use https for features.
text: sort by
tags: modified
type: chronology
[0] Mehta MR, Cortico-hippocampal interaction during up-down states and memory consolidation.Nat Neurosci 10:1, 13-5 (2007 Jan)[1] Ji D, Wilson MA, Coordinated memory replay in the visual cortex and hippocampus during sleep.Nat Neurosci 10:1, 100-7 (2007 Jan)

[0] Ji D, Wilson MA, Coordinated memory replay in the visual cortex and hippocampus during sleep.Nat Neurosci 10:1, 100-7 (2007 Jan)

[0] Káli S, Dayan P, Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions.Nat Neurosci 7:3, 286-94 (2004 Mar)

[0] Foster DJ, Wilson MA, Reverse replay of behavioural sequences in hippocampal place cells during the awake state.Nature 440:7084, 680-3 (2006 Mar 30)

[0] Rózsa B, Katona G, Kaszás A, Szipöcs R, Vizi ES, Dendritic nicotinic receptors modulate backpropagating action potentials and long-term plasticity of interneurons.Eur J Neurosci 27:2, 364-77 (2008 Jan)

[0] Pastalkova E, Itskov V, Amarasingham A, Buzsáki G, Internally generated cell assembly sequences in the rat hippocampus.Science 321:5894, 1322-7 (2008 Sep 5)

hide / / print
ref: -2020 tags: replay hippocampus variational autoencoder date: 10-11-2020 04:09 gmt revision:1 [0] [head]

Brain-inspired replay for continual learning with artificial neural networks

  • Gudo M van de Ven, Hava Siegelmann, Andreas Tolias
  • In the real world, samples are not replayed in shuffled order -- they occur in a sequence, typically few times. Hence, for training an ANN (or NN?), you need to 'replay' samples.
    • Perhaps, to get at hidden structure not obvious on first pass through the sequence.
    • In the brain, reactivation / replay likely to stabilize memories.
      • Strong evidence that this occurs through sharp-wave ripples (or the underlying activity associated with this).
  • Replay is also used to combat a common problem in training ANNs - catastrophic forgetting.
    • Generally you just re-sample from your database (easy), though in real-time applications, this is not possible.
      • It might also take a lot of memory (though that is cheap these days) or violate privacy (though again who cares about that)

  • They study two different classification problems:
    • Task incremental learning (Task-IL)
      • Agent has to serially learn distinct tasks
      • OK for Atari, doesn't make sense for classification
    • Class incremental learning (Class-IL)
      • Agent has to learn one task incrementally, one/few classes at a time.
      • Like learning a 2 digits at a time in MNIST
        • But is tested on all digits shown so far.
  • Solved via Generative Replay (GR, ~2017)
  • Use a recursive formulation: 'old' generative model is used to generate samples, which are then classified and fed, interleaved with the new samples, to the new network being trained.
    • 'Old' samples can be infrequent -- it's easier to reinforce an existing memory rather than create a new one.
    • Generative model is a VAE.
  • Compared with some existing solutions to catastrophic forgetting:
    • Methods to protect parameters in the network important for previous tasks
      • Elastic weight consolidation (EWC)
      • Synaptic intelligence (SI)
        • Both methods maintain estimates of how influential parameters were for previous tasks, and penalize changes accordingly.
        • "metaplasticity"
        • Synaptic intelligence: measure the loss change relative to the individual weights.
        • δL=δLδθδθδtδt \delta L = \int \frac{\delta L}{\delta \theta} \frac{\delta \theta}{\delta t} \delta t ; converted into discrete time / SGD: L=Σ kω k=ΣδLδθδθδtδt L = \Sigma_k \omega_k = \Sigma \int \frac{\delta L}{\delta \theta} \frac{\delta \theta}{\delta t} \delta t
        • ω k\omega_k are then the weightings for how much parameter change contributed to the training improvement.
        • Use this as a per-parameter regularization strength, scaled by one over the square of 'how far it moved'.
        • This is added to the loss, so that the network is penalized for moving important weights.
    • Context-dependent gating (XdG)
      • To reduce interference between tasks, a random subset of neurons is gated off (inhibition), depending on the task.
    • Learning without forgetting (LwF)
      • Method replays current task input after labeling them (incorrectly?) using the model trained on the previous tasks.
  • Generative replay works on Class-IL!
  • And is robust -- not to many samples or hidden units needed (for MNIST)

