Short Communication - Journal of Experimental Neurology (2021) Volume 2, Issue 2
Neural Population Computing: Parallel Distributed Processing, the Basal Ganglia, and Evolution
Stephen E. Nadeau*
Research Service and the Brain Rehabilitation Research Center, Malcom Randall VA Medical Center and the Department of Neurology, University of Florida College of Medicine, Florida, USA
- *Corresponding Author:
- Stephen E. Nadeaun
Received date: April 04, 2021; Accepted date: May 10, 2021
Citation: Nadeau SE. Neural Population Computing: Parallel Distributed Processing, the Basal Ganglia, and Evolution. J Exp
Copyright: © 2021 Nadeau SE. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Representations in the central nervous system are population encoded. Understanding the computational processes subserved by pools of cortical and connected subcortical neurons constitutes one of the major challenges facing systems neuroscience. The science of parallel distributed processing (PDP) combines neural plausibility, theoretical coherence, and a demonstrated ability to account for an enormous range of phenomena in normal and damaged human brains. PDP features have now been demonstrated in mice and Hydra. I here discuss a recently introduced granular PDP model of the basal ganglia (BG) that takes into account the fundamental anatomic and neurophysiologic features of the component structures and logically accounts for the effects of low dopamine levels as observed in Parkinson’s disease (PD). Research on lamprey and Drosophila suggests that the essential computational function of the sensorimotor BG (smBG) is reduction of a high dimensionality input space (sensory, motor, and internal drives) to a low dimensionality output space comprised of a limited portfolio of mutually compatible behaviors. Optimization is achieved in the process of iterative settling into a constellation of attractor states. An evolutionary perspective suggests that, for much of the history of complex nervous systems, dating back to arthropod precursors, the smBG, in conjunction with PDP, has provided the basis for the most fundamental of computational functions, reactive intention: the automatic translation of available afferent information into an optimal behavioral response. Experience with pallidotomy for treatment of PD suggests that, in humans, the smBG has become largely anachronistic, its function superseded by cortical mechanisms.
Parallel distributed processing, Population encoding, Attractor, Basal ganglia, Brain evolution, Lamprey, Drosophila
It has been known for some time that representations in the central nervous system (CNS) are population encoded, that is, encoded as patterns of activity involving very large numbers of highly interconnected neurons in one or more neural networks extending over large expanses of the brain [1-11]. Nonetheless, understanding the computational processes occurring in pools of cortical neurons and the subcortical nuclei with which they interact continues to be one of the major challenges facing systems neuroscience.
The science of parallel distributed processing (PDP)  provides a means for addressing this challenge. The underlying mathematics were developed by Hopfield and Tan . PDP has theoretical coherence and neurological verisimilitude and it has been able to account for a large number of cognitive phenomena in normal people, including reaction times (and reading latencies), stimulus recognition, the effect of stimulus salience on attention, perceptual invariance, simultaneous egocentric and allocentric visual processing, top-down/ bottom up processing, language errors, the effect of statistical regularities of experience, frequency, and age of acquisition, instantiation of rules and symbols, content addressable memory and the capacity for pattern completion, preservation of function in the face of noisy or distorted input, inference, parallel constraint satisfaction, the binding problem and gamma coherence, principles of hippocampal function, the location of knowledge in the brain, limitations in the scope and depth of knowledge acquired through experience, and Piagetian stages of cognitive development . PDP principles have been able
to provide a coherent account for impairment in a variety of
language functions resulting from stroke or dementia in a
large number of languages and the phenomenon of graceful degradation observed in such studies [15,16]. They have
also made important contributions to our understanding
of attention (including hemispatial neglect), emotional
function, executive function, motor planning, visual
processing, decision making, and neuroeconomics .
Until recently, PDP was a theoretical construct, validated almost exclusively in human studies. However, Yuste and his colleagues [17-19], using calcium imaging, have now demonstrated cardinal PDP principles in operation in the visual cortex of awake, behaving mice: acquisition of knowledge of statistical regularities of experience, population encoding, attractor states, and pattern completion (see also parallel work [20-23]).
