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Alessandro Treves
SISSA, Cognitive Neuroscience, Faculty Member
- Married to Giordana.Convenor of the first and only Ararat Memory meeting.Invited speaker at the first Kim Il Sung Uni... moreMarried to Giordana.Convenor of the first and only Ararat Memory meeting.Invited speaker at the first Kim Il Sung University International Conference onthe Development of Science and Human Welfare, Pyongyang.Staunch admirer of the Palestinian Neuroscience Initiative.Educated at Liceo Classico Michelangiolo, Yale College, Univ of Florence, Univ of Rome La Sapienza, Hebrew University of Jerusalem, University of Oxford.In excellent terms with the younger version of myself who, for unfathomable reasons, sports an independent page on Academia.eduedit
- Advised by Guido Martinelli, Daniel Amit, Edmund Rolls., Tried to advise Stefano Panzeri, Francesco Battaglia, Yasser Roudi, Emilio Kropff, Athena Akrami, In excellent terms with the younger version of myself who, for unfathomable reasons, sports an independent page on Academia.eduedit
We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state—latching dynamics.... more
We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state—latching dynamics. We show that in one such model, and in simple spatial memory tasks we have given to human subjects, short-term memory can be limited to similar low capacity by interference effects, in tasks terminated by errors, and can exhibit similar sublinear scaling, when errors are overlooked. The same mechanism can drive serial recall if combined with weak order-encoding plasticity. Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.
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Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this... more
Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it...
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We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved... more
We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.
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We show that symmetric n-mixture states, when they exist, are almost never stable in autoassociative networks with threshold-linear units. Only with a binary coding scheme, we could find a limited region of the parameter space in which... more
We show that symmetric n-mixture states, when they exist, are almost never stable in autoassociative networks with threshold-linear units. Only with a binary coding scheme, we could find a limited region of the parameter space in which either 2-mixture or 3-mixture states are stable attractors of the dynamics.
Research Interests: Engineering, Neuroscience, Mathematics, Physics, Biophysics, and 15 moreNeural Networks, Statistical Physics, Neural Network, Medicine, Memory, Computer Simulation, Mathematical Sciences, Animals, Physical sciences, Physical, Associative Memory, Statistical models, parameter space, Content addressable memory, and attractor
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Research Interests: Engineering, Computer Science, Artificial Intelligence, Information Theory, Motor planning, and 13 moreNeural coding, Population, Motor Cortex, Neurocomputing, Supplementary Motor Area, Neuronal Activity, Cellular and Molecular Neuroscience, Sma, Primary Motor Cortex, Temporal Analysis, neural code, Psychology and Cognitive Sciences, and Time window
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Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing... more
Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an inco...
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Natural environments are represented by local maps of grid cells and place cells that are stitched together. The manner by which transitions between map fragments are generated is unknown. We recorded grid cells while rats were trained in... more
Natural environments are represented by local maps of grid cells and place cells that are stitched together. The manner by which transitions between map fragments are generated is unknown. We recorded grid cells while rats were trained in two rectangular compartments, A and B (each 1 m × 2 m), separated by a wall. Once distinct grid maps were established in each environment, we removed the partition and allowed the rat to explore the merged environment (2 m × 2 m). The grid patterns were largely retained along the distal walls of the box. Nearer the former partition line, individual grid fields changed location, resulting almost immediately in local spatial periodicity and continuity between the two original maps. Grid cells belonging to the same grid module retained phase relationships during the transformation. Thus, when environments are merged, grid fields reorganize rapidly to establish spatial periodicity in the area where the environments meet.