  • Yet the generative replay system does not scale to CIFAR or permuted MNIST.
  • E.g. if you take the MNIST pixels, permute them based on a 'task', and ask a network to still learn the character identities , it can't do it ... though synaptic intelligence can.
  • Their solution is to make 'brain-inspired' modifications to the network:
    • RtF, Replay-though-feedback: the classifier and generator network are fused. Latent vector is the hippocampus. Cortex is the VAE / classifier.
    • Con, Conditional replay: normal prior for the VAE is replaced with multivariate class-conditional Gaussian.
      • Not sure how they sample from this, check the methods.
    • Gat, Gating based on internal context.
      • Gating is only applied to the feedback layers, since for classification ... you don't a priori know the class!
    • Int, Internal replay. This is maybe the most interesting: rather than generating pixels, feedback generates hidden layer activations.
      • First layer of a network is convolutional, dependent on visual feature statistics, and should not change much.
        • Indeed, for CIFAR, they use pre-trained layers.
      • Internal replay proved to be very important!
    • Dist, Soft target labeling of the generated targets; cross-entropy loss when training the classifier on generated samples. Aka distillation.
  • Results suggest that regularization / metaplasticity (keeping memories in parameter space) and replay (keeping memories in function space) are complementary strategies,
    • And that the brain uses both to create and protect memories.

  • When I first read this paper, it came across as a great story -- well thought out, well explained, a good level of detail, and sufficiently supported by data / lesioning experiments.
  • However, looking at the first authors pub record, it seems that he's been at this for >2-3 years ... things take time to do & publish.
  • Folding in of the VAE is satisfying -- taking one function approximator and use it to provide memory for another function approximator.
  • Also satisfying are the neurological inspirations -- and that full feedback to the pixel level was not required!
    • Maybe the hippocampus does work like this, providing high-level feature vectors to the cortex.
    • And it's likely that the cortex has some features of a VAE, e.g. able to perceive and imagine through the same nodes, just run in different directions.
      • The fact that both concepts led to an engineering solution is icing on the cake!

hide / / print
ref: -2017 tags: calcium imaging seeded iterative demixing light field microscopy mouse cortex hippocampus date: 02-13-2019 22:44 gmt revision:1 [0] [head]

PMID-28650477 Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy

  • Tobias Nöbauer, Oliver Skocek, Alejandro J Pernía-Andrade, Lukas Weilguny, Francisca Martínez Traub, Maxim I Molodtsov & Alipasha Vaziri
  • Cell-scale imaging at video rates of hundreds of GCaMP6 labeled neurons with light-field imaging followed by computationally-efficient deconvolution and iterative demixing based on non-negative factorization in space and time.
  • Utilized a hybrid light-field and 2p microscope, but didn't use the latter to inform the SID algorithm.
  • Algorithm:
    • Remove motion artifacts
    • Time iteration:
      • Compute the standard deviation versus time (subtract mean over time, measure standard deviance)
      • Deconvolve standard deviation image using Richardson-Lucy algo, with non-negativity, sparsity constraints, and a simulated PSF.
      • Yields hotspots of activity, putative neurons.
      • These neuron lcoations are convolved with the PSF, thereby estimating its ballistic image on the LFM.
      • This is converted to a binary mask of pixels which contribute information to the activity of a given neuron, a 'footprint'
        • Form a matrix of these footprints, p * n, S 0S_0 (p pixels, n neurons)
      • Also get the corresponding image data YY , p * t, (t time)
      • Solve: minimize over T ||YST|| 2|| Y - ST||_2 subject to T0T \geq 0
        • That is, find a non-negative matrix of temporal components TT which predicts data YY from masks SS .
    • Space iteration:
      • Start with the masks again, SS , find all sets O kO^k of spatially overlapping components s is_i (e.g. where footprints overlap)
      • Extract the corresponding data columns t it_i of T (from temporal step above) from O kO^k to yield T kT^k . Each column corresponds to temporal data corresponding to the spatial overlap sets. (additively?)
      • Also get the data matrix Y kY^k that is image data in the overlapping regions in the same way.
      • Minimize over S kS^k ||Y kS kT k|| 2|| Y^k - S^k T^k||_2
      • Subject to S k>=0S^k >= 0
        • That is, solve over the footprints S kS^k to best predict the data from the corresponding temporal components T kT^k .
        • They also impose spatial constraints on this non-negative least squares problem (not explained).
    • This process repeats.
    • allegedly 1000x better than existing deconvolution / blind source segmentation algorithms, such as those used in CaImAn

