Neuromodulatory control of interacting medial temporal lobe and neocortex in memory consolidation and working memory.

A model of the interacting medial temporal lobe and neocortical association areas is formulated and applied to memory consolidation and working memory. A simplified connectivity in terms of superassemblies and assemblies of neurons representing types and values, respectively, of object features is underlying the model. Realistic low-dimensional model neurons, developed in particular to take neuronal adaptation into account, are employed. Observed short- and long-term potentiation and depression of plastic synaptic couplings are incorporated. It is shown that memory consolidation by long-term potentiation, based on repeated activations of neocortical patterns, may be guided by neuromodulated dynamics of the medial temporal lobe via short-term couplings acting as pointers. Bifurcations, i.e. transitions between different modes of network dynamics, with respect to developing synaptic couplings are shown to depend on the adaptivity of excitatory neurons in the medial temporal lobe and thus to be under neuromodulatory control. At weak adaptivity, after an initial temporal segmentation of several objects accounting for the capacity of working memory to resolve several items, attention is selectively focused on a single object according to the model. At intermediate adaptivity, reactivations may persist and long-term synaptic couplings gradually develop. At strong adaptivity, the model predicts attention and memory consolidation to be subsequently terminated. The neuromodulatory control of the interacting medial temporal lobe and neocortical system via the adaptivity of excitatory neurons may account for several observations on the influence of neuromodulators on various cognitive processes and brain disorders.

Stochastic and reduced biophysical models of synaptic transmission.

Stochastic and reduced biophysical models of synaptictransmission are formulated and evaluated. Thesynaptic transmission involves presynapticfacilitation of neurotransmitter release, depletionand recovery of the presynaptic pool of readilyreleasable vesicles containing neurotransmittermolecules and saturation of postsynaptic receptors ofboth fast non-NMDA and slow NMDA types. The models areshown to display the principal dynamicalcharacteristics experimentally observed of synaptictransmission. The two main types of neural coding,i.e. rate and temporal coding, can be distinguished bymeans of different dynamical properties of synaptictransmission determined by initial neurotransmitterrelease probability and presynaptic firing rate. Fromthe temporal evolution of the postsynaptic membranepotential response to a train of presynaptic actionpotentials at a sustained firing rate, in particularthe steady-state amplitude and steady-state averagelevel of postsynaptic membrane potentials aredetermined as functions of both initial releaseprobability and presynaptic firing rate. The modelsare applicable to studies of the primary stages oflearning processes and can be extended to incorporateshort-term and long-term potentiation in memoryconsolidation processes.

Control of resolution and perception in working memory.

Mechanisms underlying and controlling resolution and perception in working memory are studied by means of a pulse-coupled network model. It is shown that the adaptivity, i.e. the degree to which previous activity affects the ability to fire, of the excitatory units can control several aspects of the network dynamics in a coordinated way to enable multiple items to be resolved and perceived in working memory. One basic aspect is the complexity of the dynamics that regulates the temporal resolution of several items. The slow NMDA-receptor-mediated component of synaptic couplings to excitatory units facilitates successive activations of a given item. The dimension of the activated subspace of the complete available neural representation space is gradually decreased as adaptivity is reduced. It is also shown that the formation of perception by sufficiently intense and coherent activation of different features of an object can be controlled concurrently with resolution by the adaptivity. The mechanisms derived can account for the observed capacity of working memory with respect to number of items consciously resolved and also for the observed temporal separation of different items. Numerous observations link neuromodulators to cognitive functions and to various brain disorders involving working memory. Based on the influence of various neuromodulators on neuronal adaptivity, the model can also account for neuromodulatory regulation of working memory functions.

A neural mechanism of the generation of meaning in cognitive processes.

