biological solutions to the selection problem peter redgrave neuroscience research group department...
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Biological solutions to the selection problem
Peter RedgraveNeuroscience Research Group
Department of Psychology,
University of Sheffield
Abbreviations:CPu, caudate putamenGP, globus pallidusMRF, medullary reticular formationSC, superior colliculusSNr, substantia nigra pars reticulataSTN, subthalamic nucleus
CPu
GP
SNr
Vm
SC
MRF
STNEN
The Team• Kevin Gurney
• Tony Prescott
• Mark Humphries
• Fernando Montes Gonzales
• Khepera Robot
Staking out the ground
• Level versus content of consciousness– “A useful distinction can be made between factors
influencing the overall level of consciousness and those determining its content”.
• Critical role for attention– “The studies of perception….demonstrate that stimuli can
be highly processed and yet not enter awareness. Attention might be the critical mechanism by which preprocessed stimuli are selected for awareness”.
Frith, C., R. Perry, et al. (1999). "The neural correlates of conscious experience: an experimental framework." Trends Cogn Sci 3: 105-114.
Outline of talk
• Selection - a fundamental computational problem
• Theoretical solutions
• Basal ganglia as a biological solution
• Tests of the selection hypothesis– Simulation
– Robotics
• Is the robot conscious ?
A General Architecture of the Brain
• Critical concept:
– Independent functional units - modules
– Each with:
• critical sensory input
• specific objectives, actions or movements as output
Necessary implication : Selection Problem
Behavioural output(Feeding)
Fluid balance(Drinking)
Predisposing Conditions
MotorResources
Energy balance(Feeding)
Threat(Escape)
• Multiple command systems
• Spatially distributed
• Sensory specificities
• Output specificities (often exclusive)
• Processing in parallel
• All act through final common motor path
Theoretical Solutions
• Recurrent reciprocal inhibition– Selection an emergent
property– Positive feedback– Winner-take-all
• Centralised selection– Localised switching
– Dissociates selection from perception and motor control
MotorPlant
MotorPlant
InputSaliencies
InputSaliencies
Problems of Scale
• Recurrent reciprocal inhibition– Each additional
competitor increases connections by n(n-1)
• Centralised selection– Each additional
competitor adds 2 further connections 3 competitors
6 connections3+1 competitors6+2 connections
8 competitors16 connections
3 competitors6 connections
3+1 competitors6+6 connections
8 competitors56 connections
Basal Ganglia: a biological solution to the selection problem
Rat
Human
Evolutionary conservatism
“The basal ganglia in modern mammals, birds and reptiles (i.e. modern amniotes) are very similar in connections and neurotransmitters, suggesting that the evolution of the basal ganglia in amniotes has been very conservative.”
Medina, L and Reiner, A.
Neurotransmitter organization and connectivity of the basal ganglia in vertebrates: Implications for the evolution of basal ganglia. Brain Behaviour and Evolution (1995) 46, 235-258
Functions attributed to the Basal Ganglia• Sensory
– Sensory perception (Brown et al 1997)– Analgesia (Chudler and Dong 1995)
• Cognitive– Attention (Jackson and Houghton 1995)– Temporal processing (Gibbon et al 1997) – Working memory (Levy et al 1997)– Habit learning (Gaffan 1996)
• Motor– Planning, selection and execution of motor strategies (Robbins and Brown 1990)– Initiating movement (Denny-brown 1962)– Scaling speed/size of movement (reviewed in Mink 1996)– Building action repertoires (Graybiel 1995)– Automatic execution of movement sequences (van den Bercken and Cools 1982)
• ?– Suppression of epileptic seizures (Depaulis 1994)
Clinical associations• Parkinson’s disease
– Disease of the motor system…….– But with increasingly appreciated cognitive overtones – set switching
• Schizophrenia– Cognitive, sensory and motor symptoms– Inability to suppress competing systems ?
