Abstract:We recorded head direction (HD) cells from the lateral mammillary nucleus (LMN) and anterior thalamus (ATN) of freely behaving rats and also made bilateral lesions of LMN while recording HD cells from ATN. We discovered that the tuning functions of LMN HD cells become narrower during contraversive head turns, but not ipsiversive head turns, compared to when the head is not turning. This narrowing effect does not occur for ATN HD cells. We also found that the HD signal in LMN leads that in ATN by about 15-20 ms… Show more
“…From the neurophysiological point of view, the tuning curves we observe are quantitatively similar to the tuning curves recorded in the rat HD cell system. HD cells are numerous in the LMN (Blair et al, 1998;Stackman and Taube, 1998). In the DTN, the picture is less clear, because few electrophysiological recordings are available in this area Bassett and Taube, 2001b).…”
Section: Discussionmentioning
confidence: 99%
“…Lesions to DTN disrupt the directional selectivity in ADN (Bassett and Taube, 2001a). Bilateral lesions of LMN abolish the HD signal in ADN (Blair et al, 1998). The directional selectivity of PoSC cells is seriously impaired when lesioning ADN (Goodridge and Taube, 1997), but lesions of the PoSC leave the signal in ADN largely intact (Goodridge and Taube, 1997).…”
Section: Introductionmentioning
confidence: 97%
“…Although the actual mechanisms underlying the generation of the HD signal are still unclear, anatomical and lesion data suggest that LMN and DTN might constitute an essential sub-circuit of the HD cell system for generating and maintaining the directional signal (Blair et al, 1998;Bassett and Taube, 2001b;Taube and Bassett, 2003). This would yield the following ascending processing scheme: DTN → LMN → ADN → PoSC.…”
Section: Introductionmentioning
confidence: 99%
“…A further evidence for the above processing pathway is provided by a series of studies that have investigated the temporal properties of HD cells in LMN, ADN, and PoSC. During head turns, LMN neurons tend to anticipate the future head direction by approximately 40-75 ms (Stackman and Taube, 1998;Blair et al, 1998), ADN cells show a smaller anticipatory time delay of about 25 ms (Taube and Muller, 1998;Cho and Sharp, 2001), and PoSC cells tend to encode the current directional heading (Blair and Sharp, 1995). The DTN-LMN circuit seems also well suited to account for the additional property of the HD cell system of integrating the head angular velocity.…”
Section: Introductionmentioning
confidence: 99%
“…However, Skaggs et al (1995) did not simulate any model system to check the feasibility of this scenario. Blair et al (1998) and Degris et al (2004) realized a neural implementation of this scenario by proposing an attractor-integrator circuit modeling the LMN-DTN system. Finally, Xie et al (2002) studied a two population network of left and right units, and showed that with appropriate connections and linear synapses, the network can integrate the inputs with good accuracy in a large velocity range.…”
Abstract. Motivated by experimental observations of the head direction system, we study a three population network model that operates as a continuous attractor network. This network is able to store in a short-term memory an angular variable (the head direction) as a spatial profile of activity across neurons in the absence of selective external inputs, and to accurately update this variable on the basis of angular velocity inputs. The network is composed of one excitatory population and two inhibitory populations, with inter-connections between populations but no connections within the neurons of a same population. In particular, there are no excitatory-to-excitatory connections. Angular velocity signals are represented as inputs in one inhibitory population (clockwise turns) or the other (counterclockwise turns). The system is studied using a combination of analytical and numerical methods. Analysis of a simplified model composed of threshold-linear neurons gives the conditions on the connectivity for (i) the emergence of the spatially selective profile, (ii) reliable integration of angular velocity inputs, and (iii) the range of angular velocities that can be accurately integrated by the model. Numerical simulations allow us to study the proposed scenario in a large network of spiking neurons and compare their dynamics with that of head direction cells recorded in the rat limbic system. In particular, we show that the directional representation encoded by the attractor network can be rapidly updated by external cues, consistent with the very short update latencies observed experimentally by Zugaro et al. (2003) in thalamic head direction cells.
