Available online at www.sciencedirect.com

ScienceDirect
Inside the corvid brain—probing the physiology ofcognition in crows
Andreas Nieder
A wealth of behavioral data show that songbirds of the corvid
family are endowed with exquisite cognitive capabilities. The
neurophysiological basis of this behavioral flexibility has beenaddressed recently by recording single-neuron activity from theassociative endbrain area nidopallium caudolaterale (NCL) inbehaving crows. NCL neurons encode sensory and cognitive
variables during working memory, but also participate in thetranslation of cognitive signals to motor behaviors. Thesefindings highlight the NCL as the corvid brain’s central
executive. Intelligence in birds can be realized with an endbrain
design that is radically different from the mammalian neocortex
and developed independently via convergent evolution.

Address
Animal Physiology Unit, Institute of Neurobiology, University ofTübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
Corresponding author: Nieder, Andreas (andreas.nieder@uni-tuebingen.
de)Current Opinion in Behavioral Sciences 2017, 16:8–14
This review comes from a themed issue on Comparative cognition
Edited by Ben Hayden and Jessica F Cantlon

http://dx.doi.org/10.1016/j.cobeha.2017.02.005
2352-1546/ã 2017 Elsevier Ltd. All rights reserved.

Introduction
The corvid family of songbirds (jays, jackdaws, crows andravens) contains some of the most intelligent bird species.
Corvids are renowned for their sophisticated behavioral
repertoire and cognitive flexibility, traits originallybelieved to be found only in primates [1,2]. For instance,
corvids manufacture and use tools [3,4], take into accountthe presence of conspecifics during social interactions[5,6], exhibit exquisite spatial memory [7], and show
signs of episodic-like long-term memory [8]. Moreover,they flexibly provide for future needs [9], master tests ofelaborate object permanence [10,11], and swiftly extract
general principles to guide behavior [12–14,15].
Such behavioral flexibility might seem unexpected
because a layered cerebral cortex, the highest integration
center in the mammalian brain, is not existant in birds.
Current Opinion in Behavioral Sciences 2017, 16:8–14

Instead of layers, the avian endbrain consists of nuclear
organized circuits that originate from a different part of
the telencephalic pallium which does not give rise to the
neocortex in mammals [16–19]. With this independently
evolved endbrain design, the corvid brain affords twice as
many neurons as a primate brain of equal mass [20],
adding up to enormous processing powers in a small anddensely packed ‘birdbrain’ [21].
In order to enable behavior beyond simple stimulusresponse association, brain areas that process information
between sensory input and motor output are required.
Association areas of the endbrain receive pre-processed
information about the outside world from sensory areas.
When combined with motivations represented in limbic
networks and past experiences stored in memory systems,
motor plans for meaningful responses are eventuallyformed and passed on to primary motor areas. The
anatomical connection patterns of the pallial region‘nidopallium caudolaterale’ (NCL) meet the requirements for high-level association areas. The avian NCL
is a multimodal forebrain area that constitutes an integration center which receives input from all sensory modalities and projects to premotor structures, is modulated bydopamine, and interacts with limbic, visceral and memory-related structures. The NCL is therefore considered
to be a functional equivalent of the prefrontal cortex(PFC) [22,23] that enables cognitive control in primates.
This constitutes a fascinating example of convergent
evolution of intelligence and cognitive control centersin the these vertebrates’ brains: However, the implications underlying structural differences between the
nuclear avian pallium and the layered mammalian neocortex remain unclear.
The cognitively-advanced corvids provide a ‘window ofopportunity’ to decipher the neurophysiological mechanisms of control functions based on different endbrain
structures. Here I review recently establishedelectrophysiological experiments in behaving carrion
crows (Corvus corone) aimed at elucidating the workingsof the NCL. The derived data provide novel insight into
the role of an important corvid association area in complex
cognition.