• Addictions– Cravings dominate
• Other basal ganglia-related dysfunctions– Attention deficit disorder– Obsessive compulsive disorder– Tourette’s syndrome– Altzheimer’s disease
Abbreviations:CPu, caudate putamenGP, globus pallidusMRF, medullary reticular formationSC, superior colliculusSNr, substantia nigra pars reticulataSTN, subthalamic nucleus
CPu
GP
SNr
Vm
SC
MRF
STNEN
Basal Ganglia: External Connectivity• External command systems
– Cortical – Limbic– Midbrain
• Command inputs– Sensory– Cognitive– Affective
• Command outputs– Converge on brainstem and
spinal motor generators
• Links with basal ganglia– Closed loop connections
Redgrave et al (1999) Neuroscience, 89, 1009-1023
Basal Ganglia Architecture : Loops I
Alexander, G. E., M. R. DeLong, et al. (1986). "Parallel organization of functionally segregated circuits linking basal ganglia and cortex." Ann. Rev. Neurosci. 9: 357-381.
Loops: a specific example
Middleton, F. A. and P. L. Strick (1996). "The temporal lobe is a target of output from the basal ganglia." Proc Natl Acad Sci USA 93(16): 8683-8687.
Phasic/excitatory
Phasic/inhibitory
Tonic/inhibitory
Phasic/Disinhibitory(PositiveFeedback)
Basal Ganglia: Repeating microcircuitry
• External command systems– Cortical – Limbic– Midbrain
• Command functions– Sensory– Cognitive– Affective
Abbreviations:CPu, caudate putamenGP, globus pallidusMRF, medullary reticular formationSC, superior colliculusSNr, substantia nigra pars reticulataSTN, subthalamic nucleus
CPu
GP
SNr
Vm
SC
MRF
STNEN
Selection by inhibition and disinhibition
MotorResources
Predisposing Conditions
Energy balance(Feeding)
Threat(Escape)
Basal Ganglia
ExcitationInhibition
Behavioural output(Feeding)
Fluid balance(Drinking)
Predisposing Conditions
MotorResources
Energy balance(Feeding)
Threat(Escape)
The Selection Problem
Potential resolution
Serial Selection in the Basal Ganglia
Striatum
Inputs (Cortex/Thalamus)
Output Nuclei
Up-state/down-state filtering
1) Up-down states of medium spiny neurones
Local inhibitory circuits
2) Local inhibition in striatum
Local recurrent circuits4) Recurrent inhibition in output nuclei
Subthalamus
3) Diffuse/focused projection onto output nuclei
Focused inhibition
Diffuse excitation
Model: Analysis
MotorResources
Predisposing Conditions
Energy balance(Feeding)
Threat(Escape)
Basal Ganglia
ExcitationInhibition
Model neurons - leaky integrators with piecewise linear output
Analytic equilibrium solution(Kevin Gurney)
Gurney, K., T. J. Prescott, et al. (2001). "A computational model of action selection in the basal ganglia. I. A new functional anatomy." Biol Cybern 84: 401-410.
Model: Common currency
Koechlin, E. and Y. Burnod (1996). "Dual population coding in the neocortex: A model of interaction between representation and attention in the visual cortex." J. Cog. Neurosci. 8: 353-370.