hide / / print
ref: -2019 tags: mosers hippocampus popsci nautilus grid cells date: 02-12-2019 07:32 gmt revision:1 [0] [head]

New Evidence for the Strange Geometry of Thought

  • Wow. Things are organized in 2d structures in the brain. The surprising thing about this article is that only the hiippocampus is mentioned, no discussion of the cortex. Well, it was written by a second year graduate student (though, admittedly, the writing style is perfectly fine.)

hide / / print
ref: -0 tags: STDP dopamine hippocampus date: 01-16-2019 21:56 gmt revision:1 [0] [head]

PMID-26516682 Retroactive modulation of spike timing-dependent plasticity by dopamine.

  • Here we show that dopamine, a positive reinforcement signal, can retroactively convert hippocampal timing-dependent synaptic depression into potentiation.
  • This effect requires functional NMDA receptors and is mediated in part through the activation of the cAMP/PKA cascade.
  • Mouse horizontal slices.
  • Plasticity induced by 100 pairings of a single EPSP followed by a postsynaptic spike (heavy-handed?)
  • Pre-before-post @ 10ms -> LTP
  • Post-before-pre @ -20ms -> LTD
  • Post-before-pre @ -10ms -> LTP (?!)
    • Addition of Dopamine antagonist (D2: sulpiride, D1/D5: SCH23390) prevented LTP and resulted in LTD.
  • Post-before-pre @ -20ms -> LTP in the presence of 20 uM DA.
    • The presence of DA during coordinated spiking activity widense the timing interval for induction of LTP.
  • What about if it's applied afterward?
  • 20 uM DA applied 1 minute (for 10-12 minutes) after LTD induction @ -20 mS converted LTD into LTP.
    • This was corrected by addition of the DA agonists.
    • Did not work if DA was applied 10 or 30 minutes after the LTD induction.
  • Others have shown that this requires functional NMDA receptors.
    • Application of NMDA agonist D-AP5 after post-before-pre -20ms did not affect LTD.
    • Application of D-AP5 before DA partially blocked conversion of LTD to LTP.
    • Application of D-AP5 alone before induction did not affect LTD.
  • This is dependent on the cAMP/PKA signaling cascade:
    • Application of forskolin (andenylyl cyclase AC activator) converts LTD -> LTP.
    • Dependent on NMDA.
  • PKA inhibitor H-89 alsoblocked LTD -> P.

hide / / print
ref: -0 tags: buszaki watson oscillations review gamma theta hippocampus cortex date: 09-30-2013 18:32 gmt revision:2 [1] [0] [head]

PMID-23393413 Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease.

