A Shared Vision for Machine Learning in Neuroscience

August 30, 2022


There is a lot of excitement about machine learning (ML). After all, it has enabled computers to outperform people in picture classification, defeat humans in complicated games like "Go" and give high-quality voice-to-text on popular mobile phones. The scientific community is taking notice of advances in machine learning.

The discipline of neurology is no different. Many opinion pieces have been written in recent years regarding the relevance of machine learning in neuroscience. Furthermore, when we look at the number of new findings concerning ML in neuroscience over the previous 20 years, we see that its application has been steadily increasing. Within this discipline, machine learning has been employed in a variety of ways. 

We will catalog the uses of ML in neuroscience as well as the shared goal for ML in neuroscience in this blog.

Objectives of Machine Learning

Humans can observe and experience the environment because of the eyes. Humans make judgments depending on what they observe with their eyes. Humans act on their ideas depending on what they perceive with their eyes. Similarly, in order for a Machine learning model to emulate human intellect, it must have eyes that can capture pictures or have data as input and make decisions based on them. 

Hence, the main objective of machine learning is to uncover patterns in user data and then generate predictions based on these complicated patterns to answer real-world queries and solve issues. 

Objectives of Neuroscience

Neuroscience is the study of how the brain performs diverse perceptual, cognitive, and motor processes. 

There are many objectives of Neuroscience, few of them are

  • To use the scientific method to construct feasible independent research projects by integrating content, skills, and critical thinking. 
  • To gain a wide awareness of the nervous system's structure and function, as well as the depth of knowledge in cellular/molecular or behavioral/cognitive perspectives. 
  • To get a better grasp of the ethical problems surrounding the use of human subjects and animal subjects in neuroscience research.

And since the advent of new technologies neuroscience is exploring its new horizons. Big data can now be processed by intelligent computers using ML-based artificial intelligence approaches, opening up new possibilities for neuroscience such as how millions of neurons and nodes cooperate to manage vast amounts of information and how the brain develops and governs actions.

From Neuroscience to ML

The advantages of thoroughly analyzing biological intelligence for constructing machine learning models are twofold. 

  • First, neuroscience is a rich source of inspiration for new sorts of algorithms and architectures that are independent of and complementary to the mathematical and logic-based methodologies and concepts that have dominated traditional approaches to Machine Learning. For example, if a novel aspect of biological computing is shown to be crucial to supporting a cognitive function, we would consider it a good candidate for inclusion in machine learning systems. 

  • Second, neuroscience can give validation for existing machine learning algorithms. If a known algorithm is later discovered to be implemented in the brain, it provides strong support for its viability as an important component of a larger general intelligence system.

Human intellect is distinguished by an extraordinary capacity to keep and alter data in an active storage, this is referred to as working memory, and it is assumed to be instantiated inside the prefrontal cortex and related regions. According to traditional cognitive theories, this functioning is dependent on interactions between a central controller ("executive") and distinct, domain-specific memory buffers (e.g. visuo-spatial sketchpad). These models have inspired machine learning research, which has resulted in systems that explicitly preserve information across time. Historically, such attempts began with the introduction of recurrent neural network topologies with attractor dynamics and rich sequential activity, work that was inspired directly by neuroscience. This study paved the way for later, more thorough models of human behavior.

In difficult object identification tests, machine learning systems now match and exceed professional humans. Machines can make synthetic natural visuals and human speech simulations that are nearly indistinguishable from their real-world counterparts, translate across several languages, and create "neural art" in the style of well-known painters.

Much effort has to be done to close the IQ gap between machines and humans. Concepts from neuroscience will become more important in narrowing this gap. The introduction of new technologies for brain imaging and genetic bioengineering in neuroscience has begun to give a precise characterization of the computations occurring in neural networks, suggesting a revolution in our knowledge of mammalian brain function. The importance of neuroscience in the following main areas, both as a roadmap for the ML research agenda and as a supply of computational tools, cannot be overstated.

