3rd-5th September 2003 Foresight Cognitive Systems InterAction Conference Mike Denham & Roman Borisyuk, Centre for Theoretical and Computational Neuroscience,

3rd-5th September 2003Foresight Cognitive Systems InterAction Conference

Mike Denham & Roman Borisyuk, Centre for Theoretical and Computational Neuroscience, Plymouth

Miles Whittington, School of Biomedical Sciences, Leeds


slow (<1 Hz) oscillations in the thalamus during “slow wave” sleep

5-9 Hz “theta” rhythm in medial temporal lobe episodic memory-related areas

8-12 Hz “alpha”, 12-30 Hz “beta” and 30-80 Hz “gamma” rhythms in sensory and memory-related areas

Rhythmic neural activity is ubiquitous in the brain


Rhythms have multiple scales

From Varela et al., 2001

Aa Synchrony between single units in monkey area V1

b Local field potentials (LFPs) from eight recording electrodes in the suprasylvian gyrus of an awake cat.

c Transient episodes of synchrony within a population of neurons recorded intracranially over the occipito-temporal junction in an epileptic patient performing a visual discrimination task.

d When recorded from a surface electrode, such synchronous patches appear as spatial summation of cortical responses that give rise to transient increases in the gamma band.

B Patches of local synchrony in distant brain sites can enter into synchrony during cognitive tasks. Black lines link electrodes that are synchronous during the perception of the face.


Rhythmic activity appears to play a major role in information processing in the brain

Object recognition

Feature extraction/abstraction

Associative learning

Selective attention

Novelty detection


For example, the function of the gamma rhythm may be to provide a framework for processing in the temporal domain

Quantisation

Gating

Combination


Neural mechanisms involved in rhythmic activity


Gamma is an INHIBITION-BASED rhythm


Inhibition-based gamma recruits principal cells

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Convergence and divergence of synaptic connections

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Denham & Borisyuk, 2000

Theta rhythm may be generated in hippocampus and reinforced through a dynamic inhibitory interplay between the septum and the hippocampus, with ascending brainstem activity controlling the frequency of oscillation

Brainstem


Mathematical modelWe describe the inhibitory feedback circuit as a Wilson-Cowan model of four coupled populations of excitatory and inhibitory neurons in which the parameters are set consistent with experimental measurements of the dynamic responses of the neuron types involved.

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Mathematical modelWe compute the bifurcation diagrams for

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Boundaries correspond to the Andronov-Hopf bifurcation: a limit cycle appears if parameters cross the boundary

The natural frequency of this oscillation is in the theta range (approximately 6 Hz) and stays almost constant under variation of the circuit parameters.


There is a great deal of experimental evidence that rhythms in the brain play a significant role in perception

and cognition


Theta and gamma rhythms are implicated in the formation of new episodic memories

• intracranial recordings were made from 793 cortical and subcortical sites in 10 epileptic patients undergoing invasive monitoring at the Children’s Hospital Boston

• the results revealed that significant increases in oscillatory power in theta and gamma bands during encoding were able to predict the subsequent recall of lists of common nouns (Sederberg et al, 2003)


Theta rhythm is implicated in cognition function

Cognition-enhancing drugs produced a dose-dependent increase in stimulated hippocampal theta rhythm amplitude in rats, suggesting that theta rhythm may be closely associated with cognitive function (Kinney et al, 1999)


Good cognitive performance is related to two types of EEG phenomena :

(i) a tonic increase in alpha but a decrease in theta power, and

(ii) a large phasic (event-related) decrease in alpha but increase in theta, depending on the type of memory demands.

(Klimesch, 1999)

EEG measurements of alpha and theta rhythms appear to reflect

cognitive performance


Experimental data indicates that gamma rhythms are used for relatively local computations whereas beta rhythms are used for higher level interactions involving more distant structures and longer conduction delays, corresponding to signals travelling a significant distance in the brain. Kopell et al, 2000.