A neural mechanism for control of computational dynamics underlying the generation of meaning in cognitive processes is demonstrated. Meaning derives from recognition of connections between items in related conceptual classes of the neural representation in the brain. It is generated from a stimulated item in one conceptual class by an associative process in which linked items in related conceptual classes are activated in parallel. The complexity of the dynamics of this process varies between exploratory and direct retrieval modes. It is shown that the dynamics mode of the generation of meaning can be controlled by the neuronal adaptivity, i.e., the coupling strength between neuronal activity and excitability. Neuronal adaptivity in turn is controlled by neuromodulators in the brain. An autonomous regulation of the dynamics can be accomplished by an activity-dependent release of neuromodulators. The generation of a sequence of bifurcations from an initial exploratory phase to a final retrieval of appropriate item in each conceptual class is demonstrated. The time required for retrieval is shown also to depend on synaptic coupling strengths. The involvement of neuromodulatory systems in cognitive processes has long been observed but the underlying mechanisms not known. The present model describes a mechanism based on the primary effect observed of neuromodulators, viz. that on neuronal adaptivity, and is shown to be consistent with many neuroanatomical, neurophysiological and behavioural observations.

Control of computational dynamics of coupled integrate-and-fire neurons.

Generation and control of different dynamical modes of computational processes in a net of interconnected integrate-and-fire neurons are demonstrated. A net architecture resembling a generic cortical structure is formed from pairs of excitatory and inhibitory units with excitatory connections between and inhibitory connections within pairs. Integrate-and-fire model neurons derived from detailed conductance-based models of neocortical pyramidal cells and fast-spiking interneurons are employed for the excitatory and inhibitory units, respectively. Firing-rate adaptation is incorporated into the excitatory units based on the regulation of the slow afterhyperpolarization phase of action potentials by intracellular calcium ions. Saturation of synaptic conductances is implemented for the interconnections between units. It is shown that neuronal adaptation of the excitatory units can generate richer net dynamics than relaxation to fixed-point attractors-in a pattern space. At strong adaptivity, i.e. when the neuronal excitability is strongly influenced by the preceding activity, complex dynamics of either aperiodic or limit-cycle character are generated in both the pattern space and the phase space of all dynamical variables. This regime corresponds to an exploratory mode of the system, in which the pattern space can be searched. At weak adaptivity, the dynamics are governed by fixed-point attractors in the pattern space, and this corresponds to a mode for retrieval of a particular pattern. In the brain, neuronal adaptivity can be regulated by various neuromodulators. The results are in accordance with those recently obtained by means of more abstract models formulated in terms of mean firing rates. The increased realism makes the present model reveal more detailed mechanisms and strengthens the relevance of the conclusions to biological systems. The simplicity and realism of the coupled integrate-and-fire neurons make the present model useful for studies of systems in which the temporal aspects of neural coding are important.

Response characteristics of a low-dimensional model neuron.

It is shown that a low-dimensional model neuron with a response time constant smaller than the membrane time constant closely reproduces the activity and excitability behavior of a detailed conductance-based model of Hodgkin-Huxley type. The fast response of the activity variable also makes it possible to reduce the model to a one-dimensional model, in particular for typical conditions. As an example, the reduction to a single-variable model from a multivariable conductance-based model of a neocortical pyramidal cell with somatic input is demonstrated. The conditions for avoiding a spurious damped oscillatory response to a constant input are derived, and it is shown that a limit-cycle response cannot occur. The capability of the low-dimensional model to approximate higher-dimensional models accurately makes it useful for describing complex dynamics of nets of interconnected neurons. The simplicity of the model facilitates analytic studies, elucidations of neurocomputational mechanisms, and applications to large-scale systems.

A low-dimensional, time-resolved and adapting model neuron.

A low-dimensional, time-resolved and adapting model neuron is formulated and evaluated. The model is an extension of the integrate-and-fire type of model with respect to adaptation and of a recent adapting firing-rate model with respect to time-resolution. It is obtained from detailed conductance-based models by a separation of fast and slow ionic processes of action potential generation. The model explicitly includes firing-rate regulation via the slow afterhyperpolarization phase of action potentials, which is controlled by calcium-sensitive potassium channels. It is demonstrated that the model closely reproduces the firing pattern and excitability behaviour of a detailed multicompartment conductance-based model of a neocortical pyramidal cell. The inclusion of adaptation in a model neuron is important for its capability to generate complex dynamics of networks of interconnected neurons. The time-resolution is required for studies of systems in which the temporal aspects of neural coding are important. The simplicity of the model facilitates analytical studies, insight into neurocomputational mechanisms and simulations of large-scale systems. The capability to generate complex network computations may also make the model useful in practical applications of artificial neural networks.