From ML to Neuroscience

Psychologists and neuroscientists frequently have only hazy ideas about the mechanisms behind the issues they examine. Machine learning is aiding them by formalizing these notions in a quantitative language and providing insights into their requirement and adequacy (or lack thereof) for intelligent action.

RL is an excellent example of its potential. After ideas from animal psychology aided in the development of reinforcement learning research, essential principles from the latter were fed back into neuroscience. In another area, research aimed at improving the performance of CNNs has revealed fresh insights into the nature of neural representations in high-level visual domains.

Machine learning research is also generating new ideas on how the brain may implement an algorithmic counterpart to backpropagation, the crucial technique that permits weights to be optimized over numerous layers of a hierarchical network toward a certain purpose 

When taken together, machine learning models hold out the prospect of identifying processes through which the brain may implement algorithms. Furthermore, these studies demonstrate the possibility of synergistic interactions between machine learning and neuroscience: study targeted towards biological development.


When planning for future collaboration between the two domains, it is vital to remember that previous contributions of neuroscience to ML have seldom entailed a straightforward transfer of full-fledged solutions that could be directly re-implemented in machines. Rather, neuroscience has been beneficial in a more subtle way, prompting algorithmic-level questions about aspects of animal learning and intelligence of interest to machine learning and offering preliminary leads toward relevant processes.

In the future, we hope that increased collaboration between neuroscience and machine learning, as well as the identification of a common language between the two fields will enable a virtuous circle in which research is accelerated through shared theoretical insights and common empirical advances. 

We think that the pursuit of machine learning will eventually lead to a greater understanding of our own minds and mental processes. Distilling intelligence into an algorithmic construct and comparing it to the human brain may reveal insights into some of the mind's most profound and persistent mysteries, such as the source of creativity, dreams, and, possibly one day, consciousness.

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Cloud GPUs vs On Premises GPUs

Cloud GPUs are typically more powerful than on-premises GPU instances. The cost of renting a cloud GPU is generally lower than the cost of purchasing an on-premise GPU. 

Cloud platforms offer fast access to high performance compute and deep learning algorithms, which makes it simpler to start using machine learning models and get early insights into your data. 

Cloud GPUs are better for machine learning because they have lower latency, which is important because the time it takes a neural network to learn from data affects its accuracy. Furthermore, cloud GPUs allow users to take advantage of large-scale training datasets without having to build and maintain their own infrastructure.

On Premises GPUs are better for machine learning if you need high performance or require access to cutting-edge technologies not available in the public cloud. For example, on-premises hardware can be used for deep learning applications that require high memory bandwidth and low latency.

Cloud GPUs: Cloud GPUs are remote data centers where you can rent unused GPU resources. This allows you to run your models on a massive scale, without having to install and manage a local machine learning cluster.

Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

Cloud GPUs: Cloud GPUs offer significant performance benefits over on-premises GPUs. They are accessible from anywhere, and you don't need to own or manage the hardware. This makes them a great choice for data scientists who work with multiple data sets across different platforms.

Numerous Platforms Available for Use: The wide variety of available platforms (Windows, Linux) means that you can run your models using the most popular machine learning libraries and frameworks across different platforms without having to worry about compatibility issues between them.

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Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

Cloud services are essential for modern businesses, as they provide easy access to software, storage, and computing resources. Cloud services are delivered over the internet and are typically charged on a per-use basis. This makes them incredibly convenient for businesses, as they can pay for only the resources they actually use. Cloud computing allows businesses to scale their resources up or down, depending on their current business needs. This makes it suitable for startups, where demand is uncertain, or large enterprises with global operations. Cloud computing is also inherently scalable and allows businesses to quickly react to changing business needs. Cloud computing is a very competitive industry and providers offer attractive prices to attract customers. However, these prices have been impacted by the strong dollar. The dollar has strengthened by 15-20% against the Indian rupee in the last few years. As a result, the costs of services such as storage and bandwidth have increased for Indian companies. Vendors charge their Indian customers in Indian rupees, taking into account the exchange rate. This has resulted in a significant rise in the costs of these services for Indian companies.

Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

The table below lists some of these services and compares their cost against their US equivalents. 

According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.


The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

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Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.


In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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