similarly, putative inhibitory neurons in the rabbit somatosensory cortex (Figure 5e) respond to all whisker directions of movement, and their lack of direction selectivity matches their lack of specificity for connections with thalamic inputs signaling different directions of movement. Finally, putative inhibitory cortical neurons in the mouse post-subicular cortex (Figure 5f) respond similarly to all head directions, and their lack of head-direction selectivity matches their lack of specificity for connections with thalamic inputs signaling different head directions. Therefore, in multiple sensory systems, the low stimulus selectivity of broadband cortical inhibitory neurons is strongly associated with the low connection specificity of their thalamic inputs.

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Figure 5. Functional specificity of thalamocortical axons. (a) Cross-correlogram between an LGN afferent and a cortical cell in cat primary visual cortex showing a peak displaced from zero (asterisk), which is indicative of a monosynaptic connection. The inset on the right shows the receptive field of the cortical cell as a contour plot (ON subregion in red and OFF subregion in blue) and the receptive field of the LGN cell is shown as a filled blue circle with purple line superimposed on the cortical receptive field. (b) Same for MGN cell (green) and primary auditory cortex (red-orange) in the cat. (c) Same for antero-dorsal thalamic nucleus (Adn) and post-subicular cortexin mice. (d–f) Same for three putative inhibitory neurons in primary visual cortex (d) and primary somatosensory cortex of rabbits (receptive field in e shows tuning for vibrissa direction), and Post-subicular cortex in mice (f).

Adapted with permission from Alonso and Swadlow (2005) for a, Miller, Escabi, and Schreiner (2001b) for b, Peyrache et al. (2015) for c and f, Zhuang et al. (2013) for d, and modified from Swadlow and Gusev (2002) for e.

Among all the examples relating low stimulus selectivity with low thalamocortical specificity, the one from the somatosensory cortex is particularly striking. In lagomorphs and rodents, each whisker is represented by an individual portion of the somatosensory cortex of approximately 0.5 mm in diameter that has the shape of a barrel (Figure 6) (Bosman et al., 2011; Wilson, Johnston, Goldstein, & Blue, 2000; Woolsey & Van der Loos, 1970). Each thalamic neuron projecting to a cortical barrel responds to the same whisker but can signal different directions of movement. Because each cortical excitatory neuron in a cortical “barrel” receives input from just a small number of thalamic neurons, cortical excitatory neurons respond only to a narrow range of whisker directions (Bruno, Khatri, Land, & Simons, 2003). In contrast, broadband cortical inhibitory neurons receive input from a much larger number of thalamic neurons and respond to all directions of whisker motion (Bruno & Simons, 2002; Swadlow & Gusev, 2002). Therefore, in barrel somatosensory cortex, cortical excitatory neurons relay the direction selectivity of thalamic neurons by connecting with a limited number of thalamic inputs (limited thalamocortical convergence), while broadband inhibitory neurons discard the direction selectivity from the thalamic neurons by connecting with multiple thalamic inputs (extensive thalamocortical convergence).

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Figure 6. Thalamocortical organization in somatosensory “barrel” cortex. (a) Whiskers of a rodent marked with letters. (b) Each whisker is represented in the somatosensory cortex by a region of ~0.5 mm with the shape of a barrel. (c) The somatosensory barrel cortex in the rat, mouse, and rabbit.

Adapted with permission from Wilson et al., 2000, for a and b and Bosman et al. (2011) for c.