  • His frequency band standards:
    • delta: 1.5 - 4Hz
    • theta: 4 - 10Hz
    • beta: 10 - 30 Hz
    • gamma: 30 - 80Hz
    • fast: 80 - 200 Hz
    • ultra fast: 200 - 600 Hz.
  • comodugram: power-power correlelogram
  • Reviews current understanding of important rhythms:
    • How gamma is preserved amongs mammals, owing to the same fundamental mechanisms (membrane time constant, GABA transmission, AMPA receptior latency) all around 25ms; suggests that this is a means of tieing neurons into meaningful groups. or symbols; (solves the binding problem?)
    • Theta rhythm, in comparison, varies between species, inversely based on the size of the hippocampus. Larger hippocampus -> greater axonal delay.
    • These and other the critical step is to break neurons into symbols (as part of a 'language' or sequenced computation), not arbitrarily long trains of spikes which are arbitrarily difficult to parse.
  • Reviews the potential role of oscillations in active sensing, though with a rather conjectorial voice: suggests that sensory systems
  • Suggests that neocortical slow-wave oscillations during sleep are critical for transferring information from the hippocampus to the cortex: the cortex become excitable at particular phases of SWS, which biases the fast ripples from the hippocampus. During wakefulness, the direction is reversed -- the hippocampus 'requests' information from the neocortex by gating gamma with theta rhythms.
  • "Typically, when oscillators of different freqencies are coupled, the network oscillation frequency is determined by the fastest one. (??)
  • I actually find figure 3 to be rather weak -- the couplings are not that obvious, espeically if this is the cherry-picked example.
  • Cross phasing-coupling, or n:m coupling: one observes m events associated with the “driven” cycle of one frequency occurring at n different times or phases in the “stimulus” cycle of the other.
    • The mechanism of cross-frequency coupling may for the backbone of neural syntax, which allows for both segmentation and linking of cell assemblies into assemblies (leters) and sequences (words). Hmm. this seems like a stretch, but I am ever cautious.
  • Brain oscillations for quantifiable phenotypes! e.g. you can mono-zygotic twins apart from di-zygotic twins.

hide / / print
ref: -0 tags: hippocampus theta oscillations memory date: 03-18-2012 18:09 gmt revision:0 [head]

PMID-21696996 The hippocampus: hub of brain network communication for memory

  • Their hypothesis: memory encoding is dominated by theta oscillations 6-10 Hz; during inactivity, hippocampal neurons burst synchronously, creating sharp waves, theoretically supporting memory consolidation.
  • (They claim): to date there is no generally accepted theoretical view on memory consolidation.
  • Generally it seems to shift from hippocampus to neocortex, but still, evidence is equivocal. (Other than HM & other human evidence?)
  • Posit a theory based on excitation ramps of reverse-replay, which seems a bit fishy to me (figure 3).
  • Didn't know this: replay in visual and PFC can be so precise that it preserves detailed features of the crosscorrelograms between neurons. [58, 65, 81].

hide / / print
ref: -0 tags: hippocampus theta oscillations date: 03-18-2012 17:34 gmt revision:2 [1] [0] [head]