While PDP has enormously advanced our understanding of cortical function , its single greatest accomplishment may be its contribution to our understanding of the computational processes subserved by the hippocampus and connected mesial temporal structures  (see brief review ). In a recent paper, I took a comparably granular approach to analyzing basal ganglia (BG) function . This approach substantially illuminated the computational function of the BG but review of the pallidotomy literature also suggested that the sensorimotor BG (smBG) (dorsal head of caudate, body and tail of caudate, and putamen) have become an anachronism in humans, its computational function supplanted by cortical processes. Research on the lamprey, a jawless fish that departed from the mammalian line 560 million years ago, and pioneering work by Fiore and his colleagues , subsuming review of deep homologies between major components of the central complex of the lamprey, Drosophila, and primate brain, provided two fundamental insights: 1) the fundamental computational function of the BG is dimensionality reduction; and 2) PDP processes date back at least to arthropods and may constitute the computational principle shared by all creatures with complex nervous systems. In this paper, I briefly review a BG model and these insights garnered from studies of far more primitive creatures.
The Sensorimotor Basal Ganglia
A somewhat simplistic model of the smBG is depicted in the figure . The model incorporates several key assumptions: 1) recurrent collaterals in the cortex provide the basis for settling within attractor basins into attractor states; 2) recurrent collaterals in the striatum, globus pallidus externa (GPe), and globus pallidus interna (GPi), all of which are comprised predominantly of GABAergic neurons, provide the basis for competitive inhibition and winner-take-all computational dynamics; 3) whereas none of the subcortical structures has the extensive interconnectivity (i.e., dense coding) that is fundamental to cerebral cortical function, there is some dispersion of input and/or output provided by dendritic and axonal arborizations (double lines in Figure); this is particularly true of the GPe and GPi, in which dendritic arborizations are enormous, spanning 1.5 mm ; and 4) central nervous
system processing (CNS) consists of sequential settling
into constellations of attractor states within attractor
basins generated throughout the CNS; this settling process
is achieved by innumerable back and forth volleys of neural
transmission (transmission time from the frontal pole to
the occipital pole of the human brain is likely on the order
of 2 ms), governed in the cortex by processes involved in
achieving gamma synchrony ; in the course of these
back and forth volleys, the limited dispersion provided by
dendritic and axonal arborizations is multiplied many-fold
such that the entire smBG is recruited, settling occurs, and
parallel constraint satisfaction is achieved.
The model also assumes two major populations of GABAergic neurons in the striatum, one giving rise to the “direct” pathway, the other to the “indirect pathway” . Direct pathway striatal neurons project to GPe and GPi. Dopamine, acting at D1 receptors, renders them more excitable by cortical glutamatergic input. Indirect pathway striatal neurons project nearly exclusively to GPe, which in turn projects to STN, which projects to GPi. Dopamine, acting at D2 receptors, renders indirect pathway striatal neurons less excitable.
This simple model provides the basis for a settling process involving all of the structures in the model (an attractor “trench”) that eventuates, over perhaps a hundred volleys, in the achievement of one or more attractor states in the cortical targets of the thalamus. Dopamine, by virtue of its opposite actions on the two GABAergic populations in the striatum, regulates both the number of states and the depth of the cortical attractor basins. High dopamine levels in the striatum will promote the generation of multiple motorically compatible attractor basins and will maximize the depth of these basins. Low dopamine levels will minimize the number of attractor basins (the explanation for the impairment in simultaneous or temporally closely sequenced movements exhibited by patients with Parkinson’s disease (PD) [29,30]), and minimize their depth (hence the hypometria and bradykinesia of patients with PD). Increases in dopamine facilitate behavioral switches (by expanding the repertoire of attractor trenches) while decreases in dopamine retard switching (as shown ).
The Evolutionary Perspective
An exact homology can be drawn between every aspect of BG structure, connectivity, neural organization, ion channels, and neurotransmitters in humans and the lamprey, the phylogenetically oldest group of living vertebrates to have diverged from the mammalian evolutionary line [32-34]. Extensive homology can be drawn between the BG of humans and lampreys and the insect central complex (e.g., in Drosophila), including
embryological derivation, orthologous genetic specification,
neural architecture, neurochemical attributes (including
the modulating influence of dopamine), physiological
properties, and behavioral outcomes of neural activity
[25,35]. The common thread linking structure and
function in these disparate creatures is dimensionality
reduction : sensory inputs define a complex, high
dimensionality space that must be optimally translated
into a low dimensionality space corresponding to a limited
repertoire of possible behavioral responses (see also ).