Recording single-neuron activity in behaving
crows
Measuring the electrical activity of neurons as physicalcarriers of cognition is key to understanding the workingsof the corvid brain. As Barlow [24] wrote, “the neuronwww.sciencedirect.com

Physiology of cognition in crows Nieder 9remains the important unit of function for developing a rational
account of how behavior is generated”. Recording neuronal
activity simultaneously with behavioral performance presents a rich and direct opportunity for experimental
analysis of the neuronal foundations of cognitive functions that would not be possible in untrained animals.
To that aim, crows were trained to perform tasks that
contain distinct task periods in which specific sensory,
cognitive and motor representations can be investigatedin a controlled way. Inspired by the success of nonhumanprimate studies [25–28], variations of delayed responsetasks have been used in combined behavioral–neurophysiological studies in crows. In such tasks, stimuli need to be
encoded, maintained and processed according to task
contingencies. Cognitive processes such as learning,memorization, categorization, association memory andrule switching can be disentangled and investigatedaccording to the specific task design. While the crows
performed under stimulus control, the electrical activityof individual nerve cells (i.e., their action potentials) wasmonitored by means of chronically-implanted and movable microelectrodes positioned within the NCL (Figure 1). Contributions of NCL neurons along the temporal
trajectory of a single trial are described in the following:from encoding, maintenance and processing in working
memory, until finally action selection.

Neuronal representations in the NCL
Sensory information

In order to fulfill its role as central executive, the NCL
needs access to incoming sensory information. The relevance of sensory input needs to be evaluated and, if
important, selected and integrated with other informationcommon to the goal at hand. Many NCL neurons

Figure 1(a) Crow brain: dorsal view(c) Single-cell recording (60 ms)
40 μV

10 ms
(d) Single-cell recording (8 s)

1s
1 cm(e) Action potential waveforms(b) Crow brain: coronal section(f) Spike sorting

20μV

Cb

A

NCL

PC2

Hp
NC

Tn

LSt

TeO

10 mm

PC1
200μs
Current Opinion in Behavioral Sciences

Electrophysiological recordings in the endbrain of carrion crows. (a) Dorso view of a crow brain showing the large telencephalic hemispheres (leftis anterior). Vertical dashed line indicates section level shown in B. (b) Coronal section through a crow’s brain indicating the borders of thenidopallium caudolaterale (NCL) in the caudal telencephalon. A, arcopallium; Cb, cerebellum; Hp, hippocampal formation; LSt, striatum laterale;NC, nidopallium caudale; NCL, nidopallium caudolaterale; Tn, nucleus taeniae amygdalae; TeO, tectum opticum. (c) Example of a 60 ms recording
trace with three action potentials from the NCL of a behaving carrion crow. (d) An 8 s recording trace from the NCL of a behaving carrion crow.
(e) Action potential waveforms (red and blue, respectively) of two isolated NCL neurons. (f) The same waveforms as in (e) shown in the twodimensional principal component space (PC1 versus PC2) used to sort single units. Two single-unit waveforms cluster together (red and blue) andare separated from each other and the noise distribution (gray). Adapted from Refs. [13,30].
www.sciencedirect.com

Current Opinion in Behavioral Sciences 2017, 16:8–14

10 Comparative cognition

Abstract categories

Rather than representing each detail of a sensory stimulus, the classification of stimuli into behaviorally meaningful categories endows animals with superior behavioral
flexibility. Classifying items according to their number isa particular abstract category. It allows animals to abstractfrom the sensory attributes of elements to arrive at thequantitative category ‘numerosity’. Ever since Koehler
and his co-workers explored the numerical capabilities ofbirds [31], corvids have been known to excel relative toother birds. Recently, relative numerosity discriminations(i.e., always choosing the larger of two sets) have beenshown to exist in trained jungle crows [32], but also in
untrained crows and jackdaws that spontaneously
selected the larger of two small food quantities [33,34].
In tasks requiring absolute numerosity discriminations,carrion crows have been shown to discriminate the specific number of items in small [35] and large visual item
sets [36]. They managed numerosity discriminations irrespective of the physical appearance of the array. For
instance, when four dots are displayed in a square-likearrangement or as a straight line, both are judged as
instances of numerosity four.
Subsequent recordings in discriminating crows foundmany neurons in the NCL that were tuned to individual
numerosities ranging from 1 to 30 items. They show thelargest discharge rates for their ‘preferred numerosity’[35,37]. The characteristics of both the neuronal and the
behavioral tuning functions showed close correspondence. Moreover, numerosity selectivity was impaired
during error trials. Both findings argue that the neuronal
discharge of NCL neurons is the neuronal basis for thecrows’ performance.
Retrospective working memory

