Liora Las

Processing of low-probability sounds by cortical neurons

The ability to detect rare auditory events can be critical for survival. We report here that neurons in cat primary auditory cortex (A1) responded more strongly to a rarely presented sound than to the same sound when it was common. For the rare stimuli, we used both frequency and amplitude deviants. Moreover, some A1 neurons showed hyperacuity for frequency deviants—a frequency resolution one order of magnitude better than receptive field widths in A1. In contrast, auditory thalamic neurons were insensitive to the probability of frequency deviants. These phenomena resulted from stimulus-specific adaptation in A1, which may be a single-neuron correlate of an extensively studied cortical potential—mismatch negativity—that is evoked by rare sounds. Our results thus indicate that A1 neurons, in addition to processing the acoustic features of sounds, may also be involved in sensory memory and novelty detection.

Multiple Time Scales of Adaptation in Auditory Cortex Neurons

Neurons in primary auditory cortex (A1) of cats show strong stimulus-specific adaptation (SSA). In probabilistic settings, in which one stimulus is common and another is rare, responses to common sounds adapt more strongly than responses to rare sounds. This SSA could be a correlate of auditory sensory memory at the level of single A1 neurons. Here we studied adaptation in A1 neurons, using three different probabilistic designs. We showed that SSA has several time scales concurrently, spanning many orders of magnitude, from hundreds of milliseconds to tens of seconds. Similar time scales are known for the auditory memory span of humans, as measured both psychophysically and using evoked potentials. A simple model, with linear dependence on both short-term and long-term stimulus history, provided a good fit to A1 responses. Auditory thalamus neurons did not show SSA, and their responses were poorly fitted by the same model. In addition, SSA increased the proportion of failures in the responses of A1 neurons to the adapting stimulus. Finally, SSA caused a bias in the neuronal responses to unbiased stimuli, enhancing the responses to eccentric stimuli. Therefore, we propose that a major function of SSA in A1 neurons is to encode auditory sensory memory on multiple time scales. This SSA might play a role in stream segregation and in binding of auditory objects over many time scales, a property that is crucial for processing of natural auditory scenes in cats and of speech and music in humans.

Primary auditory cortex of cats: feature detection or something else?

Abstract.Neurons in sensory cortices are often assumed to be “feature detectors”, computing simple and then successively more complex features out of the incoming sensory stream. These features are somehow integrated into percepts. Despite many years of research, a convincing candidate for such a feature in primary auditory cortex has not been found. We argue that feature detection is actually a secondary issue in understanding the role of primary auditory cortex. Instead, the major contribution of primary auditory cortex to auditory perception is in processing previously derived features on a number of different timescales. We hypothesize that, as a result, neurons in primary auditory cortex represent sounds in terms of auditory objects rather than in terms of feature maps. According to this hypothesis, primary auditory cortex has a pivotal role in the auditory system in that it generates the representation of auditory objects to which higher auditory centers assign properties such as spatial location, source identity, and meaning.

Representation of tone in fluctuating maskers in the ascending auditory system

Humans and animals detect low-level tones masked by slowly fluctuating noise very efficiently. A possible neuronal correlate of this phenomenon is the ability of low-level tones to suppress neuronal locking to the envelope of the fluctuating noise (“locking suppression”). Using in vivo intracellular and extracellular recordings in cats, we studied neuronal responses to combinations of fluctuating noise and tones in three successive auditory stations: inferior colliculus (IC), medial geniculate body (MGB), and primary auditory cortex (A1). We found that although the most sensitive responses in the IC were approximately isomorphic to the physical structure of the sounds, with only a small perturbation in the responses to the fluctuating noise after the addition of low-level tones, some neurons in the MGB and all A1 neurons displayed striking suppressive effects. These neurons were hypersensitive, showing suppression already with tone levels lower than the threshold of the neurons in silence. The hypersensitive locking suppression in A1 and MGB had a special timing structure, starting >75 ms after tone onset. Our findings show a qualitative change in the representation of tone in fluctuating noise along the IC-MGB-A1 axis, suggesting the gradual segregation of signal from noise and the representation of the signal as a separate perceptual object in A1.

Three-dimensional head-direction coding in the bat brain

Navigation requires a sense of direction (‘compass’), which in mammals is thought to be provided by head-direction cells, neurons that discharge when the animal’s head points to a specific azimuth. However, it remains unclear whether a three-dimensional (3D) compass exists in the brain. Here we conducted neural recordings in bats, mammals well-adapted to 3D spatial behaviours, and found head-direction cells tuned to azimuth, pitch or roll, or to conjunctive combinations of 3D angles, in both crawling and flying bats. Head-direction cells were organized along a functional–anatomical gradient in the presubiculum, transitioning from 2D to 3D representations. In inverted bats, the azimuth-tuning of neurons shifted by 180°, suggesting that 3D head direction is represented in azimuth × pitch toroidal coordinates. Consistent with our toroidal model, pitch-cell tuning was unimodal, circular, and continuous within the available 360° of pitch. Taken together, these results demonstrate a 3D head-direction mechanism in mammals, which could support navigation in 3D space.