While high thalamocortical convergence makes cortical neurons less selective to stimuli, it increases their sensory sensitivity (Figure 7). Inhibitory cortical neurons are known to receive stronger and more convergent thalamocortical input than excitatory cortical neurons (Cruikshank, Lewis, & Connors, 2007; Swadlow, Beloozerova, & Sirota, 1998) and respond to weaker stimuli in both visual (lower luminance contrast) (Contreras & Palmer, 2003; Zhuang et al., 2013) and somatosensory cortex (lower-amplitude whisker-deflections) (Swadlow, 1989). Extensive thalamocortical convergence could help to increase stimulus sensitivity by adding the high spontaneous firing rates of a large number of thalamic inputs that bring subthreshold baseline activity closer to threshold. In addition, stimulus sensitivity could be increased by adding postsynaptic responses from a large number of thalamic inputs that are synchronously activated by the stimulus (Alonso, Yeh, & Stoelzel, 2008; Alonso et al., 1996; Bruno & Sakmann, 2006; Cardin, Kumbhani, Contreras, & Palmer, 2010; Roy & Alloway, 2001; Schoonover et al., 2014; Swadlow et al., 1998; Yeh, Stoelzel, Weng, & Alonso, 2009). In summary, cortical neurons that receive input from a large number of thalamic afferents compromise their stimulus selectivity at the expense of becoming more sensitive to low-amplitude stimuli. In turn, cortical neurons making specific connections with fewer thalamic inputs become more selective at the expense of losing their stimulus sensitivity (Figure 7).

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Figure 7. Functional consequences of thalamocortical convergence. (a) Drawing showing greater thalamocortical convergence in an inhibitory cortical neuron (multicolor checkerboard in layer 4) than excitatory cortical neurons (single color circles with radial curved lines). Each thalamic neuron is represented by one color (circles in thalamus). (b) As a consequence of the greater thalamocortical convergence (low connection specificity), broadband inhibitory neurons are less selective to stimuli (respond to more stimulus directions) but more sensitive (respond to lower stimulus amplitudes).

Adapted with permission from Alonso and Swadlow (2005).

Strength of Thalamocortical Connections

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Figure 8. Functional consequences of thalamocortical divergence. (a) Cross-correlograms indicative of multiple monosynaptic connections made by a single VP thalamic neuron with multiple cortical inhibitory neurons in rabbit somatosensory cortex (adapted with permission from Swadlow & Gusev, 2002). (b) Spike-triggered current- source-density analysis demonstrates the strong impact of a single LGN neuron in the visual cortex of awake rabbits. The figure shows the impact of two single LGN neurons targeting different depths of cortical layer 4. The LGN neuron generating transient visual responses targeted the bottom part of layer 4 (left) and the one generating sustained responses targeted the top part (right). The dotted line at 0 time delay shows the time of the spike generated in thalamus. The dotted line bracket shows the thickness of layer 4.

Adapted with permission from Stoelzel et al. (2008).

Thalamocortical connections originating in first-order relays are not only specific, they are stronger than corticocortical synapses, a property that should be important to reliably transmit sensory information to the cortex. Thalamic afferents make only a small percentage (5–25%) of all the excitatory synapses within a single spiny neuron in layer 4 of visual cortex (Ahmed, Anderson, Douglas, Martin, & Nelson, 1994; Benshalom & White, 1986; Peters & Payne, 1993). Moreover, the number of synapses in a single thalamocortical connection can be an order of magnitude smaller than the number of synapses made by a single connection between a peripheral afferent and a thalamic neuron. For example, while an LGN cell makes fewer than 10 synapses to connect to a single cortical cell, a retinal afferent makes almost 200 synapses in its connection with a single LGN cell (Freund, Martin, Somogyi, & Whitteridge, 1985; Hamos, Van Horn, Raczkowski, Uhlrich, & Sherman, 1985). That being said, while modest in number, thalamocortical synapses have many properties that make them remarkably strong. They are larger, located more proximally within the dendrite (Ahmed et al., 1994; Richardson, Blundon, Bayazitov, & Zakharenko, 2009; Schoonover et al., 2014; White & Rock, 1981), have more release sites and higher reliability (Gil, Connors, & Amitai, 1999), and can generate larger excitatory postsynaptic potentials (EPSPs) than intracortical synapses (Gil et al., 1999; Stratford, Tarczy-Hornoch, Martin, Bannister, & Jack, 1996).