To solve a delayed response task, features of sensory
information need to be maintained ‘on line’ in working
memory. Persistent (or sustained) neuronal activity
throughout the memory phase of a delay period is widelybelieved to reflect active maintenance and goal-directedtransformation of stimulus information in working
Current Opinion in Behavioral Sciences 2017, 16:8–14

memory [25–28]. Since earlier attempts to attribute sustained delay activity in pigeons to working memory
remained inconclusive because of the behavioral task
[38,39], crows were trained on a visual delayed-matchto-sample (DMS) task using four different sample stimuli
per session (Figure 2). Recordings showed that many
NCL neurons temporarily maintain visual information
after the stimulus disappeared [30]. Such neurons exhibited persistent and sample-selective activity during a briefdelay period. Selective delay activity for the four sample
images allowed the birds to hold relevant information inmemory and correlated with discrimination behavior.
This suggests that sustained activity of NCL neurons
Figure 2(a) Delayed match-to-sample task

le
amp

-s

Pre

le
amp

S

ory
Mem lay
De

t
Tes

Time

(b) Selectivity to pictures in working memory

SampleFiring rate [spikes/s]

represent basic visual features, such as global visualmotion information [29]. An even larger fraction of
NCL neurons (ca. 3/4 of the population) encode stimuli
rich in sensory parameters (e.g., complex images) [30].
NCL neurons are not only activated by visual stimuli, but
often respond selectively to specific parameter modifications with increased discharge rates. Neurons are tuned toparticular motion direction [29], or specific images [30].
Such selective sensory responses constitute the first processing step for discriminating sample items. As expected
for telencephalic association areas that receive highly
processed information from secondary sensory areas,
average visual response latencies range between
110 ms and 150 ms [29,30].

Delay

8
6
4
2
0

0

500

1000

1500

Time [ms]Current Opinion in Behavioral Sciences

Working memory coding in NCL during a delayed response tasks. (a)In the classical delayed match-to-sample (DMS) task, a sample
stimulus was shown that the crow had to memorize over a brief delay
period. In the final test period, the crow chose the image identical to
the sample item from an array of four items by pecking at the correct
choice image to receive a reward. (b) In crows performing the DMS
task shown in (a), neurons selectively encoded the working memory of
specific complex pictures. The spike density histogram shows the timecourse of the average and smoothed action potential rates of a single
neuron to the four sample items This example neuron maintained asignificantly higher persistent activity for the cherry picture (green)
during the memory delay. Vertical lines indicate onset and offset of thesample stimulus. Adapted from Ref. [30].
www.sciencedirect.com

Physiology of cognition in crows Nieder 11

Figure 3(a) Cross-modal delayed association task

is a neuronal correlate of visual working memory in thecorvid brain and serves to bridge temporal gaps as aworkspace for processing immediately past visual
information.

Noise-sound to blue-color:

Prospective associative signals

50%

Simple memorization of fleeting sensory information isoften not sufficient for adaptive behavior in ecologicalsituations. Many of the complex behaviors exhibited bycorvids depend on learned associations between arbitrary
stimuli. Cross-modal associations in which the elementsof a pair of associates belong to different sensory modalities are particularly challenging because they require
integration of separate sensory channels [40,41].

50%

Tit-song to red-color:
50%

50%

Presample

SampleAssociationDelay

Test 1

Test 2Time(b) Prospective associative responses
Noise sound -> blue color association (correct)
Noise sound -> blue color association (error)
Tit song -> red color association (correct)
Tit song -> red color association (error)

DelayFiring rate [spikes/s]

Sample
25
20
15
10
5
0
0

1000

2000

3000

Time [ms]Current Opinion in Behavioral Sciences

Cross-modal association activity. (a) In a delayed association task,
crows were trained to associate two acoustic sounds with color
images. If a noise sound was played as sample, the crow had tochose the blue color test image. If a tit song was played in the sample
period, the red test image was the correct choice. In 50% of the trials,
the first test color was a match, whereas the first test stimulus was a
non-match in the other 50% of the trials. (b) When crows performed
the dalayed association task shown in (a), NCL neurons prospectively
represent the associated color. This example neuron selectively
encoded the crow’s correct pairing of the noise sound with the color
blue (solid blue line), but remained inactive for trials in which the crowwww.sciencedirect.com