Vectorial representation of spatial goals in the hippocampus of bats

How to get to place B We constantly navigate around our environment. This means moving from our current location, place A, to a new goal, place B. We have recently learned much about spatial maps in the brain in which place cells indicate current location. However, it is unclear how navigational goals are represented in the brain. Sarel et al. describe a group of neurons in the brains of bats that are tuned to goal direction and distance relative to the bats current position as it flies toward its goal. The finding elucidates the computations involved in spatial navigation. Science, this issue p. 176 A subpopulation of neurons in the bat hippocampus indicate distance and direction to the location of a goal. To navigate, animals need to represent not only their own position and orientation, but also the location of their goal. Neural representations of an animal’s own position and orientation have been extensively studied. However, it is unknown how navigational goals are encoded in the brain. We recorded from hippocampal CA1 neurons of bats flying in complex trajectories toward a spatial goal. We discovered a subpopulation of neurons with angular tuning to the goal direction. Many of these neurons were tuned to an occluded goal, suggesting that goal-direction representation is memory-based. We also found cells that encoded the distance to the goal, often in conjunction with goal direction. The goal-direction and goal-distance signals make up a vectorial representation of spatial goals, suggesting a previously unrecognized neuronal mechanism for goal-directed navigation.

Functional Gradients of Auditory Sensitivity along the Anterior Ectosylvian Sulcus of the Cat

Determining the spatial direction of sound sources is one of the major computations performed by the auditory system. The anterior ectosylvian sulcus (AES) of cat cortex is known to be important for sound localization. However, there are contradicting reports as to the spatial response properties of neurons in AES: whereas some studies found narrowly tuned neurons, others reported mostly spatially widely tuned neurons. We hypothesized that this is the result of a nonhomogenous distribution of the auditory neurons in this area. To test this possibility, we recorded neuronal activity along the AES, together with a sample of neurons from primary auditory cortex (A1) of cats in response to pure tones and to virtual acoustic space stimuli. In all areas, most neurons responded to both types of stimuli. Neurons located in posterior AES (pAES) showed special response properties that distinguished them from neurons in A1 and from neurons in anterior AES (aAES). The proportion of space-selective neurons among auditory neurons was significantly higher in pAES (82%) than in A1 (72%) and in aAES (60%). Furthermore, whereas the large majority of A1 neurons responded preferentially to contralateral sounds, neurons in pAES (and to a lesser extent in aAES) had their spatial selectivity distributed more homogenously. In particular, 28% of the space-selective neurons in pAES had highly modulated frontal receptive fields, against 8% in A1 and 17% in aAES. We conclude that in cats, pAES contains a secondary auditory cortical field which is specialized for spatial processing, in particular for the representation of frontal space.

3-D Maps and Compasses in the Brain

The world has a complex, three-dimensional (3-D) spatial structure, but until recently the neural representation of space was studied primarily in planar horizontal environments. Here we review the emerging literature on allocentric spatial representations in 3-D and discuss the relations between 3-D spatial perception and the underlying neural codes. We suggest that the statistics of movements through space determine the topology and the dimensionality of the neural representation, across species and different behavioral modes. We argue that hippocampal place-cell maps are metric in all three dimensions, and might be composed of 2-D and 3-D fragments that are stitched together into a global 3-D metric representation via the 3-D head-direction cells. Finally, we propose that the hippocampal formation might implement a neural analogue of a Kalman filter, a standard engineering algorithm used for 3-D navigation.

Social place-cells in the bat hippocampus

The representation of others in space Different sets of neurons encode the spatial position and orientation of an organism. However, social animals need to know the position of other individuals for social interactions, observational learning, and group navigation. Surprisingly, very little is known about how the position of other animals is represented in the brain. Danjo et al. and Omer et al. now report the discovery of a subgroup of neurons in hippocampal area CA1 that encodes the presence of conspecifics in rat and bat brains, respectively. Science, this issue p. 213, p. 218 A subpopulation of bat hippocampal CA1 neurons represents the spatial position of another bat. Social animals have to know the spatial positions of conspecifics. However, it is unknown how the position of others is represented in the brain. We designed a spatial observational-learning task, in which an observer bat mimicked a demonstrator bat while we recorded hippocampal dorsal-CA1 neurons from the observer bat. A neuronal subpopulation represented the position of the other bat, in allocentric coordinates. About half of these “social place-cells” represented also the observer’s own position—that is, were place cells. The representation of the demonstrator bat did not reflect self-movement or trajectory planning by the observer. Some neurons represented also the position of inanimate moving objects; however, their representation differed from the representation of the demonstrator bat. This suggests a role for hippocampal CA1 neurons in social-spatial cognition.

Conserved size and periodicity of pyramidal patches in layer 2 of medial/caudal entorhinal cortex.

To understand the structural basis of grid cell activity, we compare medial entorhinal cortex architecture in layer 2 across five mammalian species (Etruscan shrews, mice, rats, Egyptian fruit bats, and humans), bridging ∼100 million years of evolutionary diversity. Principal neurons in layer 2 are divided into two distinct cell types, pyramidal and stellate, based on morphology, immunoreactivity, and functional properties. We confirm the existence of patches of calbindin‐positive pyramidal cells across these species, arranged periodically according to analyses techniques like spatial autocorrelation, grid scores, and modifiable areal unit analysis. In rodents, which show sustained theta oscillations in entorhinal cortex, cholinergic innervation targeted calbindin patches. In bats and humans, which only show intermittent entorhinal theta activity, cholinergic innervation avoided calbindin patches. The organization of calbindin‐negative and calbindin‐positive cells showed marked differences in entorhinal subregions of the human brain. Layer 2 of the rodent medial and the human caudal entorhinal cortex were structurally similar in that in both species patches of calbindin‐positive pyramidal cells were superimposed on scattered stellate cells. The number of calbindin‐positive neurons in a patch increased from ∼80 in Etruscan shrews to ∼800 in humans, only an ∼10‐fold over a 20,000‐fold difference in brain size. The relatively constant size of calbindin patches differs from cortical modules such as barrels, which scale with brain size. Thus, selective pressure appears to conserve the distribution of stellate and pyramidal cells, periodic arrangement of calbindin patches, and relatively constant neuron number in calbindin patches in medial/caudal entorhinal cortex. J. Comp. Neurol. 524:783–806, 2016.