In addition, thalamocortical neurons sharing input from the same afferent are driven with precise synchrony (Alonso et al., 1996; Bruno & Sakmann, 2006; Roy & Alloway, 2001; Stanley et al., 2012; Wang, Webber, & Stanley, 2010; Yeh et al., 2009), providing a powerful mechanism to simultaneously activate multiple thalamocortical synapses in the same cortical neuron. Also, baseline firing rates are much higher in thalamic afferents than cortical excitatory neurons (Bereshpolova et al., 2011), providing yet another mechanism to boost thalamocortical excitation. Finally, abundant recurrent connections between cortical excitatory neurons (Ahmed et al., 1994) further amplify the impact of thalamocortical inputs (Chung & Ferster, 1998; Ferster, Chung, & Wheat, 1996; Li, Ibrahim, Liu, Zhang, & Tao, 2013; Lien & Scanziani, 2013; Reinhold, Lien, & Scanziani, 2015). Therefore, multiple mechanisms cooperate to make thalamocortical connections strong, while the cortical impact of a thalamocortical impulse is continuously adjusted by the recurrent intracortical network. In fact, the strength of some thalamocortical connections is so pronounced that, in some cases, up to 20% of the spikes from a single thalamic afferent can drive spikes in a cortical neuron with a minimum temporal jitter of just 1–2 msec (Swadlow & Gusev, 2002) (Figure 8a). Moreover, a single thalamocortical afferent can drive a large population of cortical neurons and generate a powerful current sink in the cortex that is exquisitely restricted to just a few hundred micrometers of the layer 4 thickness (Jin et al., 2008, 2011; Stoelzel, Bereshpolova, Gusev, & Swadlow, 2008; Swadlow, Gusev, & Bezdudnaya, 2002) (Figure 8b).

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Figure 9. Synaptic depression in thalamocortical connections. (Top) In thalamocortical (TC) EPSPs separated by intervals shorter than 100 msec, the second EPSP (TC2) is smaller than the first EPSP (TC1), which is indicative of thalamocortical synaptic depression. (Bottom) The reverse is observed in intracortical (IC) EPSPs; the second EPSP (IC2) is larger than the first EPSP (IC1), which is indicative of intracortical facilitation.

Adapted with permission from Gil et al. (1997).

While thalamocortical afferents have a powerful impact in the cortex, their strength is greatly dependent on temporal history. The second EPSP generated by two consecutive impulses is depressed in thalamocortical connections but can be enhanced in cortico-cortical connections. Therefore, the average thalamocortical synapse shows pronounced paired-pulse depression while the average cortico-cortical synapse in layer 4 shows paired-pulse facilitation (Gil, Connors, & Amitai, 1997; Stratford et al., 1996) (Figure 9). As a consequence of this paired-pulse depression, thalamic spikes separated by intervals of 100 msec in the awake brain generate EPSPs that can be half the amplitude of spikes separated by more than a 500 msec interval (Stoelzel et al., 2008; Swadlow et al., 2002). Because thalamic firing rates are high in the awake state, most spikes are separated by short inter-spike intervals in awake animals, and thalamocortical synapses become chronically depressed (Alonso & Swadlow, 2015; Boudreau & Ferster, 2005; Castro-Alamancos & Oldford, 2002; Reinhold et al., 2015; Swadlow et al., 2002).

Corticothalamic Feedback

Just as layer 4 cortical neurons receive only a small percentage of excitatory synapses from the thalamus, neurons in specific thalamic nuclei such as the LGN receive only a small percentage (<10%) of their excitatory input from the sensory periphery (Erisir, Van Horn, & Sherman, 1997; Van Horn, Erisir, & Sherman, 2000). The remainder consists of inputs that modulate brain state, inhibitory inputs from both intrinsic interneurons and the thalamic reticular nucleus, and a massive input from the sensory neocortex.

