Lifeboat Foundation

Most models for the overall shape and geometry of the Universe—including some exotic ones—are compatible with the latest cosmic observations.

Is the Universe simply connected like a sphere or does it contain holes like a doughnut or a more complicated structure? The topology of the Universe—that is, its overall geometry—remains far from settled, according to a collaboration of cosmologists. Despite past claims that observations of the cosmic microwave background (CMB) rule out various topologies, the researchers contend that many of these shapes, including some strange ones, have not been contradicted by the evidence [1].

The overall geometry of the Universe is thought to have been determined by quantum processes that unfolded in the initial moment of the big bang. Identifying the topology of the Universe would provide researchers with an important clue as to the nature of those quantum processes and could help them sift through the many proposed theories of the early Universe.

Varying the parameters of weight distribution did not account for the observed amount of HD information conveyed by PoSub-FS cells (Fig. 2a). Rather, we found that the number of inputs received by each output unit was a key factor influencing the amount of HD information (Extended Data Fig. 5e). Varying both weight distribution and the number of input units, we obtained a distribution of HD information in output tuning curves that matched the real data (Extended Data Fig. 5f), revealing that the tuning of PoSub-FS cells can be used to estimate both the distribution of weights and the number of input neurons. Notably, under optimal network conditions, Isomap projection of output tuning curve auto-correlograms has a similar geometry to that of real PoSub-FS cells (Extended Data Fig. 5g), confirming similar distribution of tuning shapes.

To further quantify the relative contributions of ADN and local PoSub inputs to PoSub-FS cell tuning, we expanded the simulation to include the following two inputs: one with tuning curve widths corresponding to ADN-HD cells and one with tuning curve widths corresponding to PoSub-HD cells (Fig. 4h, left). We then trained the model using gradient descent to find the variances and means of input weights that result in the best fit between the simulated output and real data. The combination of parameters that best described the real data resulted in ADN inputs distributed in a near Gaussian-like manner but a heavy-tailed distribution of PoSub-HD inputs (Fig. 4h, middle). Using these distribution parameters, we performed simulations to determine the contribution of ADN-HD and PoSub-HD inputs to the output tuning curves and established that PoSub-FS cell-like outputs are best explained by flat, high firing rate inputs from ADN-HD cells and low firing rate, HD-modulated inputs from PoSub-HD cells (Fig. 4h, right).

Our simulations, complemented by direct analytical derivation (detailed in the Supplementary Methods), not only support the hypothesis that the symmetries observed in PoSub-FS cell tuning curves originate from local cortical circuits but also demonstrate that these symmetries emerge from strongly skewed distributions of synaptic weights.

A study investigates subcellular, single-cell and network-level comunication within the astrocyte network in response to the two major neurotransmitter inputs.

Hollunder et al. identify networks where deep brain stimulation reduces symptoms for Parkinson’s disease, Tourette’s syndrome, dystonia and obsessive-compulsive disorder. This revealed a fronto-rostral topography that segregates the frontal cortex.

The changes in engram composition and selectivity observed in our model were associated with ongoing synaptic plasticity during memory consolidation (Fig. 1l). Feedforward synapses from training stimulus neurons (that is, sensory engram cells; Methods) onto hippocampal engram cells were strengthened over the course of memory consolidation, and, consequently, the synaptic coupling between the stimulus population and the hippocampus network was increased. Recurrent excitatory synapses between engram cells also experienced a modest gain in synaptic efficacy. Notably, inhibitory synapses from inhibitory engram cells onto both engram and non-engram cells were strongly potentiated throughout memory consolidation. This indicated that a number of training-activated engram cells were forced out of the engram due to strong inhibition, and, consequently, only neurons highly responsive to the training stimulus remained in the engram, in line with our previous analysis (Fig. 1e). Inhibitory neurons also controlled the overall activity of excitatory neurons in the network through inhibitory synaptic plasticity (Extended Data Fig. 2h).