“…From the neurophysiological point of view, the tuning curves we observe are quantitatively similar to the tuning curves recorded in the rat HD cell system. HD cells are numerous in the LMN (Blair et al, 1998;Stackman and Taube, 1998). In the DTN, the picture is less clear, because few electrophysiological recordings are available in this area Bassett and Taube, 2001b).…”
Section: Discussionmentioning
confidence: 99%
“…Lesions to DTN disrupt the directional selectivity in ADN (Bassett and Taube, 2001a). Bilateral lesions of LMN abolish the HD signal in ADN (Blair et al, 1998). The directional selectivity of PoSC cells is seriously impaired when lesioning ADN (Goodridge and Taube, 1997), but lesions of the PoSC leave the signal in ADN largely intact (Goodridge and Taube, 1997).…”
Section: Introductionmentioning
confidence: 97%
“…Although the actual mechanisms underlying the generation of the HD signal are still unclear, anatomical and lesion data suggest that LMN and DTN might constitute an essential sub-circuit of the HD cell system for generating and maintaining the directional signal (Blair et al, 1998;Bassett and Taube, 2001b;Taube and Bassett, 2003). This would yield the following ascending processing scheme: DTN → LMN → ADN → PoSC.…”
Section: Introductionmentioning
confidence: 99%
“…A further evidence for the above processing pathway is provided by a series of studies that have investigated the temporal properties of HD cells in LMN, ADN, and PoSC. During head turns, LMN neurons tend to anticipate the future head direction by approximately 40-75 ms (Stackman and Taube, 1998;Blair et al, 1998), ADN cells show a smaller anticipatory time delay of about 25 ms (Taube and Muller, 1998;Cho and Sharp, 2001), and PoSC cells tend to encode the current directional heading (Blair and Sharp, 1995). The DTN-LMN circuit seems also well suited to account for the additional property of the HD cell system of integrating the head angular velocity.…”
Section: Introductionmentioning
confidence: 99%
“…However, Skaggs et al (1995) did not simulate any model system to check the feasibility of this scenario. Blair et al (1998) and Degris et al (2004) realized a neural implementation of this scenario by proposing an attractor-integrator circuit modeling the LMN-DTN system. Finally, Xie et al (2002) studied a two population network of left and right units, and showed that with appropriate connections and linear synapses, the network can integrate the inputs with good accuracy in a large velocity range.…”
Abstract. Motivated by experimental observations of the head direction system, we study a three population network model that operates as a continuous attractor network. This network is able to store in a short-term memory an angular variable (the head direction) as a spatial profile of activity across neurons in the absence of selective external inputs, and to accurately update this variable on the basis of angular velocity inputs. The network is composed of one excitatory population and two inhibitory populations, with inter-connections between populations but no connections within the neurons of a same population. In particular, there are no excitatory-to-excitatory connections. Angular velocity signals are represented as inputs in one inhibitory population (clockwise turns) or the other (counterclockwise turns). The system is studied using a combination of analytical and numerical methods. Analysis of a simplified model composed of threshold-linear neurons gives the conditions on the connectivity for (i) the emergence of the spatially selective profile, (ii) reliable integration of angular velocity inputs, and (iii) the range of angular velocities that can be accurately integrated by the model. Numerical simulations allow us to study the proposed scenario in a large network of spiking neurons and compare their dynamics with that of head direction cells recorded in the rat limbic system. In particular, we show that the directional representation encoded by the attractor network can be rapidly updated by external cues, consistent with the very short update latencies observed experimentally by Zugaro et al. (2003) in thalamic head direction cells.
The past thirty years have seen a revolution in our understanding of neural connectivity. The enormous amount of new neuroanatomical data has significantly altered our ideas about how different parts of the brain interact to control behavior. This chapter assesses what we currently understand about the neuroanatomical basis of motivated behaviors. These behaviors enable animals to organize their interactions with the goal objects that promote the survival of an individual or maintain sustainable numbers of individuals within a species. Four classes of behavior are recognized: ingestive and thermoregulatory behaviors, which are the behavioral adjuncts of physiological homeostasis, and defensive and reproductive behaviors, which organize those social interactions that are more concerned with individual security and the maintenance of the species.
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