The feedback inputs from the cortex originate mostly in layer 6 and are topographically aligned with the thalamic neurons. Neurons located at different depths within layer 6 may form parallel corticothalamic streams for different sensory properties (Briggs & Usrey, 2009; Briggs, Kiley, Callaway, & Usrey, 2016; Fitzpatrick, Usrey, Schofield, & Einstein, 1994; Killackey & Sherman, 2003; Tsumoto & Suda, 1980). Notably, corticothalamic inputs provide far more synapses to thalamic relay neurons than the inputs from the sensory periphery, although they are much weaker in strength. Thus, unlike inputs from the periphery, which can depolarize thalamic cells sufficiently to “drive” their spiking activity (Cleland, Dubin, & Levick, 1971; Mastronarde, 1992; Usrey, Reppas, & Reid, 1998, 1999), the effect of layer 6 corticothalamic inputs appears to be largely modulatory. The reasons for the differing effects on thalamic neurons of “driver” versus “modulator” synapses are numerous (Figure 10) (Crandall, Cruikshank, & Connors, 2015; Crick & Koch, 1998; Lee & Sherman, 2010; McCormick & von Krosigk, 1992; Sherman & Guillery, 1998): (1) synapses formed by peripheral inputs are larger than those formed by corticothalamic neurons, and they make more contacts with more release sites onto each thalamic neuron; (2) synaptic contacts from the periphery are formed closer to the region of spike generation near the soma, whereas corticothalamic synapses are more distal on the dendritic tree, where they may have a less potent effect on spike generation; (3) although both peripheral sensory inputs and corticothalamic inputs are glutamatergic, receptor mechanisms differ, with peripheral inputs being mediated largely by fast, ionotropic receptors, and corticothalamic inputs by slower, metabotropic receptors; (4) whereas peripheral synaptic inputs have high release probability, and exhibit paired pulse depression, corticothalamic inputs have a low release probability, and exhibit facilitation; (5) axons mediating input from the periphery are uniformly fast conducting, and the onset of a sensory stimulus can generate a near synchronous barrage of input from multiple sensory neurons that synapse upon a relay cell. By contrast, corticothalamic axons exhibit an enormous range of conduction times, spanning many tens of milliseconds, and inputs from corticothalamic neurons activated by the same sensory stimulus may arrive in the thalamus asynchronously, >30 msec apart, and are therefore unlikely to summate temporally (Briggs & Usrey, 2009; Ferster & Lindstrom, 1983; Swadlow & Weyand, 1987).

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Figure 10. Drivers and modulators in the thalamocortical pathway. Thalamic neurons receive two types of inputs, drivers and modulators. Drivers and modulators differ in anatomical origin, types of receptors they use, size of the EPSPs that generate, synaptic location, and density/thickness of their axon terminals. In LGN, the drivers originate in the retina and the modulators in the brain stem and visual cortex.

Adapted from Lee and Sherman (2010).

It is notable that the properties of corticothalamic inputs described above hold largely for the projection from cortical cells in layer 6 to specific thalamic nuclei. By contrast, layer 5 corticothalamic neurons have fast conducting axons, form large synaptic boutons, and may be potent drivers of their post-synaptic thalamic targets, comprising a mechanism for powerful cortico-thalamo-cortical communication (Guillery & Sherman, 2002; Sherman & Guillery, 2011).