To address the neuronal correlate of this capacity, crows
were trained to associate each of two auditory signals
with a visual item displayed after a brief delay time
[42,43] (Figure 3a). During recordings, more than half ofthe population of recorded neurons showed associationselective activity. NCL neurons responded with sustained activity across the temporal gap for one of thepaired associates, but not for the other. For example, theneuron displayed in Figure 3b showed elevated discharges during the delay whenever the crow associateda noise sound with the blue target, but remained silent
when ever the crow responded to the tit song-red target
association. That way, neurons signaled the memorization of the learned audio-visual associations. Associative
activity was predictive of the crow’s behavior: if a neuron
did not represent its preferred association in trials inwhich this association was in effect, the crow madebehavioral errors and associated the wrong stimuli. In
this cross-temporal, cross-modal association task, sustained delay activity did not reflect a retrospective
memory signal of the auditory sample but rather aprospective associative signal related to the upcoming
visual associate [42].
To study the neuronal processes giving rise to associationneurons, single neurons in NCL were recorded before
and during association learning of crows [44]. The task
was designed such that the progressive neuronal linking
of novel sample stimuli to their associates could be
followed and compared to the representation of alreadyfamiliar associations that had to be retrieved from longterm memory. NCL neurons exhibited a common, prospective code for both familiar and newly learned paired
associates [44]. Such associative neuronal representations
were rapidly established for novel stimuli within the firstwrightly associated the tit song with the color red. Interestingly, such
association neurons predicted the crow’s prospective behavior;
Whenever the crow erroneously chose the color blue after a tit song
(red dotted lines), the neuron responed as if the noise sound had
indicated an association with the color blue. Layout as in Figure 2b.
Adapted from Ref. [42].
Current Opinion in Behavioral Sciences 2017, 16:8–14

12 Comparative cognition

few trials of associative learning and were in accordancewith the crows’ behavioral accuracy.
These data emphasize that NCL rapidly establishes atask-relevant representation of sensory cues that belong
together in working memory to guide the crows’ flexible
behavior. The corvid NCL has a role in synthesizing both
external multi-sensory information and internal mnemonic data needed for goal-directed behavior.

Conceptual rule activity

More important than associations between arbitrary stimuli are conditioned associations between sensory cuesand the behavioral instruction, or rules, they signal. For
instance, ‘stop at red’ is a very familiar and concrete rulethat is based on simple stimulus-response associations.
Intelligence, however, requires an understanding of
abstract rules based on overarching concepts of how to
act upon stimuli.
Abstract conceptual skills were demonstrated in crows
performing relational matching-to-sample (RMTS) tasks[15]. In RMTS tasks, the relationship between objects
constituting a sample (‘same’ versus ‘different’) is thefeature to be discriminated. For instance, when presented
with a sample showing two circles, crows chose a test card
displaying two triangles (relational matching, or sameness), but not the alternative card showing a triangle and asquare, and vice versa.
To investigate whether crows would be able to understand and switch between abstract rules in an instant,crows were trained to alternate rule application in adelayed response task. In one trial condition, a rule cue
indicated that a test image needed to be matched tothe same preceding sample image (‘same’ rule). In theother trial condition, another rule cue informed thecrow that a test image needed to be chosen that wasdifferent from the sample (‘different’ rule). Crows
indeed apply these rules successfully with novel
images, indicating that they had formed an abstract
concept ‘same versus different’ that they could follow
on demand based on rule cues [13]. During
electrophysiological recordings, the most prevalent
activity in NCL represents the behavioral rules, while
abstracting over sample images and sensory modalitiesof the rule cues. A population of neurons discharged
whenever the crows prepared for the ‘same’ rule,
whereas a different ensemble of cells showed elevatedfiring rates whenever the ‘different’ rule was in effect.
Rule coding was weaker in error trials, thus predicting
the crows’ behavioral decisions [13]. This suggeststhat the abstraction of general principles may be an
important function of the NCL, mirroring the functionof primate prefrontal cortex.
Current Opinion in Behavioral Sciences 2017, 16:8–14