Dynamics control of semantic processes in a hierarchical associative memory.

A neural mechanism for control of dynamics and function of associative processes in a hierarchical memory system is demonstrated. For the representation and processing of abstract knowledge, the semantic declarative memory system of the human brain is considered. The dynamics control mechanism is based on the influence of neuronal adaptation on the complexity of neural network dynamics. Different dynamical modes correspond to different levels of the ultrametric structure of the hierarchical memory being invoked during an associative process. The mechanism is deterministic but may also underlie free associative thought processes. The formulation of an abstract neural network model of hierarchical associative memory utilizes a recent approach to incorporate neuronal adaptation. It includes a generalized neuronal activation function recently derived by a Hodgkin-Huxley-type model. It is shown that the extent to which a hierarchically organized memory structure is searched is controlled by the neuronal adaptability, i.e. the strength of coupling between neuronal activity and excitability. In the brain, the concentration of various neuromodulators in turn can regulate the adaptability. An autonomously controlled sequence of bifurcations, from an initial exploratory to a final retrieval phase, of an associative process is shown to result from an activity-dependent release of neuromodulators. The dynamics control mechanism may be important in the context of various disorders of the brain and may also extend the range of applications of artificial neural networks.

Generation of associative processes in a neural network with realistic features of architecture and units.

A recent neural network model of cortical associative memory incorporating neuronal adaptation by a simplified description of its underlying ionic mechanisms is extended towards more realistic network units and architecture. Excitatory units correspond to groups of adapting pyramidal neurons and inhibitory units to groups of nonadapting interneurons. The network architecture is formed from pairs of one pyramidal and one interneuron unit each with inhibitory connections within and excitatory connections between pairs. The degree of adaptability of the pyramidal units controls the character of the network dynamics. An intermediate adaptability generates limit cycles of transitions between stored patterns and regulates oscillation frequencies in the range of theta rhythms observed in the brain. In particular, neuronal adaptation can impose a direction of transitions between overlapping patterns also in a symmetrically connected network. The model permits a detailed analysis of the transition mechanisms. Temporal sequences of patterns thus formed may constitute parts of associative processes, such as recall of stored sequences or search of pattern subspaces. As a special case, neuronal adaptation can accomplish pattern segmentation by which overlapping patterns are temporally resolved. The type of limit cycles produced by neuronal adaptation may also be of significance for central pattern generators, also for networks involving motor neurons. The applied learning rule of Hebbian type is compared to a modified version also common in neural network modelling. It is also shown that the dependence of the network dynamic behaviour on neuronal adaptability, from fixed point attractors at weak adaptability towards more complex dynamics of limit cycles and chaos at strong adaptability, agrees with that recently observed in a more abstract version of the model. The present description of neuronal adaptation is compared to models based on dynamic firing thresholds.

Control of the complexity of associative memory dynamics by neuronal adaptation.

An abstract neural network model of the Hopfield type is extended to incorporate neuronal adaptation by defining the state of a neuron in terms of two variables, activity and excitability. The model is formulated to represent the regulation of the firing rate of action potentials in a biological system via the neuron cell membrane afterhyperpolarization by the effect of intracellular calcium ion concentration on the conductance of calcium sensitive potassium channels. It is shown that the complexity, and thus the exploratory degree, of associative memory dynamics are controlled by neuronal adaptability. At low adaptability, the dynamics have fixed point attractors corresponding to direct memory retrieval. In a subsequent region of adaptability values, a simple limit cycle persists with frequency increasing with adaptability. The range of frequencies agrees with that observed for theta rhythms of activity in the brain. A higher degree of freedom of the associative process corresponding to more complex dynamics, either limit cycles of varying complexity and period or chaotic behaviour, results at higher adaptability. In the brain, the neuronal adaptability is regulated by neuromodulators which suppress adaptation and increase absolute firing rates of action potentials. An associative process can be started at low concentration of neuromodulators as an exploratory search of state space during which firing rates are low. As the concentration of neuromodulators increases, the state space search becomes simpler cyclic and more restricted, and firing rates increase. Eventually, a particular stored state is retrieved and its activity is high. This correspondence between the complexity of associative memory dynamics and the concentration of neuromodulators is consistent with the observation for Alzheimer's disease of selective degeneracy of neurons releasing the neuromodulator acetylcholine. In an artificial neural network, inclusion of adaptation among neuronal properties allows control of the degree of freedom of associative processes and thus extends the range of possible applications.