PMID-11832222 Theta Oscillations in the Hippocampus

  • Theta-alpha oscillations have been found in 'all mammals to-date, including humans. (Hence conserved, hence possibly essential).
    • Prevalent in REM sleep.
    • Present in slices bathed in carbachol, too.
    • As well as locomotor activities; but not usually when the animal is resting.
  • Other reviews: Bland 1986, VAnderwolf 1988, Lopes da Silva et al 1990, Buzaki et al 1994 Stewart and Fox 1990, Vinogradova 1995, Vertex and Kocsis 1997.
    • Modeling reviews used passive cable properties; actually, it seems neurons, and their dendrites are have active conductances & active oscillatory features.
  • Theta oscillations most strongly present in CA1
  • Along similar lamina, oscillations are similar.
  • Osc. visible in cortical structures ...
    • subicular complex, entorhinal cortex, perirhinal cortex, cingulate cortex, amygdala -- though none of these structures are capable of generating theta oscillations intrinsically.
  • Also apparent in subcortical structures,
    • Dorsal raphe nucleus, ventral tegmental nucleus, and anterior thalamic nuclei. None of these seem required for oscillation, however:
  • Oscillations may emanate from the medial-septum-diagonal band of Broca (MS-DBB); lesion inactivates theta oscillations in all cortical areas, but the relative role is uncertain, as MS-DBB oscillations may require hippocampal and entorhinal afferents.
    • EPSPs brought about by the MS-DBB cholinergic neurons on hippocampal pyramidal cells cannot be responsible for the atrophine-sensitive form of theta.
    • That said, even though atrophine treatment only modestrly affects theta, it is reduced several-fold after selective neurotoxin elminiation of MS-DBB cholinergic cells -- maybe it's nicotinic synapses?
  • Drugs:
    • Theta can be blocked by GABA antagonist (picrotoxin, induces epilepsy) or agonist (pentoparbital anesthesia).
    • Many other drugs affect oscillations.
    • Broken down into atrophine-sensitive and atrophine-resistant oscillations.
      • (Atrophine blocks muscarinic Ach receptors).
    • Amplitude and frequency of theta does not appreciably change even after large doses of systematic muscarinic blockers.
      • Same drugs abolish theta under anesthesia.
    • The neurotransmitter and receptor causative in theta have never been clearly determined.
  • Theta in CA3 is much smaller than in CA3:
    • Distal dendritic arbor of CA3 pyramidal cells is considerably smaller than that of CA1 pyramidal neurons.
    • CA3 pyramidal neurons receive perisomatic exitation near their somata from the large mossy terminals of granule cells.
      • Regarding this, size of mossy fiber projection correlates well with spatial ability in mice, possibly causative. link (note: used the dryland radial maze, more appropriate for non-swimming mice!)
    • Intrahippocampal oscillator (CA3?) can change its frequency and phase relatively independently from the extrahippocampal (entorhinal) theta inputs.
  • CA1 interneurons discharge on the descenting phase of theta in the pyramidal cell layer, and are assumed to be responsible for the increased gamma of this phase.
  • CA1 pyramidal cells discharge on the negative phase (makes sense) of theta as recorded from the CA1 pyramidal cell layer.
    • Phase fluctuation of spikesis not random and correlates with behavioral varaibles.
      • Stronger excitation = more spikes earlier in the theta negative phase.
    • Firing of place cells varies systematically with animal position and theta phase -- there is a phase precession.
      • Seems as though place is encoded in both which cell is firing as well as when in theta.
      • alternately, this may be an effect of the CA3 oscillator running slightly faster than the extrinsic oscillator.

Original model for theta oscillation creation (figure 2):

  • Note that all oscillations require a dipole which periodically inverts along it's axis, as is required in a conductive solution.
    • And yet there is no 'null' zone in theta oscillation, as dipole would imply. Rather, there is a gradual shift, more like a traveling wave.
  • Dendrites are passive cables, LFP generated by summed activity of IPSP and EPSP on soma and dendrites.
    • Excitation from perforant path,
    • Inhibition from septum to feed-forward inhibitory neuron inputs.
  • That said, the model is not completely consistent with experimental evidence:
    • The highest probability of discharge in the behaving rat occurs around the positive peak of theta recorded at the level of the distal dendrites, corresponding to the negative phase in the pyramidal level. (Remember, spiking corresponds to sodium influx, hence decreased extracellular +)
    • Cells may oscillate by themselves, without input.
    • The cell connections within the hippocampus matter a lot, too.


  • Induction is present / optimal when the spacing between pulses is 200ms.
    • Priming can be only one pulse!
    • Not clear how this works - endogenous cannabanoids?
  • Theta oscillation may provide a mechanism for bringing together time afferent inducing depolarization and dendritic invasion of fast spikes.


  • A theta cycle may be considered an information quantum, allowing the exchange of information among the linked members in a phase-locked manner. ...
  • This discontinuous mode of operation may be a unique solution to temporally segregate and link neuronal assemblies to perform various operations.
  • Notable support of this hypothesis:
    • Theta cycle phase resets upon sensory stimulation
    • Motor activity can become theta locked.


  • Ketamine blocks NMDA receptors.
  • Granule cells can be eliminated by neonatal X-ray exposure. (why?)

hide / / print
ref: Wilson-1993.08 tags: Wilson McNaughton 1993 sleep hippocampus array recording date: 01-03-2012 00:57 gmt revision:2 [1] [0] [head]

PMID-8351520[0] Dynamics of the hippocampal ensemble code for space.

  • 73-148 neurons.
  • Could accurately decode the rat's movement through space.
  • "The parallel recording methods outlined here make possible the study of the dynamics of neuronal interactions during unique behavioral events."

PMID-8036517[1] Reactivation of hippocampal ensemble memories during sleep.