Directed motor signals in visual search

The processing of cognitive information finally needs toresult in the appropriate behavioral output. NCL also
plays a role in motor behavior during the execution of
movement following cognitive processing. In a DMS task,
crows were trained to search and select the previously
remembered picture in a choice array of four complex
visual pictures on a touch screen. The crows exhibited
patterns of performance and reaction times consistent
with serial visual search of the choice screen [45]. During
this period of visual search and visually guided directed
motor behavior, single NCL neurons were strongly tuned
to different target positions. NCL therefore also participates in the translation of cognitive processes to specificbehaviors in the response period of a task.
Interestingly, the neuronal tuning of NCL neurons todifferent parameters was not rigidly determined, but
could rapidly change according to task demands. Thistuning flexibility of NCL neurons is reminiscent of the
responses of PFC neurons that can encode a variety ofcognitive factors during delayed response tasks [26]. PFC
neurons are known to dynamically adapt their tuning
properties to different task demands, both at the levelof single neurons [46–48] and as a population [49,50].
Perhaps cognitive flexibility in corvids and primates
depends on the capacity of the NCL and PFC, respectively, to dynamically encode task relevant information.
This property could be a key computational featurefacilitating the emergence of complex cognitive behavior
in vertebrates.

Conclusion
Over the last few years, crows have been established as afascinating model organism in systems neuroscience. The
presented findings in corvids emphasize that intelligencein vertebrates can be realized with an endbrain designthat is radically different from the mammalian neocortex
and independently developed via convergent evolution.
Understanding the neuronal code for cognitive control in
the corvid brain and comparisons with the primate brain
will help clarify neurophysiological solutions that evolution may have found for different vertebrate endbrains.
Such data will help to decipher the general principles andevolutionary constraints for the design of clever vertebrate brains [51].

Conflict of interest
The author declares no conflict of interest.

Acknowledgement
Supported by DFG grants NI 618/3-1, NI 618/6-1 and NI 618/7-1.

References and recommended reading
Papers of particular interest, published within the period of review,
have been highlighted as:
 of special interest
www.sciencedirect.com

Physiology of cognition in crows Nieder 13

 of outstanding interest
1.

Emery NJ, Clayton NS: The mentality of crows: convergent
evolution of intelligence in corvids and apes. Science 2004,
306:1903-1907.

2.

Clayton NS, Emery NJ: Avian models for human cognitive
neuroscience: a proposal. Neuron 2015, 86:1330-1342.

3.

Weir AA, Chappell J, Kacelnik A: Shaping of hooks in New
Caledonian crows. Science 2002, 297:981.

4.

Rutz C, Klump BC, Komarczyk L, Leighton R, Kramer J,
Wischnewski S, Sugasawa S, Morrissey MB, James R, St Clair JJ
et al.: Discovery of species-wide tool use in the Hawaiian crow.
Nature 2016, 537:403-407.

5.

Emery NJ, Clayton NS: Effects of experience and social context
on prospective caching strategies by scrub jays. Nature 2001,
414:443-446.

6. Bugnyar T, Reber SA, Buckner C: Ravens attribute visual access
to unseen competitors. Nat Commun 2016, 7:10506.
Important new contribution on how animals may predict conspecifics’
behavior. It suggest that ravens can generalize from their own perceptual
experience to infer, or simulate, the possibility of being seen.
7.

Balda RP, Kamil AC: Long-term spatial memory in Clark’s
nutcracker, Nucifaga columbiana. Anim Behav 1992, 44:761769.

8.

Clayton NS, Dickinson A: Episodic-like memory during cache
recovery by scrub jays. Nature 1998, 395:272-274.

9.

Raby CR, Alexis DM, Dickinson A, Clayton NS: Planning for the
future by western scrub-jays. Nature 2007, 445:919-921.