For example, the lamprey receives rich visual, vestibular,
auditory, somatosensory, olfactory, and lateral line organ
input of different types from various regions in space
(suggestion the adaptive value of rich multimodal sensory
input), as well as neural input reflecting the current state of
its motor system and internal drives (e.g., hunger, sexual
urge), and it must translate this into swimming forward
or backward, lateral and vertical bending movements,
mouth movements (biting and sucking), and release of
eggs or sperm . In computational terms, sensory input defines, through the BG/central complex, specific
positions within an attractor trench landscape in which
attractor state constellations and behaviors are achieved
that constitute optimal responses to extremely complex,
multimodal patterns of sensory input without incurring
mutually incompatible behaviors .
The adaptive value of such a mechanism provides a robust explanation for its evolutionary conservation dating back at least to arthropods. Evidence of a neurological basis for attractor dynamics has recently been reported in Drosophila [37-40]. Dupre and Yuste  have provided evidence of population encoding (but not attractor neurodynamics) in Hydra vulgaris, a far more primitive invertebrate classed in the phylum Cnidaria, which includes jelly fish. These observations suggest that PDP may be the computational mechanism underlying neural function in all creatures with multicellular nervous systems, evolutionarily conserved through perhaps a billion years because of its adaptive value. They also illuminate the central role that the BG has played throughout much of the evolution of complex creatures: reactive intention — automatic responses to configurations of sensory input .
This brief review has elucidated, using a simple PDPinspired model, the essential computational function of the BG. Given the architectural similarities between the smBG and the rostroventral basal ganglia (rvBG) — the portion incorporating the ventral head of the caudate nucleus and nucleus accumbens, we can assume that the rvBG serves a similar purpose of dimensionality reduction. However, the dimensions being reduced — the province of dorsolateral and orbitofrontal cortex — are unclear. Furthermore, we have neither animal models nor human selective lesion models to guide us.
As I have detailed, data emerging from animal studies (mice, lamprey, Drosophila, Hydra) suggest that PDP may provide the fundamental basis for CNS computation in all creatures with multicellular nervous systems.
The evidence suggests that the smBG has been the lynchpin of CNS function for perhaps as much as a billion years. How is it possible, then, to say that the smBG has become an anachronism in humans — a structure that serves no useful purpose but can wreak havoc when its function is impaired? This conclusion was based upon four considerations: 1) well-placed lesions in the middle of the posteroventral aspect of the GPi are almost definitive in their relief of Parkinsonian symptoms and apparently at no clinical cost; 2) the cortex (vestigial in lamprey) has become so large and the behavioral repertoire so expansive in humans that the necessity for dimensionality reduction has been markedly reduced; 3) we have extensive evidence of cortical systems, well developed in higher animals, particularly primates, subserving dimensionality reduction: working memory and volitional and reactive attention; and 4) much of human behavior is volitional, not automatic, driven by the dorsolateral frontal cortex, which is informed of objective knowledge and perceptual input by postcentral cortices and of subjective knowledge and limbic input from orbitofrontal cortex . These considerations compel us to view the nervous system of any creature in an entirely different way: to understand that any structure, pattern of connectivity, neuron type, or neurotransmitter receptor may still have adaptational value, or it may be an evolutionary relic.
This work was supported by resources provided by the North Florida/South Georgia Veterans Health System, Gainesville, FL. It was not supported by a specific grant from funding agencies in the public, commercial, or notfor- profit sectors. I am indebted to Alfonso Martin-Peña for alerting me to the work of Rafael Yutse and Daniel Turner-Evans. I am very grateful to John Richardson for creation of the figure.
The contents of this manuscript do not represent the views of the U.S. Department of Veterans Affairs, the United States Government, or the University of Florida.