  • "Information acquired during active behavior is thus re-expressed in hippocampal circuits during sleep, as postulated by some theories of memory consolidation."


[0] Wilson MA, McNaughton BL, Dynamics of the hippocampal ensemble code for space.Science 261:5124, 1055-8 (1993 Aug 20)
[1] Wilson MA, McNaughton BL, Reactivation of hippocampal ensemble memories during sleep.Science 265:5172, 676-9 (1994 Jul 29)

hide / / print
ref: BuzsAki-1996.04 tags: hippocampus neocortex theta gamma consolidation sleep Buzsaki review learning memory date: 12-07-2011 02:31 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-8670641[0] The hippocampo-neocortical dialogue.

  • the entorhinal ctx is bidirectionally conneted to nearly all areas of the neocortical mantle.
  • Buzsaki correctly predicts that information gathered during exploration is played back at a faster scale during synchronous population busts during (comnsummatory) behaviors.
  • looks like a good review of the hippocampus, but don't have time to read it now.
  • excellent explanation of the anatomy (with some omissions, click through to read the caption):
  • SPW = sharp waves, 40-120ms in duration. caused by synchronous firing in much of the cortex ; occur 0.02 - 3 times/sec in daily activity & during slow wave sleep.
    • BUzsaki thinks that this may be related to memory consolidation.
  • check the cited-by articles : http://cercor.oxfordjournals.org/cgi/content/abstract/6/2/8
[0] Buzsaiki G, The hippocampo-neocortical dialogue.Cereb Cortex 6:2, 81-92 (1996 Mar-Apr)

hide / / print
ref: MAPlle-2009.03 tags: sleep spindles learning ripples LFP hippocampus neocortex synchrony SWS REM date: 03-25-2009 15:05 gmt revision:2 [1] [0] [head]

PMID-19245368[0] The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats

  • Here we examined whether slow oscillations also group learning-induced increases in spindle and ripple activity, thereby providing time-frames of facilitated hippocampus-to-neocortical information transfer underlying the conversion of temporary into long-term memories.
  • No apparent grouping effect between slow oscillations and learning-induced spindles and ripples in rats.
  • Stronger effect of learning on spindles (neocortex) and ripples (hippocampus) ; less or little effect of learning on slow waves in the neocortex.
  • have a good plot showing their time-series analysis:


[0] Mölle M, Eschenko O, Gais S, Sara SJ, Born J, The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats.Eur J Neurosci 29:5, 1071-81 (2009 Mar)

hide / / print
ref: Mehta-2007.01 tags: hippocampus visual cortex wilson replay sleep learning states date: 03-09-2009 18:53 gmt revision:1 [0] [head]

PMID-17189946[0] Cortico-hippocampal interaction during up-down states and memory consolidation.

  • (from the associated review) Good pictorial description of how the hippocampus may impinge order upon the cortex:
    • During sleep the cortex is spontaneously and randomly active. Hippocampal activity is similarly disorganized.
    • During waking, the mouse/rat moves about in the environment, activating a sequence of place cells. The weights of the associated place cells are modified to reflect this sequence.
    • When the rat falls back to sleep, the hippocampus is still not random, and replays a compressed copy of the day's events to the cortex, which can then (and with other help, eg. ACh), learn/consolidate it.
  • see [1].


hide / / print
ref: Ji-2007.01 tags: hippocampus visual cortex wilson replay sleep date: 03-09-2009 18:48 gmt revision:3 [2] [1] [0] [head]

PMID-17173043[0] Coordinated memory replay in the visual cortex and hippocampus during sleep.