Understanding of the function(s) of the massive corticothalamic feedback to specific thalamic nuclei has proved a challenge for investigators that is somewhat reminiscent of the proverbial story of the blind men’s investigation of the elephant in which each blind man infers a very different beast in line with his limited tactile experience. Different schools of investigation, using different tools and asking different questions, have posited a host of functions for this feedback, each very consistent with some aspect of the available data. These range from the modulation of receptive field center/surround mechanisms (Alitto & Usrey, 2003; Sillito & Jones, 2002; Temereanca & Simons, 2004), control of response gain in thalamus and cortex (Olsen, Bortone, Adesnik, & Scanziani, 2012), modulation of the coherence of global thalamic oscillations and of brain states (Contreras & Steriade, 1996; Crandall et al., 2015; Eyding, Macklis, Neubacher, Funke, & Worgotter, 2003; McCormick & von Krosigk, 1992), to mediating shifts in attention (Crick, 1984; Wells, Wimmer, Schmitt, Feng, & Halassa, 2016; Wimmer et al., 2015). Hopefully, as in the proverbial tale, each of these conclusions is, in large part, correct, but not the entire story (which will be revealed at some future date!).

Thalamocortical Changes with Brain State

While thalamocortical excitatory synapses are chronically depressed in the awake state, chronic depression is reduced during sleep or general anesthesia, as thalamic firing rates decrease. With the onset of drowsiness and sleep, the thalamus transitions from a depolarized state to a hyperpolarized state and retino-geniculate synaptic transmission becomes weaker (Weyand, 2007). In the depolarized state, thalamic neurons are continuously transmitting high spiking rates to the cortex, a state that is commonly referred as “tonic mode.” In the hyperpolarized state, thalamic neurons generate low spiking rates frequently interspersed with spike bursts, a thalamic state that is commonly referred as “burst mode” (Sherman, 2001a,b) (Figure 11).

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Figure 11. Thalamic tonic mode and burst mode. (Top) When the membrane potential of the thalamic neuron is depolarized, a depolarizing pulse generates tonic firing (tonic mode). (Bottom) When the membrane potential of the thalamic neuron has been hyperpolarized for >100 msec, a depolarizing pulse generates a calcium conductance and a spike burst (burst mode).

Adapted with permission from Sherman (2001a, b).

Spike bursts are generated in the thalamus by a low-threshold, voltage-dependent calcium conductance that is activated when the membrane potential is hyperpolarized (Deschenes, Paradis, Roy, & Steriade, 1984; Deschenes, Roy, & Steriade, 1982; Jahnsen & Llinas, 1984a, b; Roy, Clercq, Steriade, & Deschenes, 1984; Steriade & Llinas, 1988). To generate a spike burst, a thalamic neuron needs to be hyperpolarized for a period of at least 100 msec and then rapidly depolarized. Therefore, any stimulus that generates this hyperpolarization-depolarization sequence in a hyperpolarized thalamic neuron will be able to trigger a calcium spike that causes a burst of action potentials (Alitto, Weyand, & Usrey, 2005; Lesica et al., 2006; Lu, Guido, & Sherman, 1992; Rivadulla, Martinez, Grieve, & Cudeiro, 2003; Weyand, Boudreaux, & Guido, 2001). Because spike bursts occur when thalamic firing rates are low, their cortical impact is enhanced by the large EPSPs resulting from the reduced thalamocortical synaptic depression. As a consequence, the combination of large EPSPs and short inter-spike intervals make thalamic bursts very effective at driving cortical responses (Swadlow & Gusev, 2001) and transmitting abrupt changes in the sensory environment (Lu et al., 1992; Sherman, 2001a; Sherman & Guillery, 1996).

Thalamic bursts are common when awake subjects are drowsy, and their frequency can dramatically increase in transitions from alert to non-alert states (Bezdudnaya et al., 2006; Stoelzel, Bereshpolova, & Swadlow, 2009; Swadlow & Gusev, 2001). As the brain transitions into a non-alert state, there is a sudden increase in thalamic burst frequency that is accompanied by a pronounced and widespread change in cortical activity (Figure 12) (Bezdudnaya et al., 2006). The hippocampus changes from theta rhythm to irregular activity, and visual cortex changes from desynchronized low-amplitude to synchronized high-amplitude activity. Changes in thalamic burst frequency in the awake brain can be very fast. Such fast changes have been described in LGN (Bezdudnaya et al., 2006; Weyand et al., 2001) and ventrobasal somatosensory thalamus (Ramcharan, Gnadt, & Sherman, 2000; Swadlow & Gusev, 2001) and are likely to be present in other specific thalamic nuclei.