Intermediate and stable redox states of cytochrome c studied by low temperature resonance Raman spectroscopy.

Stabilized intermediate redox states of cytochrome c are generated by radiolytic reduction of initially oxidized enzyme in glass matrices at liquid nitrogen temperature. In the intermediate states the heme group is reduced by hydrated electrons, whereas the protein conformation is restrained close to its oxidized form by the low-temperature glass matrix. The intermediate and stable redox states of cytochrome c at neutral and alkaline pH are studied by low-temperature resonance Raman spectroscopy using excitations in resonance with the B (Soret) and Q1 (beta) optical transitions. The assignments of the cytochrome c resonance Raman bands are discussed. The observed spectral characteristics of the intermediate states as well as of the alkaline transition in the oxidized state are interpreted in terms of oxidation-state marker modes, spin-state marker modes, heme iron--axial ligand stretching modes, totally symmetric in-plane porphyrin modes, nontotally symmetric in-plane modes, and out-of-plane modes.

Continuous flow-resonance Raman spectroscopy of an intermediate redox state of cytochrome C.

An intermediate redox state of cytochrome c at alkaline pH, generated upon rapid reduction by sodium dithionite, has been observed by resonance Raman (RR) spectroscopy in combination with the continuous flow technique. The RR spectrum of the intermediate state is reported for excitation both in the (alpha, beta) and the Soret optical absorption band. The spectra of the intermediate state are more like those of the stable reduced form than those of the stable oxidized form. For excitation of 514.5 nm, the most prominent indication of an intermediate state is the wave-number shift of one RR band from 1,562 cm-1 in the stable oxidized state through 1,535 cm-1 in the intermediate state to 1,544 cm-1 in the stable reduced state. For excitation at 413.1 nm, a band, present at 1,542 cm-1 in the stable reduced state but not present in the stable oxidized state, is absent in the intermediate state. We interpret the intermediate species as the state where the heme iron is reduced but the protein remains in the conformation of the oxidized state, with methionine-80 displaced as sixth ligand to the heme iron, before relaxing to the conformation of the stable reduced state, with methionine-80 returned as sixth ligand.

Time-resolved resonance Raman spectroscopy of cytochrome c reduced by pulse radiolysis.

The first investigation of the dynamics of a redox transition of an electron-transfer enzyme by time-resolved resonance Raman spectroscopy in combination with pulse-radiolytic reduction is described by an application to cytochrome c. A long-lived transient state is observed upon reduction of the alkaline form of cytochrome c as a distinct frequency shift of one resonance Raman band. From the frequency in the stable oxidized state, 1567 cm(-1), this particular resonance Raman band shifts within less than 1 microsecond to 1533 cm(-1) in the transient reduced state, which has a lifetime longer than 20 ms but shorter than a few seconds. Finally, in the stable reduced state, this band is located at 1547 cm(-1). According to a previous normal coordinate analysis, this resonance Raman band can be assigned predominantly to a stretching mode of the outermost C-C bonds in the four pyrrole rings of porphyrin. This vibrational mode is influenced by the protein most directly through the covalent thioether linkages of two cysteines to porphyrin. We interpret the long lifetime of the transient state as due to the slow return of Met-80 as sixth ligand to the heme iron upon reduction of the alkaline form of cytochrome c.

A molecular mechanism of the energetic coupling of a sequence of electron transfer reactions to endergonic reactions.

A molecular mechanism of the energetic coupling of a sequence of electron transfer reactions to endergonic reactions is proposed and discussed from a physical point of view. The scheme represents a synthesis of concepts of electron transfer by tunneling and the conformational and chemiosmotic aspects of energy coupling processes. Its relation to existing experimental information and theoretical models is discussed, and further experimental tests are suggested.