There is evidence that beta (12-30 Hz) and gamma (30-80 HZ) rhythms play a differential role in synchronisation of neural activity


The role of alpha rhythm (8-12 Hz) in perception?

Is perception discrete or continuous ? (VanRullen & Koch, TICS, 2003)


From VanRullen & Koch, 2003, adapted from Gho & Varela, 1988.

Perception of an event can be influenced by its relation to the phase of the occipital EEG alpha rhythm


However, contradictory to Koch’s ideas, it would appear that alpha rhythm is mostly present in sensory areas AFTER a sensory event.

For example, EEG alpha is suppressed by opening the eyes and with increased attentiveness (Vanni et al, 1997)


The role of the parietal-occipital alpha rhythm may be increasing S/N ratios within cortex by inhibition of unnecessary or conflicting stimuli or processes

Alpha synchronisation (ERS) increases in foot area when hand is activated and vice-versa (Suffczynski et al, 2001)


Hypothesis is that each period of the fast gamma rhythm underlies a specific representation.

Gamma is superimposed on a slower rhythm (alpha or theta) that effectively multiplexes the representations

This mechanism could explain how the interactions between neuronal rhythms participate in shaping the holding in short-term (working) memory of perceptual events: the fast wave representations would constitute the contents of each discrete snapshot, the entire percept being mediated by the slow waves.

Gamma-theta interaction

From VanRullen & Koch, 2003(Lisman &

Idiart, 1995).


Summary“The emergence of a unified cognitive moment appears to rely on the temporal coordination of scattered mosaics of functionally specialized brain regions.

The mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity and enable the emergence of coherent behaviour and cognition are still largely unknown

The most plausible candidate appears to be the formation of dynamic links mediated by synchrony over multiple frequency bands.”

Varela et al, Nature Reviews Neuroscience, 2003


Future Research Agenda

Future computational architectures for cognitive systems are similarly likely to involve spatially distributed, functionally-specialised information processing regions, like those in the brain, and will similarly require mechanisms for coordinating activity across these distributed processes .

The synchronisation of rhythmic activity between distributed processes may be a candidate mechanism to enable the efficient operation of such architectures.



To address this question it will be necessary to fully understand

•the neural mechanisms of rhythmic activity and synchronisation, both locally and across widely distributed regions,

•the interplay between slow and fast brain rhythms, and

•the role of synchronisation over different frequency bands.



It will also be necessary to understand how such mechanisms might be implemented in future computer hardware architectures

This will require the combined research efforts of several disciplines: experimental, theoretical and computational neuroscience, computer science, mathematics and cognitive neuropsychology


Key questions

•How does the brain build a coherent perceptual account of a sensory event in the case that the component features of the event are processed asynchronously in widely distributed areas of the cortex?

•Is rhythmic activity fundamental to this process?

•Will future articifial sensory systems require similar mechanisms?


Key questions

•Rhythmic activity is observed in regions of the brain strongly linked to memory storage and retrieval processes, in particular episodic memories.

•Does rhythmic activity play a role in the organisation, storage and retrieval of episodic memories, and if so, what role, eg "chunking" of individual perceptual/cognitive experiences into a complete "episode"?

•Does this have any impact on the way information composed of sequences of events might be stored/retrieved in future artificial cognitive systems?


Key questions•If future artificial cognitive systems employ "massively" distributed asynchronous processing hardware architectures, will they face the same problems as the brain in providing coherent behaviour?

•Is rhythmic, synchronised activity in the brain dependent on intrinsic neural mechanisms or is it an "emergent" behaviour of the brain resulting from inherent self-organising, adaptive processes?

•If so, would we expect to observe it as an emergent feature of any massively parallel, distributed self-organising computational architecture when it is required to deliver coherent behaviour?

Documents

3rd-5th September 2003 Foresight Cognitive Systems InterAction Conference Mike Denham & Roman Borisyuk, Centre for Theoretical and Computational Neuroscience,