Conflicts of interest
The author has no conflicts of interest bearing on this
- Churchland PS, Sejnowski TJ. The computational
brain. Cambridge, Massachusetts: MIT Press; 1992.
- Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT.
On the relations between the direction of two-dimensional
arm movements and cell discharge in primate motor
cortex. Journal of Neuroscience. 1982 Nov 1;2(11):1527-
- Lebedev MA, Nicolelis MA. Brain-machine
interfaces: From basic science to neuroprostheses and
neurorehabilitation. Physiological Reviews. 2017 Apr;
- O’keefe J, Nadel L. The hippocampus as a cognitive
map. Behavioral and Brain Sciences. 1979; 2:487-533.
- Deco G, Rolls ET. Computational neuroscience of
vision. Oxford: Oxford University Press; 2002.
- Rolls ET, Treves A, Rolls ET. Neural networks and
brain function. New York: Oxford university press; 1998.
- Rolls ET. Cerebral cortex: principles of operation.
Oxford: Oxford University Press; 2016.
- Zhang K, Ginzburg I, McNaughton BL, Sejnowski
TJ. Interpreting neuronal population activity by
reconstruction: unified framework with application to
hippocampal place cells. Journal of Neurophysiology.
1998 Feb 1; 79(2):1017-44.
- Zhang K, Sejnowski TJ. Neuronal tuning: To sharpen
or broaden?. Neural Computation. 1999 Jan 1;11(1):75-84.
- Behrmann M, Plaut DC. Distributed circuits, not
circumscribed centers, mediate visual recognition. Trends
in Cognitive Sciences. 2013 May 1;17(5):210-9.
- Stefanini F, Kushnir L, Jimenez JC, Jennings JH,
Woods NI, Stuber GD, Kheirbek MA, Hen R, Fusi S. A
distributed neural code in the dentate gyrus and in CA1.
Neuron. 2020 Aug 19; 107(4):703-16.
- McClelland JL, Rumelhart DE, PDP Research Group.
Parallel distributed processing. Cambridge, MA: MIT
- Hopfield JJ, Tank DW. Computing with neural
circuits: A model. Science. 1986 Aug 8;233(4764):625-33.
- Nadeau SE. Neural population dynamics and cognitive
function. Frontiers in Human Neuroscience. 2020 Mar 12; 14:50.
- Nadeau SE. The neural architecture of grammar.
Cambridge: MIT Press; 2012.
- Nadeau SE. Mechanisms of aging-related cognitive
decline. In: Heilman KM, Nadeau SE, editors. Cognitive
Changes and the Aging Brain. Cambridge, U.K.: Cambridge
University Press; 2019. p. 226-44.
- Carrillo-Reid L, Han S, Yang W, Akrouh A, Yuste
R. Controlling visually guided behavior by holographic
recalling of cortical ensembles. Cell. 2019 Jul 11;
- Carrillo-Reid L, Yang W, Bando Y, Peterka DS, Yuste
R. Imprinting and recalling cortical ensembles. Science.
2016 Aug 12; 353(6300):691-4.
- Carrillo-Reid L, Yuste R. Playing the piano with
the cortex: role of neuronal ensembles and pattern
completion in perception and behavior. Current Opinion
in Neurobiology. 2020; 64:89-95.
- Marshel JH, Kim YS, Machado TA, Quirin S, Benson
B, Kadmon J, Raja C, Chibukhchyan A, Ramakrishnan
C, Inoue M, Shane JC. Cortical layer–specific critical
dynamics triggering perception. Science. 2019; 365:1-12.
- Montijn JS, Goltstein PM, Pennartz CM. Mouse
V1 population correlates of visual detection rely on
heterogeneity within neuronal response patterns. Elife.
2015 Dec 8; 4:e10163.
- Peron S, Pancholi R, Voelcker B, Wittenbach JD,
Ólafsdóttir HF, Freeman J, Svoboda K. Recurrent
interactions in local cortical circuits. Nature. 2020 Mar;
- Petersen RS, Brambilla M, Bale MR, Alenda A, Panzeri
S, Montemurro MA, Maravall M. Diverse and temporally
precise kinetic feature selectivity in the VPm thalamic
nucleus. Neuron. 2008 Dec 10; 60(5):890-903.