10. Zucca P, Milos N, Vallortigara G: Piagetian object permanence
and its development in Eurasian jays (Garrulus glandarius).
Anim Cogn 2007, 10:243-258.
11. Hoffmann A, Rüttler V, Nieder A: Ontogeny of object
permanence and object tracking in the carrion crow, Corvus
corone. Anim Behav 2011, 82:359-367.
12. Wilson B, Mackintosh NJ, Boakes RA: Transfer of relational rules
in matching and oddity learning by pigeons and corvids. Q J
Exp Psychol 1985, 37:313-332.
13. Veit L, Nieder A: Abstract rule neurons in the endbrain support
 intelligent behaviour in corvid songbirds. Nat Commun 2013,
4:2878.
This paper reports the first single-neuron recordings in behaving crows.
Crows perform a rule-switching task, a hallmark of executive functioning.
The most prevalent neuronal activity represented the abstract rules andpredicted the crows’ rule-guided decisions.
14. Moll FW, Nieder A: The long and the short of it: rule-based
relative length discrimination in carrion crows, Corvus corone.
Behav Process 2014, 107:142-149.
15. Smirnova A, Zorina Z, Obozova T, Wasserman E: Crows
spontaneously exhibit analogical reasoning. Curr Biol 2015,
25:256-260.
This behavioral study shows that crows grasp a broadly applicableconcept of sameness and differentness. Crows spontaneously appreciate the relation between one pair of items (e.g., same shape) and thenapplied it to a new pair of items to generate the correct answer: Thisbehavior is evidence of analogical reasoning in corvids.
16. Jarvis ED, Güntürkün O, Bruce L, Csillag A, Karten H, Kuenzel W,
Medina L, Paxinos G, Perkel DJ, Shimizu T et al.: Avian brains anda new understanding of vertebrate brain evolution. Nat Rev
Neurosci 2005, 6:151-159.
17. Reiner A, Perkel DJ, Bruce LL, Butler AB, Csillag A, Kuenzel W,
Medina L, Paxinos G, Shimizu T, Striedter G et al.: Revised
nomenclature for avian telencephalon and some related
brainstem nuclei. J Comp Neurol 2004, 473:377-414.
18. Butler AB, Reiner A, Karten HJ: Evolution of the amniote pallium
and the origins of mammalian neocortex. Ann N Y Acad Sci
2011, 1225:14-27.
19. Güntürkün O, Bugnyar T: Cognition without cortex. Trends Cogn
Sci 2016, 20:291-303.
www.sciencedirect.com