Grandmother cell– out of fashion
A
B1
B2
NeuralActivity
Space
A B1
B2Stimuli
Population Coding– spatial distribution of activity = feature– area under curve = salience
Simulation results
Dynamic switching between channels on basis of changes in input salience
Computer Model: Conclusions
• Relatively easy to: – Take the basic architectural features of the basal
ganglia – Simulate them in a computer model– And have the model select between channels on the
basis of relative input salience (strength)
• Provides an existence proof of the hypothesised selective function
Next strategy
• Embody the model – Generates realistic (environmentally driven) sequences of
input– Forces interpretation of outputs in terms of actions
• Aim: To test if model can generate action sequences in a behaving robot– Research sought to model behavioural switching in a
foraging rat
Action Selection: Rat foraging
• Motivations– Hungry : 24hrs food
deprived– Frightened: placed in
open arena
• Behaviour– Initially keeps close to
walls and corners– Collects food– Returns to corner to
eat
Robot Action Selection: The foraging Khepera
• Motivations– Hunger– Fear
• 5 behavioural sub-systems– Wall seek– Wall follow– Can seek– Can pick-up– Can deposit
• 8 Infra-red sensors detect– Walls– Corners– Cans
• Gripper sensors detect– Presence/absence of can
The Physical Interface: Side-step problems of motor control
BasalGanglia
Sensor Data
Wheels/Gripper
PerceptualSub-systems
ActionSub-systems
At each time step: Basal ganglia/thalamus• Computes sub-systems saliences• Resolves competition• Disinhibits winning sub-system
Thalamus
Extrinsic Variables1) Perceptual sub-systems (Wall, corner, can, gripper)
3) Action sub-systems/current state
(Wall seek, corner seek, can
seek, can pick-up, can deposit)
MotivationalSub-systems
Intrinsic Variables2) Motivational sub-systems (Fear, Hunger)
Robot Action Selection: Model Dynamics
SelectionsCan Seek
Can Pick-up
Wall Seek
Wall follow
Can Deposit
Sensors
Motivations
FearHunger
Model dynamics
Saliences
Robot Ethogram
• Competitor with highest salience controls motor plant– Clean switching
– Little distortion
– Infrequent dithering
• Platform to test the effects of simulated: – Lesions
– Stimulation
– Pharmacological treatments
cylinder-seek
-1.5
-0.5
0.5
1.5
2.5
3.5
wall-seek
-1.5
-0.5
0.5
1.5
2.5
3.5
corner-seek
-1.5
-0.5
0.5
1.5
2.5
3.5
Salience BG output
Sig
nal
leve
l
time
cylinder-seek
0
0.2
0.4
0.6
0.8
1
Behaviour selection
corner-seek
0
0.2
0.4
0.6
0.8
1
wall-seek
0
0.2
0.4
0.6
0.8
1
0
0.2
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0.6
0.8
1
-1.5
-0.5
0.5
1.5
2.5
3.5
cylinder-pickup cylinder-pickup
Conclusions
• Selection hypothesis confirmed in simulation and control of robot action selection
• Represents a generic task performed by the basal ganglia– High level behavioural objectives
– Actions
– Movements
• Consistent with early development and evolutionary conservation
• Explains basal ganglia ‘involvement’ in so many tasks
Wider implications
• Decision making– Head vs heart
• Cortical vs sub-cortical competitors
– Free will ? • Is the central selector a dumb switch ?
• Personality – A statistical profile of the winners and loosers
Implications for content of consciousness
• Selective attention/conscious awareness– Equivalent to current ‘winning’ channel and associated circuits ?
– No single location for consciousness
– Dynamically switches from region to region
• Dissociations in neuropsychology– Blindsight vs peripheral neglect
– Achomatopsia – loss of experience of colour
– Procedural memory in amnesics
– Prosopagnosia (failure to recognise familiar faces)
– Impulsiveness of pre-frontal patients
Conscious awareness in artificial systems
• Does the robot have any awareness ?
• Has limited representations of ‘hunger’ and ‘fear’
• Has representations of critical aspects of external world
• Uses biologically inspired architecture to select appropriate actions
Critical role for working memory
• Cannabis– Subjective awareness of “self”
• Receptors in human brain– High densities in basal ganglia
• Problem of selection– Disruption of working memory
loop ?
Glass, M., M. Dragunow, et al. (1997). "Cannabinoid receptors in the human brain: A detailed anatomical and quantitative autoradiographic study in the fetal, neonatal and adult human brain." Neuroscience 77: 299-318.
Critical role for episodic memory
• Diazepam – Unconscious consciousness
• Receptor distribution– High densities in cortex and
cerebellum
• Not related to basal ganglia– Selection fine
Young and Kuhar (1979) Autoradiographic localisation of benzodiazepine receptors in the brains of humans and animals Nature, 280, 393-395