  • EEG from Layer 5 of the visual cortex.
  • used tetrodes.
  • rats were trained to alternate loops in a figure-8 maze to get at food.
  • the walls of the maze were lined with high-contrast cues.
  • data for correlated activity between ctx and hippocampus weak - they just show that the frame ('up' period in cellular activity) start & end between the two regions are correlated. No surprise - they are in the same brain after all!
  • Found that cells in the deep visual cortex (V1 & V2) had localized firing fields. Rat vision is geared for navigation? (mostly?)
  • From this, they could show offline replay of the same sequence; these offline sequences were compressed by about 5-10.
    • shuffle tests on the replayed frames look pretty good - respectable degree of significance here.
    • Aside: possibly some of the noise of the recordings is reflective not of the noise of the system, but the noise / high dimensionality of the sensory input driving the visual ctx.
  • Also found some visual and some hippocampal cells that replayed sequences simultaneously; shuffle test here looks ok too.
  • picture from associated review, {692}


hide / / print
ref: KAli-2004.03 tags: hippocampus memory model Dayan replay learning memory date: 03-06-2009 17:53 gmt revision:1 [0] [head]

PMID-14983183[0] Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions

  • (i'm skimming the article)
  • The neocortex acts as a probabilistic generative model. unsupervised learning extracts categories, tendencies and correlations from the statistics of the inputs into the [synaptic weights].
  • Their hypothesis is that hippocampal replay is required for maintenance of episodic memories; their model and simulations support this.
  • quote: "However, the computational goal of episodic learning is storing individual events rather than discovering statistical structure, seemingly rendering consolidation inappropriate. If initial hippocampal storage of the episode already ensures that it can later be recalled episodically, then, barring practical advantages such as storage capacity (or perhaps efficiency), there seems little point in duplicating this capacity in neocortex." makes sense!


hide / / print
ref: Foster-2006.03 tags: hippocampus memory place cells reverse replay Wilson date: 03-06-2009 17:53 gmt revision:1 [0] [head]

PMID-16474382[0] Reverse replay of behavioral sequences in hippocampal place cells during the awake state.

  • wow: they show compressed reverse replay of firing sequences of hippocampal place cells during movement. While the rat is awake, too!
  • recorded up to 128 cells from the rat hippocampus; 4 animals.
  • the replay occurred while the rat was stopped, and lasted a few hundred milliseconds (~300).
  • phenomena appears to be very common, at least for the rats on the novel tracks.
  • replay events were coincident with ripples in the hippocampal EEG, which also occurs during sleep.
    • however, during slow-wave sleep, the replay was forward.
  • they offer a reasonable hypothesis for the reverse replay's function: it is used to propagate value information from the rewarded lcoation backwards along incoming (behavioral) trajectories.
    • quote "awake replay represents efficient use of hard-won experience."


hide / / print
ref: RAzsa-2008.01 tags: nAChR nicotinic acetylchoine receptor interneurons backpropagating LTP hippocampus date: 10-08-2008 17:37 gmt revision:0 [head]

PMID-18215234[0] Dendritic nicotinic receptors modulate backpropagating action potentials and long-term plasticity of interneurons.

  • idea: nAChRs are highly permeable to Ca+2, LTP is dependent on Ca2+, so they tested nAChR -> LTP in interneurons of rat hippocampus using whole-cell electrophysiology and 2-photon imaging.
  • Here we show that precisely timed activation of dendritic α7-nAChRs boosts the induction of LTP by excitatory postsynaptic potentials (EPSPs) and synaptically triggered dendritic Ca2+ transients.
  • suggest that this rapid (ionotropic) method of memory encoding and retrieval via LTP/D facilitated by acetylcholine.
  • I haven't read much of the article, since it is much out of my field of experience.


hide / / print
ref: Pastalkova-2008.09 tags: hippocampus Buzsaki sequences date: 09-22-2008 21:25 gmt revision:1 [0] [head]

PMID-18772431[0] Internally generated cell assembly sequences in the rat hippocampus.

  • The task was unique: the rats had to run in a wheel for 10-20 seconds before choosing the left or right arms of a figure-8 maze. The rats were rewarded with water if they alternated arm choice.
  • Looked at the activity of pyramical cells - many of them place cells as well as episode-cells - in the hippocampus, and found that the pattern of firing per neuron was predictable and predictive or which choice the rat would take after running in the wheel.
  • The same pattern of hippocampal firing was not found in a control running task (one that did not require a choice).
  • The pattern of firing was phase locked to the theta oscillations in the hippocampus; this phase relationship gradually advanced during the course of trials.
  • During the wheel running, there seemed to be a series of delayed firing bursts by the hippocampal neurons.