- Nadeau SE. Basal Ganglia and Thalamic Contributions
to Language Function: Insights from A Parallel Distributed
Processing Perspective. Neuropsychology Review. 2021
- Fiore VG, Dolan RJ, Strausfeld NJ, Hirth F.
Evolutionarily conserved mechanisms for the selection
and maintenance of behavioural activity. Philosophical Transactions of the Royal Society B: Biological Sciences.
2015 Dec 19; 370(1684):20150053.
- Percheron G, Yelnik J, François C. A Golgi analysis
of the primate globus pallidus. III. Spatial organization
of the striato-pallidal complex. Journal of Comparative
Neurology. 1984 Aug 1; 227(2):214-27.
- Fries P. Rhythms for cognition: communication
through coherence. Neuron. 2015 Oct 7; 88(1):220-35.
- Gerfen CR, Surmeier DJ. Modulation of striatal
projection systems by dopamine. Annual review of
neuroscience. 2011 Jul 21; 34:441-66.
- Benecke R, Rothwell JC, Dick JP, Day BL, Marsden
CD. Performance of simultaneous movements in patients
with Parkinson’s disease. Brain. 1986 Aug 1; 109(4):739-
- Benecke R, Rothwell JC, Dick JP, Day BL, Marsden
CD. Disturbance of sequential movements in patients with
Parkinson’s disease. Brain. 1987 Apr 1; 110(2):361-79.
- Redgrave P, Prescott TJ, Gurney K. The basal
ganglia: a vertebrate solution to the selection problem?.
Neuroscience. 1999; 89(4):1009-23.
- Grillner S, Robertson B. The basal ganglia over 500
million years. Current Biology. 2016 Oct 24; 26(20):R1088-
- Stephenson-Jones M, Samuelsson E, Ericsson J,
Robertson B, Grillner S. Evolutionary conservation of
the basal ganglia as a common vertebrate mechanism for
action selection. Current Biology. 2011 Jul 12;21(13):1081-
- Ocaña FM, Suryanarayana SM, Saitoh K, Kardamakis
AA, Capantini L, Robertson B, Grillner S. The lamprey
pallium provides a blueprint of the mammalian motor
projections from cortex. Current Biology. 2015 Feb
- Strausfeld NJ, Hirth F. Deep homology of arthropod
central complex and vertebrate basal ganglia. Science.
2013 Apr 12; 340(6129):157-61.
- Bar-Gad I, Morris G, Bergman H. Information
processing, dimensionality reduction and reinforcement
learning in the basal ganglia. Progress in Neurobiology.
2003 Dec 1; 71(6):439-73.
- Turner-Evans DB, Jensen KT, Ali S, Paterson T,
Sheridan A, Ray RP, Wolff T, Lauritzen JS, Rubin GM, Bock
DD, Jayaraman V. The neuroanatomical ultrastructure
and function of a biological ring attractor. Neuron. 2020
Oct 14; 108(1):145-63.
- Kim SS, Rouault H, Druckmann S, Jayaraman V.
Ring attractor dynamics in the Drosophila central brain.
Science. 2017 May 26; 356(6340):849-53.
- Fisher YE, Lu J, D’Alessandro I, Wilson RI.
Sensorimotor experience remaps visual input to a headingdirection
network. Nature. 2019 Dec; 576(7785):121-5.
- Pisokas I, Heinze S, Webb B. The head direction
circuit of two insect species. Elife. 2020 Jul 6;9:e53985.
- Dupre C, Yuste R. Non-overlapping neural networks in Hydra vulgaris. Current Biology. 2017 Apr 24; 27(8):1085-97.
- Nadeau S. Neural mechanisms of emotions,
alexithymia, and depression. In: Heilman KM, Nadeau
SE, editors. Handbook of Clinical Neurology Emotional
Disorders Associated with Neurological Diseases.
Amsterdam: Elsevier; In press.
- Yelnik J. Functional anatomy of the basal ganglia.
Movement disorders: official journal of the Movement
Disorder Society. 2002 Mar; 17(S3):S15-21.