To investigate the contribution of synaptic plasticity to the engram dynamics in our model, we performed several manipulations in our simulations. First, we blocked the reactivation of the training stimulus during memory consolidation and found that this altered the temporal profile of engrams and prevented them from becoming selective (Extended Data Fig. 3a–i). These effects were associated with reduced potentiation of inhibitory synapses onto engram cells (compare Extended Data Fig. 3i to Fig. 1l, bottom rows). Previous experiments demonstrated that sleep-specific inhibition of learning-activated sensory neurons disrupts memory selectivity¹¹, and, hence, our model was consistent with these findings, and it predicted underlying mechanisms. Second, blocking long-term potentiation (LTP) during memory consolidation almost completely eliminated engram cell turnover after a steady state was reached, and it also impaired memory recall relative to the control case (Extended Data Fig. 4a–i). Reduced feedforward and recurrent excitatory synaptic weights due to LTP blockage led to engram stabilization and impaired recall (compare Extended Data Fig. 4h to Fig. 1l, top and middle rows). These results are in line with a recent study showing that memory recall is impaired when LTP is optically erased selectively during sleep¹⁴. Third, we separately blocked the Hebbian and non-Hebbian forms of long-term excitatory synaptic plasticity in our model and verified that each was essential for memory encoding and consolidation (Extended Data Fig. 5). These results are consistent with a previously reported mean-field analysis showing that this combination of plasticity mechanisms can support stable memory formation and recall⁹. Fourth, we blocked inhibitory synaptic plasticity in our entire simulation protocol, and this disrupted the emergence of memory selectivity in our network model (Extended Data Fig. 6a–h). This demonstrated that excitatory synaptic plasticity alone could not drive an increase in memory selectivity because it could not increase competition among excitatory neurons in the absence of inhibitory synaptic plasticity (compare Extended Data Fig. 6h to Fig. 1l). However, excitatory synaptic plasticity could promote engram cell turnover on its own in an even more pronounced manner than in the presence of both excitatory and inhibitory synaptic plasticity (compare Extended Data Fig. 6b to Fig. 1g). Finally, we found that an alternative inhibitory synaptic plasticity formulation yielded engram dynamics analogous to those in our original network (compare Extended Data Fig. 7a–h to Fig. 1e–l). This suggested that the dynamic and selective engrams predicted by our model are not a product of a specific form of inhibitory plasticity but a consequence of memory encoding and consolidation in inhibition-stabilized plastic networks in general.

We also conduced loss-of-function and gain-of-function manipulations to examine the role of training-activated engram cells in memory recall in our model (Fig. 2). We found that blocking training-activated engram cells after a consolidation period of 24 h prevented memory recall (Fig. 2a), whereas artificially reactivating them in the absence of retrieval cues was able to elicit recall (Fig. 2b), in a manner consistent with previous experimental findings^3,4 and despite the dynamic nature of engrams in our simulations (Fig. 1e–g). Thus, our model was able to reconcile the prominent role of training-activated engram cells in memory storage and retrieval with dynamic memory engrams. To determine whether neuronal activity during memory acquisition was predictive of neurons dropping out of or dropping into the engram, we examined the distribution of stimulus-evoked neuronal firing rates in the training phase (Extended Data Fig. 3j–m). We found that training-activated engram cells that remained part of the engram throughout memory consolidation exhibited higher stimulus-evoked firing rates than the remaining neurons in the network (Extended Data Fig. 3j) and training-activated engram cells that dropped out of the engram over the course of consolidation (Extended Data Fig. 3k). Therefore, stimulus-evoked firing rates during training were indicative of a neuron’s ability to outlast inhibition and remain part of the engram after initial memory encoding. We also verified that neurons that were not engram cells at the end of training but later dropped into the engram displayed lower training stimulus-evoked firing rates than the remaining neurons in the network (Extended Data Fig. 3l). Surprisingly, neurons that dropped into the engram after training showed slightly lower stimulus-evoked firing rates than neurons that failed to become part of the engram altogether (Extended Data Fig. 3m). This suggested that stimulus-evoked firing rates during memory acquisition may not be reliable predictors of a neuron’s ability to increase its response to the training stimulus and become an engram cell after encoding. Lastly, we found that using a neuronal population-based approach to identify engram cells in our simulations yielded analogous engram dynamics (compare Extended Data Fig. 4j–o to Fig. 1e−j and Extended Data Fig. 2i–n to Extended Data Fig. 2b−g; Methods).

A collaborative research team from NIMS and Tokyo University of Science has successfully developed an artificial intelligence (AI) device that executes brain-like information processing through few-molecule reservoir computing. This innovation utilizes the molecular vibrations of a select number of organic molecules.

By applying this device for the blood glucose level prediction in patients with diabetes, it has significantly outperformed existing AI devices in terms of prediction accuracy.

The work is published in the journal Science Advances.

According to a big German study, those in middle or older age today have an elevated idea of “old” compared to previous generations.” This mirrors increases in life expectancy, especially for the better-off half of the population in rich countries.

Research is finding ways to extend animal lifespans but regulators are still wary of treating ageing as a disease.

Robotics is really about ‘embodied AI’, says the CEO of Sanctuary AI, whose vision of robots’ future is now close to the one predicted by sci-fi. Meanwhile: the real transformers are… us.

Telcos are a prime example of an enterprise that would benefit from adopting this hybrid AI model. They have a unique role, as they can be both consumers and providers. Similar scenarios may be applicable to healthcare, oil rigs, logistics companies and other industries. Are the telcos prepared to make good use of gen AI? We know they have a lot of data, but do they have a time-series model that fits the data?

When it comes to AI models, IBM has a multimodel strategy to accommodate each unique use case. Bigger is not always better, as specialized models outperform general-purpose models with lower infrastructure requirements.