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Publisher Summary This chapter discusses information coding in higher sensory and memory areas. Neurons are vastly simpler than human beings are, but the metaphor is not completely silly because it illustrates the volatility of the notion... more
Publisher Summary This chapter discusses information coding in higher sensory and memory areas. Neurons are vastly simpler than human beings are, but the metaphor is not completely silly because it illustrates the volatility of the notion of neural codes. Information theory has been developed precisely to quantify communication and is quintessential to an appraisal of neural codes. Applying information theory to neural activity (rather than to the synthetic communication systems for which it was developed) is however riddled with practical problems and subtleties, which must be clarified before reporting experimental results. The chapter considers other means of neuronal communication than the emission of action potentials or spikes and regards them as self-similar all-or-none events whose only distinctive features are the time of emission and the identity of the emitting neuron. The extent to which the firing rates of a population of neurons may or may not carry most of the information represented in the complete list of spike emission times is a question to be addressed experimentally in any given situation.
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Recent advances in neuroscience are making it possible to describe the architecture and properties of some of the networks of neurones (brain cells) in brain structures, while neuronal network theory, including approaches related to the... more
Recent advances in neuroscience are making it possible to describe the architecture and properties of some of the networks of neurones (brain cells) in brain structures, while neuronal network theory, including approaches related to the statistical mechanics of spin glasses, is becoming helpful in analysing the computational properties of these networks. These developments are illuminating processes of information storage in the brain, and this is illustrated in the case of one brain structure considered here, the hippocampus.
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We argue that the debate on language universals should be directed away from the discussion as to whether typological diversity is or is not an argument against the existence of language universals. Instead, given our growing awareness of... more
We argue that the debate on language universals should be directed away from the discussion as to whether typological diversity is or is not an argument against the existence of language universals. Instead, given our growing awareness of the fact that the neural mechanisms underlying language use are the same as those underlying other cognitive functions in both humans and mammals, the central question for cognitive neuroscientists and linguists is what neural mechanisms can facilitate compositional interactions, and how the range of grammatical structures and the ability to use language creatively emerges from a much narrower range of neural mechanisms. We suggest two complementary methods for investigating these issues, one more linguistically oriented and one more computationally oriented, and present preliminary results from investigations concerning the expression of the mass-count distinction crosslinguistically, using both methodologies. These results suggest that universality in language might express itself at the deeper level of the computational operations involved in the processing of language, rather than in the results of those computations.
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Qualitatively, some real networks in the brain could be characterized as 'small worlds', in the sense that the structure of their connections is intermediate between the extremes of an orderly geometric arrangement and of a... more
Qualitatively, some real networks in the brain could be characterized as 'small worlds', in the sense that the structure of their connections is intermediate between the extremes of an orderly geometric arrangement and of a geometry-independent random mesh. Small worlds can be defined more precisely in terms of their mean path length and clustering coefficient; but is such a precise
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Understanding the neural basis of higher cognitive functions, such as those involved in language, requires a shift from mere localisation to an analysis of network operation. A recent proposal points at infinite recursion as the core of... more
Understanding the neural basis of higher cognitive functions, such as those involved in language, requires a shift from mere localisation to an analysis of network operation. A recent proposal points at infinite recursion as the core of several higher functions, and thus challenges cortical network theorists to describe network behaviour that could subserve infinite recursion. I propose here that a capacity for infinite recursion may be associated with the natural adaptive dynamics of large semantic associative networks, once their connectivity becomes sufficiently extensive to support structured transition probabilities between global network states. The crucial development endowing a semantic system with a nonrandom dynamics would thus be an increase in connectivity, perhaps to be identified with the dramatic increase in spine numbers recently observed in the basal dendrites of pyramidal cells in Old World monkey and particularly in human frontal cortex.
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Speed, noise, information and the graded nature of
A new decoding method is described that enables the information that is encoded by simultaneously recorded neurons to be measured. The algorithm measures the information that is contained not only in the number of spikes from each neuron,... more
A new decoding method is described that enables the information that is encoded by simultaneously recorded neurons to be measured. The algorithm measures the information that is contained not only in the number of spikes from each neuron, but also in the cross-correlations between the neuronal firing including stimulus-dependent synchronization effects. The approach enables the effects of interactions between the `signal' and `noise' correlations to be identified and measured, as well as those from stimulus-dependent cross-correlations. The approach provides an estimate of the statistical significance of the stimulus-dependent synchronization information, as well as enabling its magnitude to be compared with the magnitude of the spike-count related information, and also whether these two contributions are additive or redundant. The algorithm operates even with limited numbers of trials. The algorithm is validated by simulation. It was then used to analyze neuronal data from ...