Thalamocortical Interactions for Sensory ProcessingClick to view larger

Figure 12. Thalamocortical function can change from tonic to burst mode within a single second in the awake brain. (a) Recordings in the awake rabbit from two LGN neurons (LGN 1 and 2), and electroencephalogram (EEG) from hippocampus (Hipp EEG) and visual cortex (superficial, Cortex S, and deep layers, Cortex D). In the LGN recordings, each line represents a spike and each asterisk a spike burst. Notice the sudden change at time zero in all recordings. (b) Average burst rates for 10 LGN cells. Notice the sudden increase in burst rate within a single second (asterisk illustrates statistical significance).

Adapted with permission from Bezdudnaya et al. (2006).

The increase in burst firing with low alertness is associated with a reduction in the strength of visual responses. An important consequence of this pronounced reduction in thalamic firing is a loss of linearity in the transformation between stimulus and response (Cano, Bezdudnaya, Swadlow, & Alonso, 2006; Coenen & Vendrik, 1972; Hubel, 1960; Livingstone & Hubel, 1981; Maffei, Moruzzi, & Rizzolatti, 1965). For example, in the alert state, visual thalamic neurons respond to a sinusoidal grating with a sinusoidal modulation of their firing rates that closely resembles the stimulus (Figure 13a). However, when alertness is reduced, the modulation of the visual response is no longer sinusoidal and concentrates in the stimulus transient (Figure 13b). This change in response linearity between tonic and burst mode can also be demonstrated in vitro by injecting a sinusoidal-modulated current in a thalamic neuron that is either depolarized or hyperpolarized (Figure 13c). It can also be demonstrated under general anesthesia by separating the visual responses from bursts and single spikes (Lu et al., 1992; Sherman, 2001b). Therefore, any pronounced reduction in thalamic firing rate, be caused by drowsiness, sleep, general anesthesia, or current injection, disrupts the linear transformation between stimulus and response while increasing the frequency of thalamic bursts, which are more effective at detecting sudden stimulus changes than single spikes (Sherman, 2001b; Sherman & Guillery, 2002).

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Figure 13. Changes in response linearity with brain state. (a) Visual responses of an LGN neuron to a sinusoidal drifting grating with different contrasts in an alert rabbit. Each dot is a spike and each raster line a trial of visual stimulation. Histograms for each contrast are shown below the rasters. The right panel shows the average response to two drifting cycles (dotted lines are sinusoidal fits). (b) Same neuron in the non-alert state. Notice that the average response on the right is no longer sinusoidal. This loss of response linearity is characteristic of brain states that exhibit neocortical synchronization (slow-wave activity) such as low alertness, sleep, or general anesthesia. (c) Contrast response of an LGN neuron recorded in vitro to sinusoidal current injections when the neuron was depolarized (tonic mode) and hyperpolarized (burst mode).

Adapted with permission from Cano et al. (2006) for a–b and Sherman (2001b) for c.

Low alertness also causes a strong attenuation of thalamic responses to high temporal frequencies, compromising the thalamocortical transmission of rapid changes in the visual environment (Figure 14). In the awake rabbit (Bezdudnaya et al., 2006), many thalamic neurons can follow temporal frequencies as high as 40 Hz when alert (Figure 14a), but the responses to high temporal frequencies are completely abolished when alertness wanes (Figure 14b). Both low alertness (Figure 14c) and general anesthesia (Mukherjee & Kaplan, 1995; Reinhold et al., 2015) (Figure 14d) cause a pronounced shift in temporal frequency tuning toward lower frequencies but spike bursts provide a mechanism to preserve the transmission of sudden stimulus changes to the cortex.