can RK, Porte M, Fitch WT, Herculano20. Olkowicz S, Kocourek M, Lu9
mec P: Birds have primate-like numbers of Houzel S, Ne
neurons in the forebrain. Proc Natl Acad Sci U S A 2016,
113:7255-7260.
Counting the number of neurons in the brains of 28 bird species, theauthors discovered that the brains of songbirds such as corvids contain
twice as many neurons as primate brains of the same mass. These new
findings could help to explain why some birds, and corvids in particular,
excel in their cognitive skills.
21. Cnotka J, Güntürkün O, Rehkämper G, Gray RD, Hunt GR:
Extraordinary large brains in tool-using New Caledonian
crows (Corvus moneduloides). Neurosci Lett 2008, 433:241-245.
22. Divac I, Mogensen J, Björklund A: The prefrontal “cortex” in the
pigeon. Biochemical evidence. Brain Res 1985, 332:365-368.
23. Güntürkün O: The avian “prefrontal cortex” and cognition. Curr
Opin Neurobiol 2005, 15:686-693.
24. Barlow H: The neuron doctrine in perception. In The Cognitive
Neurosciences. Edited by Gazzaniga Michael ScalS. Cambridge,
MA, USA: MIT Press; 1995:415-435.
25. Goldman-Rakic PS: Cellular basis of working memory. Neuron
1995, 14:477-485.
26. Miller EK: The prefrontal cortex and cognitive control. Nat Rev
Neurosci 2000, 1:59-65.
27. Fuster JM: The prefrontal cortex—an update: time is of theessence. Neuron 2001, 30:319-333.
28. Nieder A: The neuronal code for number. Nat Rev Neurosci 2016,
17:366-382.
29. Wagener L, Nieder A: Encoding of global visual motion in thenidopallium caudolaterale of behaving crows. Eur J Neurosci
2017, 45:267-277.
30. Veit L, Hartmann K, Nieder A: Neuronal correlates of visual
working memory in the corvid endbrain. J Neurosci 2014,
34:7778-7786.
31. Koehler O: The ability of birds to ‘count’. Bull Anim Behav 1951,
9:41-45.
32. Bogale BA, Kamata N, Mioko K, Sugita S: Quantity
discrimination in jungle crows, Corvus macrorhynchos. Anim
Behav 2011, 82:635-641.
33. Ujfalussy D, Miklósi A, Bugnyar T, Kotrschal K: Role of mental
representations in quantity judgments by jackdaws (Corvus
monedula). J Comp Psychol 2013, 128:11-20.
34. Bogale BA, Aoyama M, Sugita S: Spontaneous discrimination offood quantities in the jungle crow, Corvus macrorhynchos.
Anim Behav 2014, 94:73-78.
35. Ditz HM, Nieder A: Neurons selective to the number of visual
items in the corvid songbird endbrain. Proc Natl Acad Sci U S A2015, 112:7827-7832.
The authors monitored single-neuron activity while crows performed anumber-matching task. Neurons in the NCL were tuned to the numbers ofvisual items, regardless of the item’s size or spatial arrangement.
36. Ditz HM, Nieder A: Numerosity representations in crows obey
the Weber–Fechner law. Proc Biol Sci 2016, 283:20160083.
37. Ditz HM, Nieder A: Sensory and working memory
representations of small and large numerosities in the crowendbrain. J Neurosci 2016, 36:12044-12052.
38. Diekamp B, Kalt T, Güntürkün O: Working memory neurons in
pigeons. J Neurosci 2002, 22:RC210.
39. Rose J, Colombo M: Neural correlates of executive control in
the avian brain. PLoS Biol 2005, 3:e190.
40. Kondo N, Izawa EI, Watanabe S: Crows cross-modally
recognize group members but not non-group members. Proc
R Soc B 2012, 279:1937-1942.
41. Wascher CAF, Szipl G, Boeckle M, Wilkinson A: You soundfamiliar: carrion crows can differentiate between the calls ofknown and unknown heterospecifics. Anim Cogn 2012,
15:1015-1019.
Current Opinion in Behavioral Sciences 2017, 16:8–14

14 Comparative cognition

42. Moll FW, Nieder A: Cross-modal associative mnemonic signals
in crow endbrain neurons. Curr Biol 2015, 25:2196-2201.
43. Moll FW, Nieder A: Modality-invariant audio-visual association
coding in crow endbrain neurons. Neurobiol Learn Mem 2017,
137:65-76.
44. Veit L, Pidpruzhnykova G, Nieder A: Associative learning rapidly
establishes neuronal representations of upcoming behavioral
choices in crows. Proc Natl Acad Sci 2015, 112:15208-15213.
45. Veit L, Hartmann K, Nieder A: Spatially tuned neurons in corvid
nidopallium caudolaterale signal target position during visual
search. Cereb Cortex 2015. pii: bhv299 [Epub ahead of print].
46. Rao SC, Rainer G, Miller EK: Integration of what and where in the
primate prefrontal cortex. Science 1997, 276:821-824.

Current Opinion in Behavioral Sciences 2017, 16:8–14

47. Kennerley SW, Wallis JD: Reward-dependent modulation ofworking memory in lateral prefrontal cortex. J Neurosci 2009,
29:3259-3270.
48. Merten K, Nieder A: Active encoding of decisions about
stimulus absence in primate prefrontal cortex neurons. Proc
Natl Acad Sci U S A 2012, 109:6289-6294.
49. Rainer G, Asaad WF, Miller EK: Selective representation of
relevant information by neurons in the primate prefrontal
cortex. Nature 1998, 393:577-579.
50. Stokes MG, Kusunoki M, Sigala N, Nili H, Gaffan D, Duncan J:
Dynamic coding for cognitive control in prefrontal cortex.
Neuron 2013, 78:364-375.
51. Bullock TH: Comparative neuroscience holds promise for quiet
revolutions. Science 1984, 225:473-478.

www.sciencedirect.com