Multielectrode arrays allow recording of the activity of many single neurons, from which correlations can be calculated. The functional roles of correlations can be revealed by the measures of the information conveyed by neuronal... more
Multielectrode arrays allow recording of the activity of many single neurons, from which correlations can be calculated. The functional roles of correlations can be revealed by the measures of the information conveyed by neuronal activity; a simple formula has been shown to discriminate the information transmitted by individual spikes from the positive or negative contributions due to correlations (Panzeri et
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I consider a mean-field description of the dynamics of interacting intergrate-and-fire neuron-like units. The basic dynamical variables are the membrane potential of each (point-like)'cell'and the... more
I consider a mean-field description of the dynamics of interacting intergrate-and-fire neuron-like units. The basic dynamical variables are the membrane potential of each (point-like)'cell'and the conductance associated with each synaptic connection, both of which ...
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ABSTRACT
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At the transition from early reptilian ancestors to primordial mammals, the areas of sensory cortex that process topographic modalities acquire the laminar structure of isocortex. A prominent step in lamination is granulation, whereby the... more
At the transition from early reptilian ancestors to primordial mammals, the areas of sensory cortex that process topographic modalities acquire the laminar structure of isocortex. A prominent step in lamination is granulation, whereby the formerly unique principal layer of pyramidal cells is split by the insertion of a new layer of excitatory, but intrinsic, granule cells, layer IV. I consider the hypothesis that granulation, and the differentiation between supra- and infra-granular pyramidal layers, may be advantageous to support fine topography in their sensory maps. Fine topography implies a generic distinction between "where" information, explicitly mapped on the cortical sheet, and "what" information, represented in a distributed fashion as a distinct firing pattern across neurons. These patterns can be stored on recurrent collaterals in the cortex, and such memory can help substantially in the analysis of current sensory input. The simulation of a simplifie...
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In a previous paper we have evaluated analytically the mutual information between the firing rates of N independent units and a set of multidimensional continuous and discrete stimuli, for a finite population size and in the limit of... more
In a previous paper we have evaluated analytically the mutual information between the firing rates of N independent units and a set of multidimensional continuous and discrete stimuli, for a finite population size and in the limit of large noise. Here, we extend the analysis to the case of two interconnected populations, where input units activate output ones via Gaussian weights and a threshold linear transfer function. We evaluate the information carried by a population of M output units, again about continuous and discrete correlates. The mutual information is evaluated solving saddle-point equations under the assumption of replica symmetry, a method that, by taking into account only the term linear in N of the input information, is equivalent to assuming the noise to be large. Within this limitation, we analyze the dependence of the information on the ratio M/N, on the selectivity of the input units and on the level of the output noise. We show analytically, and confirm numerica...
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ABSTRACT Most of the core memory operations carried out by the hippocampus may be implemented in the standard cortical circuitry of its CA3 network, largely conserved from pre-mammalian times. We propose that the new mammalian DG-CA3... more
ABSTRACT Most of the core memory operations carried out by the hippocampus may be implemented in the standard cortical circuitry of its CA3 network, largely conserved from pre-mammalian times. We propose that the new mammalian DG-CA3 circuitry has evolved in order to facilitate one particular process: the formation of novel memories, uncorrelated from those already stored on CA3 recurrent connections. Such teaching aid is shown here to be effective not only in producing discrete memory states, but also for establishing quasi-continuous spatial charts.
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... fully differentiated class out of the third-to-last of the great extinctions, in the ... In the hippocampus, however, it appears that mammals have devised a more refined expedient ... by McNaughton and Morris, however, an increasing... more
... fully differentiated class out of the third-to-last of the great extinctions, in the ... In the hippocampus, however, it appears that mammals have devised a more refined expedient ... by McNaughton and Morris, however, an increasing number of other investigators rediscovered the young ...