Thalamocortical Interactions for Sensory ProcessingClick to view larger

Figure 14. Changes in temporal frequency tuning with brain state. (a) Visual responses from an LGN neuron in an alert rabbit. Each line shows the visual response to a grating flickering in counterphase at different frequencies. Notice that the neuron can respond to frequencies as high as 40 Hz. (b) Visual responses from the same LGN neuron in the non-alert state. Notice that the neuron no longer responds to the higher temporal frequencies. (c) Temporal frequency tuning during alert (triangles) and non-alert states (squares). Notice that the thalamic neuron responds stronger and follows higher temporal frequencies in the alert than the non-alert state. (d) Visual responses from LGN multiunit activity recorded in the mouse, when awake (left) and under general anesthesia (right). As with low alertness, general anesthesia reduces the responses to high temporal frequencies.

Adapted with permission from Bezdudnaya et al. (2006) for a, and Reinhold et al. (2015) for b.

The reduced firing rates during low alertness also make thalamic responses less sustained. Transient thalamic neurons can respond to high temporal frequencies (Figure 14a) but cannot sustain their responses for more than a few hundred milliseconds. In contrast, sustained thalamic neurons can maintain their responses for many seconds and continuously transmit information about the contrast polarity and duration of a static stimulus. Both low alertness (Bezdudnaya et al., 2006; Swadlow & Weyand, 1985) and general anesthesia (Worgotter et al., 1998) strongly reduce the sustained responses in the visual thalamus while preserving the response transients. Most LGN neurons can respond to stimulus transients under general anesthesia. However, as the level of anesthesia is reduced, many thalamic neurons also start generating robust sustained responses that can last for the entire duration of the stimulus (Figure 15) (Worgotter et al., 1998). Alertness also increases spontaneous firing rate in thalamic neurons, however, this increase is not always transferred to the cortex. In rabbit visual cortex, alertness does not affect the low spontaneous rates of layer 4 excitatory neurons and can reduce the spontaneous rate of layer 4 inhibitory neurons while increasing the spontaneous rates of their thalamic inputs (Bereshpolova et al., 2011). Therefore, while alertness enhances the strength of visual responses in both thalamus and visual cortex, it can leave unaffected the spontaneous rates of cortical excitatory neurons, which may be important to maximize the signal-to-noise ratio of visual cortical responses.

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Figure 15. Changes in response duration with brain state. Visual responses from an LGN neuron to a flashing light spot measured under different levels of anesthesia in a cat, deep anesthesia (synchronized EEG) and light anesthesia (non-synchronized EEG). Notice that the response becomes more sustained under light anesthesia.

Adapted with permission from Worgotter et al. (1998).

Alertness also makes visual responses more reliable and less variable across repeated presentations of the same stimulus. Neurons respond to the same stimulus by generating slightly different number of spikes, and this variability increases from the sensory periphery to the cortex (Figure 16) (Kara, Reinagel, & Reid, 2000). Response variability is usually calculated as the ratio between the variance and the mean number of spikes generated by the multiple repetitions of the same stimulus, a ratio called the Fano Factor (FF=variance/mean) (Fano, 1947), after Ugo Fano, an Italian American physicist. If each stimulus presentation always generates the same number of spikes, the variability is zero (FF = 0) and the neuron is completely reliable. However, the spike count usually varies, making the Fano Factor larger than zero. Both brain state (Zhuang et al., 2014) and general anesthesia (Alitto, Moore, Rathbun, & Usrey, 2011) affects response variability, as quantified by the Fano Factor, but this effect is more pronounced in the cortex than in the visual thalamus (Alitto et al., 2011; Zhuang et al., 2014). While response variability can be nearly four times larger in the cortex than the retina under general anesthesia (Kara et al., 2000) (Figure 16), the difference is strongly reduced in the awake brain (Gur & Snodderly, 2006; Zhuang et al., 2014). Alertness makes visual cortical responses more reliable, which, together with the increased response strength, could help to improve visual detection.

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Figure 16. Response variability increases from retina to visual cortex in the anesthetized cat. (Top) Receptive fields from four neurons that were simultaneously recorded at three different stages of the visual pathway. From left to right, the figure shows receptive fields from a retinal ganglion cell (RGC), two LGN cells (LGN1, LGN2), and a cell in visual cortex (V1). (Bottom) Visual responses to a drifting grating measured in the simultaneous recordings. Each line illustrates a spike and each row a trial of visual stimulation. Retinal responses are very reliable (most stimulus presentations generate 6 or 7 spikes), however, the responses become more variable in LGN and visual cortex.

Adapted with permission from Kara et al. (2000).

Alertness also enhances the suppression of responses to non-preferred stimuli (Hei et al., 2014; Zhuang et al., 2014), a mechanism that is important to sharpen stimulus selectivity emerging from thalamocortical convergence (Celebrini, Thorpe, Trotter, & Imbert, 1993; De Valois, Yund, & Hepler, 1982; Li et al., 2012; Marino et al., 2005; Monier, Chavane, Baudot, Graham, & Fregnac, 2003; Nelson, Toth, Sheth, & Sur, 1994; Ringach, Hawken, & Shapley, 2003; Sillito, 1975; Xing, Ringach, Hawken, & Shapley, 2011). At the same time and just as important, alertness does not distort the thalamocortical transmission of several stimulus properties that are crucial in sensory processing, such as visual acuity and contrast sensitivity. Both thalamic neurons and cortical excitatory neurons in layer 4 have similar receptive field sizes, contrast sensitivity, orientation tuning, and direction tuning under different levels of alertness (Swadlow & Weyand, 1987; Zhuang et al., 2014), and a similar invariance in these basic stimulus properties can be demonstrated in cats that transition between sleep and arousal (Livingstone & Hubel, 1981) and mice that transition between periods of quiescence and locomotion (Niell & Stryker, 2010).

In summary, changes in brain state have a pronounced effect on thalamic firing and thalamocortical function. As the brain transitions from non-alert to alert states, visual responses become stronger (Figure 17a), more sustained, and more linearly related with the stimulus (Figure 17b), making a sinusoidal modulation of a stimulus accurately transformed in a sinusoidal modulation of a thalamic response. As alertness wanes, the sinusoidal replica of the stimulus is disrupted, but thalamic neurons remain responsive to stimulus transients (Figure 17b) and low temporal frequencies (Figure 17c), while generating spike bursts that also increase the transmission of stimulus transients (Figure 17b). Alertness also increases thalamic spontaneous activity, but this increase is not always transmitted to the cortex, as cortical excitatory neurons can maintain a low baseline activity under different levels of alertness (Figure 17d). Finally, alertness makes visual responses more reliable (Figure 17e), which could be important to facilitate stimulus detection.

Thalamocortical Interactions for Sensory ProcessingClick to view larger

Figure 17. Changes in thalamocortical function with brain state. (a) Thalamic visual responses increase in strength when thalamus transition from non-alert (blue) to alert (red) states. However, the contrast sensitivity (contrast that generates half-maximum response) is not affected. (b) An increase in alertness improves the linear relationship between stimulus and visual response. LGN responses resemble the sinusoidal modulation of a visual stimulus (black) in the alert state (red) but not in the non-alert state (blue). (c) In the alert state, thalamic neurons respond to higher temporal frequencies. (d) In the alert state, the spontaneous activity increases in the thalamus (LGN). However, in visual cortex, it remains constant in regular spiking neurons (RS) and decreases with alertness in suspected inhibitory neurons (SIN). (e) Response variability is reduced with alertness and the reduction is more pronounced in visual cortex than the thalamus.

Overall, the thalamocortical pathway provides an excellent example of how wiring specificity is used to build accurate representations of sensory space in the cerebral cortex and how changes in brain state modulate thalamocortical interactions to accommodate the demands of the sensory environment.


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